What’s inside:

• Why classic GTM programs fail in AI-native environments

• What is an Attractive Value Experience (AVX)

• The 6 components of an AVX

• How Human-AI Cognitive Pairing reshapes GTM execution

• Real-world AVX journey examples, like AI inventory optimization

• A free worksheet to help you design your first AVX today

This one’s for tech founders and builders who want their AI product to actually resonate with users and buyers. Let's dive in!

The landscape of enterprise software and AI is shifting rapidly, demanding a fundamental re-evaluation of how businesses go to market. Traditional GTM playbooks, while still relevant in parts, often fall short in harnessing the transformative power of Artificial Intelligence. For entrepreneurs building the next generation of AI-first or AI-enhanced solutions, a first-principles approach is not just an advantage—it's a necessity.

This post outlines a step-by-step framework to rethink your GTM strategy, anchored by the concept of the Attractive Value Experience (AVX) and with Human-AI Cognitive Pairing, powered by an ecosystem of AI agents.

How AI Disrupts GTM Campaigns

In today’s AI-driven GTM world, why are traditional campaign architectures still the default? Programs, campaigns, and tactics were designed for a pre-AI era—too bulky, too disconnected to keep up. AI is tossed into content, targeting, analytics—but where exactly should humans step in? Who owns the decisions? This lack of clarity creates friction, not flow.

Are we delivering value that truly resonates with buyers, or just amplifying noise? When AI speeds up execution but humans struggle to guide it, who’s accountable for meaningful outcomes? And how do we avoid wasting effort on fragmented campaigns that confuse rather than compel?

The root issue is simple but profound: the unit of GTM execution is outdated for human-AI collaboration.

Campaigns and programs are too large and vague, channels too siloed, and personas too generic to flex with real-time buyer needs—or to harness AI-human collaboration effectively.

So how do we rethink GTM for a world where AI isn’t just a tool but a partner? Where value is not an abstract goal but a tangible, micro-experience that aligns with buyer journeys and human judgment? The answer lies in a new, sharper GTM building block—one designed for continuous learning, intelligent pairing, and crystal-clear value delivery.

The Attractive Value Experience: A Foundational AI Native GTM Unit

The Attractive Value Experience (AVX) is a proposal for the foundational unit of GTM in the AI era—one that aligns with how buyers actually experience value and how teams can intelligently deliver it with AI and Agents. At its core, effective GTM is about consistently delivering value that compels customers forward. We define this foundational unit as an Attractive Value Experience (AVX).

AVX is a meticulously designed, measurable, and iterative micro-journey that attracts a customer with a small, self-contained value prop, engages them intelligently, and then propels them toward a specific immediate goal.

It's a holistic, single-purpose flow where interdependent components are strategically bundled to deliver clear-cut value, optimized for the right level of Human-AI Cognitive Pairing powered by a growing ecosystem of AI agents, and continuously refined.

In short:

AVX is a Micro Experience that

→ Attracts Customer with Value

→ Engages with Intelligence

→ Propels to a Goal

Six Components of an Attractive Value Experience

Every single AVX has six components that are necessary to make it functionally complete and effective as a GTM unit. Working in an integrated fashion, these six components deliver an experience that is self contained. Moreover, you can optimize these components based on feedback until the experience (and the underlying value prop) is strong enough to both attract and propel customers successfully to their goals.

Here are the six components and their roles:

AVX Examples

Zoom Webinar

AVXs often take the form of:

• An AI-assisted interactive experience that delivers fast insight, outcomes or recommendations

• A micro-journey that solves a high-value problem or risk for a specific persona

• A proof-of-value tool or workflow that aligns to the buyer/user’s real-world decision-making or usage context

The unit of value in an AVX is intentionally small, clear, and specific to a target persona. It’s designed to be immediately useful, self-contained, and easily measurable — making it ideal for:

• Accelerating user adoption and trust

• Enabling champions with internal proof points

• Driving usage-based expansion and upsell

The Power of Human-AI Cognitive Pairing in GTM

The true differentiator in AI-native GTM lies in the seamless Human-AI Cognitive Pairing. This isn't about AI replacing humans, but rather AI augmenting human capabilities and vice-versa, creating a synergistic GTM machine that is both scalable and deeply impactful. There are multiple levels of Human AI pairing feasible. Here is a basic one:

AI for Scale and Precision: AI excels at processing vast amounts of data, identifying patterns, personalizing content at scale, automating routine tasks, and providing predictive insights. This allows GTM teams to operate with unprecedented efficiency and precision.

Humans for Empathy and Strategy: Humans bring empathy, creativity, strategic thinking, complex problem-solving, and the ability to build deep relationships. In the GTM process, they interpret AI insights, craft compelling narratives, navigate complex negotiations, and provide the nuanced touch that AI cannot replicate.

Why This Matters for AI-Native GTM

Traditional SaaS go-to-market models — especially those built for large B2B products — are breaking down in the AI-native world:

• AI-native products are faster, smaller, and more modular. They often deliver value in specific tasks or insights, not entire platforms.

• Buyers and users expect proof of value early. They need to experience tangible value before full adoption or enterprise buy-in.

• Human-AI pairing is central to usage. New forms of decision-making, productivity, and workflows are emerging — changing how GTM must prove value.

That’s why the AVX framework is key. It provides a repeatable way to design product-led, AI-native GTM that accelerates time-to-value, reduces friction in buying, and builds trust in intelligent systems.

Building GTM Pathways: Create and Orchestrate AVXs Across Customer Journey

Each AVX is built from three core parts to support customer journey:

1. Cognitive Pairing (Human + AI)
   A clear mapping of how the AI augments, assists, or accelerates the customer (buyer, user or team) in achieving a specific goal or task.

2. Context-Aware Unit of Value
   A modular outcome, action, or insight that aligns to the customer’s real-world process, decision, or metric of success.

3. Agentic Enablement to Attract & Propel
   Designed to be demoed, shared, or used directly by the users and buyers to enable internal alignment, proof of value, and next-step adoption.

Together, these three elements form a durable architecture for a value journey that is:

• Attractive (to the right persona)

• Valuable (for their success metric)

• Experiential (in the form of interaction, not just content)

A string of AVXs can be used to build custom GTM pathways.

Each attractive experience pulls a customer into a value cell, and upon delivery, triggers the next attractive experience. Ultimately, with right optimization of the GTM pathway itself (through AI) this approach helps customers discover the product offering, and reach their business outcome through its consumption.

In practice, this pairing manifests across GTM functions and applies to various personas, such as User and Buyer, across all stages of their Customer Journey.

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End-to-End Example: Retail – Inventory Optimization

By meticulously designing and orchestrating multiple AVX experiences, you can build a GTM engine that is both efficient and deeply customer-centric, capable of leveraging the right AI's strengths at every turn.

Use Case: AI-powered inventory management and demand forecasting for omnichannel retail.

User Persona: Store Manager or Inventory Analyst

Buyer Persona: Head of Supply Chain or VP of Merchandising

User Journey – Store Manager / Inventory Analyst

Buyer Journey – VP of Merchandising / Head of Supply Chain

Note:

• AVX Flexibility: AVXs are modular and persona-specific, yet linkable (e.g., usage data feeds business case).

• Proximate Goal-Centric: Each AVX advances the journey with a specific, measurable goal.

• Human-AI Pairing: AI and agents are embedded across both tracks—not just as backend automation but as a visible partner in delivery.

Five AVX Combination Customer Journey Patterns

No customer journey is linear. So, these AVXs can be combined in various patterns to craft and enable journeys to achieve customer goals.

Below is a summary of the five key ways AVXs can be used as building blocks in go-to-market (GTM) design—like Lego blocks or biological cells—followed by real-world-style examples for each pattern.

AVX as a Modular GTM Building Block

Pattern 1: Local Optimization [Lego brick]

What It Means: Each AVX can be optimized independently by refining its 6 elements based on performance.

Purpose: Nail individual moments of value

Example & Outcome: A customer onboarding checklist AVX for new admins. Use case: Focuses on helping an IT admin complete setup within 30 mins. Result: 30% faster time-to-value and 15% drop in setup-related support tickets.

Pattern 2: Sequenced Cascade [Relay Race]

What It Means: AVXs can be linked in a linear sequence along the journey to build momentum toward conversion.

Purpose: Drive engagement across time

Example & Outcome: A sequence from product trial → security validation → stakeholder approval. Use case: Orchestrates the buyer journey for an enterprise IT tool. Result: 2x faster deal cycles and smoother handoffs between sales and legal teams.

Pattern 3: Parallel Tracks [Threads]

What It Means: Different AVXs can run simultaneously for different segments, channels, or functions.

Purpose: Scale reach without confusion

Example & Outcome: Simultaneous AVXs for CFO (ROI modeling), VP Eng (integration demo), and Procurement (contract Q&A). Use case: Address different stakeholder needs concurrently in a complex sale. Result: 40% higher close rates due to role-specific value delivery.

Pattern 4: Stacked Personas [Big Moment]

What It Means: At one event or campaign, multiple AVXs can serve distinct personas (user, manager, exec).

Purpose: Drive dense engagement and conversion

Example & Outcome: Product pitch deck that combines AVXs for ROI, roadmap confidence, technical feasibility, and procurement clarity. Use case: Tailored pitch for CFO, CTO, and VP Ops in one enterprise meeting. Result: Stakeholder alignment in one session and accelerated decision.

Pattern 5: Channel Adaptation [Custom Fitting]

What It Means: Each AVX can be tailored to a specific GTM channel (e.g. email, live demo, outbound SDR).

Purpose: Maximize distribution efficiency

Example & Outcome: A self-assessment AVX on AI readiness delivered via email, LinkedIn, and partner webinars. Use case: One AVX deployed across three acquisition channels. Result: Increased lead capture volume by 3x without duplicating effort.

Note that AVXs are:

🔹 Modular: Can be deployed incrementally.

🔹 Composable: Can be arranged into sequences or stacked into bundles.

🔹 Contextual: Aligned to job-to-be-done (user) or business moment (buyer).

🔹 Adaptable: Works across touchpoints, lifecycle stages, and personas.

🔹 Trackable: Each AVX can be instrumented for engagement and conversion.

Therefore, AVX based AI native GTM approach can enable multiple GTM strategies: Product-Led Growth, Product Led Sales, Account Led Sales, Channel and Community Led Growth etc. How to ladder multiple AVXs into these strategies is outside the scope of this document, but an interesting area of work in progress.

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Comparing AVX to Other GTM Constructs

Since AVX is a new AI-native GTM framework, it helps to understand how it differs from other common GTM constructs in favor today. It is critical to note that all current GTM units will remain relevant in the near future. Already, they are being used by every GTM practitioner to add and test AI capabilities with hundreds of tactics today.

Here is how AVX differs from current GTM constructs:

The AVX framework also complements these constructs and co-exists with each of them. Here is how:

Advantages of AVX: An AI Native GTM Framework

The Attractive Value Experience framework, especially when integrated with Human-AI Cognitive Pairing, offers significant advantages over conventional GTM approaches for AI products.

1. Enhanced Precision and Personalization: AI thrives on data and personalization. The AVX's focus on defining clear "Context" and "Proximate Goals" for each experience, combined with AI's ability to analyze customer data, allows for hyper-targeted messaging and interactions. This is far more effective than traditional broad-stroke campaigns, ensuring AI's value is demonstrated precisely where it matters.

2. Deep Customer-Centricity: By explicitly defining the "Value Proposition" and "Proximate Goal" from the customer's perspective in each experience, AVX ensures that every GTM activity is focused on delivering tangible customer value. This builds trust and adoption for AI solutions, which often require a shift in customer behavior.

3. Optimized Human-AI Synergy: The framework inherently promotes the best use of both human and AI capabilities. AI handles the heavy lifting of data processing and automation, freeing humans to focus on complex strategy, relationship building, and creative problem-solving – areas where AI currently falls short. This maximizes the impact of both, leading to more effective GTM.

4. Superior Efficiency and Scalability: By automating data analysis, content generation, and routine engagements (AI-Driven methods), and by augmenting human efforts (Human-AI Pairing), AVX enables GTM teams to achieve more with less. This scalability is crucial for AI companies aiming for rapid growth without linearly increasing human resources.

5. Real-time Optimization and Agility: The "Measure & Feedback" component is a core tenet of AVX. With AI, this feedback loop becomes real-time and highly granular. AI can rapidly process performance data, identify trends, and suggest optimizations for each experience, allowing for continuous, data-driven improvement that traditional, slower feedback cycles cannot match.

The Future of GTM is Built on Orchestrated AVXs with AI Agents

The era of AI demands a fundamental shift in GTM strategy. By embracing a first-principles approach centered on the Attractive Value Experience and strategically integrating Human-AI Cognitive Pairing powered by an evolving network of AI agents into every stage of the user and buyer journeys, entrepreneurs can unlock unprecedented growth.

This framework empowers you to design purposeful, measurable interactions that consistently propel your customers towards their goals, ensuring your AI innovations translate into undeniable market success. While demanding in its initial setup and requiring robust data infrastructure, the precision, scalability, and real-time optimization offered by the AVX framework make it the superior choice for navigating the complexities and opportunities of the AI-driven market.

Getting Started with AVX Approach

• Initiate an AVX Pilot Program: Select a critical customer touchpoint and redesign it as a single Attractive Value Experience (AVX), meticulously defining its context, value proposition, engagement methods, proximate goal, measurement strategy, and investment level.

• Form Cross-Functional AI-GTM Teams: Establish dedicated teams comprising GTM professionals and AI specialists to collaboratively identify opportunities for Human-AI Cognitive Pairing within your existing GTM programs, focusing on areas where AI can augment human precision and scale.

• Conduct a "GTM Program to AVX" Audit: Review your current GTM programs and campaigns to identify how they can be broken down into modular AVXs, and then strategize how these AVXs can be sequenced, run in parallel, adapted for different channels, or stacked for diverse personas.

• Assess AVX Technical Stack for Enablement: Pinpoint crucial gaps across your data, AI, delivery, measurement, and integration, giving you a clear roadmap for the technical effort needed. Don't let technical uncertainties slow your progress; leverage this assessment to build the precise capabilities for market success.

• Develop an Internal Training Curriculum: Create educational materials and workshops to familiarize your entire GTM organization with the principles of the Attractive Value Experience (AVX) and the strategic implementation of Human-AI Cognitive Pairing, fostering a culture of AI-native GTM design.

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WORKSHEETS

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REFERENCES

• Positioning Your AI & Agentic Apps to Win

• Layering AI into your B2B Product and GTM

• The Only Two Ways to Attract Customers - by Anurag Wadehra

The AI landscape is evolving rapidly, and to build a successful AI-powered product, it is crucial to position it effectively. In this blog post, I explain a structured approach that will help founders, product leaders, and GTM teams anchor their AI applications in real business value and differentiate them in a crowded market.

This post is an expanded version of a talk I gave recently on this topic. (video)

Setting the Stage: Why Positioning Matters

AI Is Not Enough

Many tech founders struggle to gain traction with their AI product for a simple reason they fail to appreciate.

AI is an ingredient technology whose value is in how it is applied, not how it is built.

The product teams and AI builders are in thrall of the latest models, benchmarks and architectures. They are busy educating the smart buyers on how to build a gen AI stack with RAG, how to engineer data pipelines, how to fine tune models, and how to deploy AI apps and wrappers.

Yet none of this matters much to positioning your AI product to win. Since this describes the how of AI, not what is the business value and why should buyers care for your specific AI-enabled products.

Your value proposition must be anchored in high-impact use cases and your AI capabilities should be differentiated by how they unlock this value within a business.

This guide provides a structured framework for positioning AI and agentic applications. It follows five critical steps. Whether you're developing an AI-first product or enhancing existing SaaS software with AI, these steps will help you craft a compelling value proposition and establish a winning market position.

How to Position AI & Agentic Apps for Market Success

To create a winning AI positioning, follow these structured steps:

1. Identify Your Industry Fit: Understand the AI adoption pattern of your industry: structured (e.g., healthcare) or agile (e.g., retail)?

2. Anchor on a Core AI Use Case: Choose a high-impact use case where AI delivers tangible benefits.

3. Determine the Role of AI Agents: Understand how AI agents fit alongside humans—Automator, Augmentor, or Autonomous.

4. Define Your AI Differentiation: Use the three-factor differentiation framework that includes value.

5. Position for Leadership: Craft a compelling positioning statement centred on a clear business value.

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Understanding Structured vs. Agile AI Adoption

When it comes to AI adoption, industries can be classified as structured (e.g., healthcare, finance) or agile (e.g., tech, retail, media).

Structured industries

are regulated industries with strong legal, compliance and risk guardrails that are enforced by business users and teams and supported by established procedures and processes. They are likely to adopt AI gradually, starting with low level task automation and moving towards augmentation and autonomy, prioritizing compliance and risk management at every step, along with proven ROI. They also care a lot more about the impact of AI on human roles and jobs.

Agile industries

are more open to changing their people and processes due to competitive pressures. They are likely to integrate AI dynamically, focusing on immediate impact and differentiation through rapid experimentation and iteration. They may be more open to training employees to work with AI or reassigning them elsewhere within or outside the company.

In some cases this distinction may vary between companies within a sector. Or even within parts of a company. Nonetheless, understanding this distinction is a solid starting point to effective AI positioning. Why? Because AI adoption frameworks differ based on this distinction.

Structured Industries: The User-Team-Buyer Framework

Most SaaS product companies today follow this framework. Inside each company where their product is deployed, their users focus on tasks, teams collaborate on tasks through workflows that are internal or external to the app, and buyers focus on generating the overall business value by optimizing these tasks and flows through insights that are usually lagging.

This pattern of different personas (user/team/buyer) with different levels of accountability (productivity, collaboration, decisions) is common across thousands of SaaS applications. This framework has been stable for nearly two decades. The visual below shows how neatly each group does their part, and how the feedback flows over time to improve their work.

The true intelligence of humans - the feedback loop on their tasks, workflows and decisions - are mostly external to the app.

Hence these apps are slow and limited in how they improve - and ultimately how they deliver value.

Example:

If at a call center, the customer service agents are mishandling the support tickets, and CS ops teams have poor escalation processes, while the CS leaders don’t have correct dashboards to decide on fate of key service metrics, most of the remedies to inject “intelligence” in this situation are outside the app. Such as getting CS leaders the right insights to support decisions, improving the escalation protocols, and re-training human agents.

AI disrupts this twenty year SaaS pattern. By adding prescriptive, predictive, generative capabilities at each layer of the work. Let’s see how.

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How to Layer AI into SaaS products:

Healthcare Example: AI-Assisted Patient Workflow Optimization

SaaS incumbents have a huge advantage here. They have an installed base of users and team workflows that already conform to current regulations and org hierarchies. People's hierarchies and processes change slower than AI tech. SaaS vendors may be able to layer in AI in their products, adding incremental value one layer at a time.

Consider the situation where a hospital is considering AI to optimize patient workflow.

User Layer:

AI improves task execution for hospital admin and doctors. This allows each of them to move up their personal productivity ladder.

In the initial stages, the software provider can help their Users recognize and routinize simple tasks using AI as Automator. The User’s role is that of a Monitor over such tasks, which may be executed through multiple task agents.

Over time, the software can help Users reflect and retrieve more intelligence to improve their personal productivity using AI as Assistant. Here the User’s role becomes that of an Advisor who is assisted by several research agents.

(examples in the picture)

Here are the three sharp questions to enhance user productivity with AI:

• Who are the different user personas? And where is each of them in their productivity ladder?

• What routine tasks can AI automate for each user?

• What reflective tasks can AI assist for each user?

Team Layer:

AI coordinates workflows across care teams. This allows the teams to move up the collaboration ladder.

In the initial stages, the software provider can help the teams control and coordinate a few workflows with AI as Work Executor. The Team’s role is that of a Coordinator over such automated workflows, which may be executed through multiple workflow agents.

Over time, the software can help teams create and collaborate across workflows to plan more intelligence to improve collaboration outcomes using AI as Work Planner. Here the Team’s role becomes that of Facilitator who is assisted by several workplan agents.

(examples in the picture)

Here are the three sharp questions to enhance team collaboration with AI:

• Who are the teams that use your software? And where is each of them in their collaboration ladder?

• What routine workflows can AI execute for each team?

• What collaborative workflows can AI plan for each team?

Buyer Layer:

AI optimizes resource allocation for hospital administrators. This allows the buyers to move up the decision ladder.

In the initial stages, the software provider can help the decision maker (buyer) predict and plan with AI as a decision support tool, such as a forecaster. The Buyer’s role is that of a Evaluator over such forecasts which may be executed through multiple predictive or forecasting agents.

Over time, the software can help the buyer - or the decision maker - make some decisions autonomously through AI that engages and operates systems to execute the decisions. Here the Buyer’s role is to guide and guard the autonomous agents in maintaining the decision quality and outcomes.

(examples in the picture)

Here are the three sharp questions to enhance buyer decision making with AI:

• Who are the goals of the decision maker? What level of autonomy do they need on the decision?

• What decisions can AI predict or forecast for the buyer?

• What decisions can AI make autonomously for the buyer?

In summary, you can layer in AI into the current SaaS products for known users, teams and buyers in structured industries through progressive improvement of AI-led feedback at each of the three hierarchical layers. You can build direct AI and Agentic apps using this layered approach.

Note that the AI value ladder is bottom up, slowly driving to better outcomes of goals and KPIs - bringing along users, teams and buyers in roles they are already familiar with. This may be necessary for some industries and companies.

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Agile Industries: Human AI Cognitive Pairing

AI is more disruptive than what we have been outlining so far. When we are not constrained by the hierarchical structures of current jobs and roles, we can reimagine how human workers may use AI and Agentic apps to achieve business goals.

This is happening in agile industries. At heart of this re-imagination is the idea of how humans and AI pair together. Of course, AI can and will displace humans from many tasks in a company. However, those remaining employees will work with AI at various levels of cognitive collaboration.

There are many ways to categorize this. Here is one framework that maps humans in new roles (personas) alongside AI Agents personas.

Three ways human workers can pair with AI today, in a work environment.

Human as Supervisor; AI as an Automated Assistant

• Focuses on routine task automation.

• Eliminates repetitive, rule-based tasks.

• Increases efficiency and reduces manual errors.

• Example: AI-powered data entry and appointment scheduling in healthcare.

Human as Collaborator; AI as an Augmented Co-worker

• Supports human decision-making with data-driven insights.

• Enhances productivity without replacing human expertise.

• Example: AI-assisted diagnostics that help doctors analyze patient records.

Human as Guardian; AI as Autonomous Specialist

• AI Acts independently in decision-making

• Executes complex, real time decisions

• Guided and governed by experienced humans with right guardrails

• Example: AI-driven forecasting tools that predict staffing needs in hospitals; AI-driven fraud detection in financial services.

Each of the three human personas depend on the tasks they are asked to perform based on their current task skills. The corresponding AI Agent personas depend upon the full stack capabilities, agentic and otherwise, imbued into these agents to support the human tasks. A human worker may shift from one persona to another as they master new skills to move up the complexity ladder or move down to take on new tasks - or pass a task to another human-AI pair.

In concert with this shift, the agents may learn from feedback and take on more tasks - while improving their core capability. And in turn, they may enroll or involve other agents, tools and resources as agentic platforms become ubiquitous.

This co-evolution of human-AI pairing is the driving force of the creative destruction - both creating new cognitive capacities for some humans at work while also relieving others from tasks no longer economically viable.

[As an aside, technically, there are more evolved & complex ways for Human and AI cognitive partnership that are possible today. I have limited this post to the three modes that are most common and practical today. Over time, The framework can be extended to incorporate more advanced pairing of Human as Mentor: AI as Learner and Human as Explorer: AI as Challenger. See my research note for more.]

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In Agile, human AI partnership solves directly for a goal

This framework of cognitive coevolution explains how a small team of Human-AI pairs may be able to take on significant goals at work.

All the tasks are reconstituted among humans and agents to serve a goal, using this framework.

The UX and workflows are also redefined among humans and AI to serve the goal. And as KPIs improve through human-AI pairing, and as more value is realized, tasks and workflows keep getting reconstituted - all to serve the goal.

Example Retail: Improve margins while maintaining customer satisfaction

In this example, we can see how the overall goal is decomposed into smaller goals, which in turn, can be achieved through agile collaboration of human-AI agents, each team focusing on a narrower goal.

AI’s Value Ladder: Structured vs. Agile Environments

AI enhanced value ladder is incremental and bottom up, as it works through current hierarchies of roles, tasks and workflows.

Structured AI Value Ladder

(Healthcare, Finance, Manufacturing)

• Bottom-Up Adoption: AI moves from automating tasks → enhancing workflows → enabling strategic decisions.

• Example: AI in patient scheduling optimizes time slots → enhances care coordination → improves overall hospital efficiency.

This AI first value ladder is top-down and transformative with focus on goals and KPIs.

Agile AI Value Ladder

(Retail, Media, Consumer Tech)

• Goal-Driven Adoption: AI is directly embedded in key decision-making.

• Example: AI improves margin in retail by adjusting prices, routing service calls, coaching store agents.

Business Impact Framework

Here is a simple value framework: efficiency, effectiveness and scalability. The aim is to ensure that each AI & Agentic app can minimally show impact on key business metrics in one or more of these three areas. This framework is common across both approaches and can be used as a first pass.

The critical focus should be in attaching AI agents and apps directly to a key business metric.

Such a business metric can be tied to a task or use case - and can be anchored in a specific human-AI pairing.

Defining Your AI Differentiation

Now we come to the question of AI based differentiation. We went through the substantial pre-work on business context to get here because the differentiation of AI & Agentic applications is not only about technology—it must be framed in terms of business outcomes.

Three Key Differentiation Factors

This framework lays out a quick way to ensure your AI product is anchored in business value.

The three layers are:

AI Stack:

What makes your AI based capabilities unique? (Data, Model, UX, or Agentic Layer).

Rather than focus on a single part of the stack (data integration, or proprietary models or better UX), figure out the right combination of AI stack elements that makes your AI app or agents better than the alternative approaches. Often, the elements of your AI stack may not be highly distinct, but how you combine them to solve the key use cases may well be your differentiation.

Time-to-Value:

How quickly do customers see results? (Deployment speed, onboarding ease).

This is often overlooked in the hot world of AI demos and POCs. Customers are now skeptical of “cool” AI, so show a clear path to value with firm time commitments (deployment methodology, optimization and error corrections, governance etc.). All the factors that you will have to master to bring your AI app/agents to life in the hands of users with right guard rails.

Proof of Impact:

What are the measurable business outcomes? (Efficiency, revenue growth, customer success).

After you go live, you will need to show how your App is delivering measuring outcomes. Rather than wait for this, showcase these metrics before the sale, instrument them during the implementation, and use them operationally to prove the value of your AI and agentic apps. Ultimately, showcasing these metrics as part of the value framework above: efficiency gains (quantifiable cost savings), effectiveness (revenue improvements) or scalability of operations over time. .

Example: AI-Powered Patient Scheduling

• AI Stack: AI app integrates with hospital data sources and embeds in current apps, learning from admin users.

• Time-to-Value: Hospitals integrate it within 3 weeks.

• Proof of Impact: 30% reduction in appointment no-shows.

In summary, a winning AI differentiation strategy must: showcase AI tech superiority (e.g., proprietary algorithms, better data models, faster processing); highlight rapid value realization (e.g., customers achieving measurable improvements within weeks of adoption); and provide tangible business benefits (e.g., quantifiable cost savings, revenue improvements, operational efficiency).

Creating a Winning AI Positioning Statement

Now, we can summarize all the above into positioning statements.

Guidelines for High-Impact Positioning

✅ Start with the highest business goal relevant to your industry (Autonomous AI).

✅ If full autonomy isn’t feasible, position your product as an Augmentor.

✅ If augmentation isn’t applicable, position your product as an Automator.

✅ Align AI capabilities with specific personas and their KPIs.

Positioning Template for AI App/Agents

Here is a simple way to summarize all the above.

Example: Healthcare AI Positioning Statement

“Our AI-powered Patient Flow Optimization Agent helps hospital executives maximize revenue and bed utilization by autonomously optimizing patient intake and discharge sequencing (Autonomous Layer). It augments hospital coordinators by predicting real-time patient demand (Augmentation Layer) and automates scheduling workflows (Automation Layer). This results in a 20% increase in capacity utilization and a 15% reduction in operational costs.”

Example: Retail AI Positioning Statement

“Our AI-powered Retail Demand Forecasting Agent helps retail executives maximize profitability by autonomously optimizing inventory levels and real-time pricing (Autonomous Layer). It augments inventory managers by providing predictive demand analytics (Augmentation Layer) and automates stock replenishment (Automation Layer), leading to a 12% revenue increase and 15% reduction in overstock waste.”

Key Takeaways

Positioning AI effectively requires a structured approach:

1. Identify Industry Fit – Understand how AI adoption varies across structured and agile industries.

2. Anchor on Core Use Cases – Ensure AI solves a real, high-value problem - incrementally or transformatively - based on your industry.

3. Define Human-AI Pairing – Clarify how AI serves as an Automator, Augmentor, or Autonomous agent to partner with humans.

4. Differentiate the AI Offering – Highlight unique AI stack, time-to-value, and proof of impact.

5. Establish Market Leadership – Craft compelling messaging and reinforce AI’s business impact.

By integrating these key insights, businesses can position AI solutions in a way that drives adoption, maximizes impact, and builds long-term market leadership.

References

Research Summary: AI-Human Collaboration in the Workplace

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Unlocking the Future of SaaS Pricing

“How Well Are You Positioned for AI-Led Growth?”

This self guided video course and accompanying worksheet is designed specifically for tech founders to help you assess the strength of your current positioning in the face of AI technologies. It identifies areas where you might need to reposition to stay competitive.

Before we explore what this tool is, how it works, and why taking this assessment could be the key to your company’s future success, let talk why positioning matters.

Why Positioning in AI Matters More Than Ever

AI is transforming industries across the board, but merely adopting AI doesn’t automatically set you apart from competitors. In fact, AI will soon become a baseline capability for many businesses.

The companies that thrive will be those that can position themselves effectively by showcasing how their AI solutions deliver unique, tangible value.

For example, in industries like SaaS, AI-driven features such as predictive analytics or workflow automation are becoming common. To differentiate, your positioning needs to focus on what specific problems your AI solves and how it creates value in a way that competitors can’t replicate.

Many tech founders and companies are already developing products using AI or delivering AI-based services to their clients or at least experimenting with AI platforms, APIs and models.

This is where effective positioning comes into play—ensuring that customers understand not just what your product is, but why it matters and how it delivers value in ways that no other solution does.

The Story Map Framework for Positioning

Based on my operating experience, here is the Story Map Framework that I have built and applied multiple times at tech companies. This framework is applicable to both technology product and tech-enabled service companies. Increasingly AI companies are a blend of both.

The framework creates your company positioning by having you decide answers to three key factors and then helps you synthesize them into a value story that positions you distinctly in the market.

The three factors are:

• Hungry Customer: What problem are you solving and for whom?

• Magical Product: How does your product offering promise to solve this problem? And how is your way of doing unique?

• New Game in Category: What makes your approach and your product better than other competitive alternatives?

You could answer these questions in normal times. However, today these questions have an added urgency:

Your fresh answers or commitments should be used to create:

• A Unique Value Story (with AI):

  • What’s the customer story that delivers them differentiated value

• Positioning Statement (with AI):

  • How do you NAME the Promise of your company (product)

  • How do you CLAIM a Unique Way of delivering in it

  • How do you FRAME the Category to win against competitive alternatives

Using the Story Map Framework, tech founders can construct a clear, compelling narrative that defines not only what their product does but why it’s different—and better—than anything else on the market. This is crucial when you’re positioning your company in a space like AI, where the technology is fast moving, and the value it brings to customers needs to be reassessed and communicated continually.

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Positioning Self-Assessment Tool

The Positioning Self-Assessment Tool—“How Well Are You Positioned for AI-Led Growth?”—is a structured set of questions designed to help tech founders evaluate their current positioning in the rapidly evolving AI landscape.

Positioning Self Assessment Tool

The tool helps you evaluate your current status of the three factors and summarize your current story and positioning.

Customer Factor: How clear are you about your customer personas, and their burning problems? How relevant is AI to your customers today in addressing these problems?

Product Factor: How your product leverage AI in a way that sets it apart from the competition?

Category Factor: How is your current category getting disrupted by AI? And do you have a point of view on it?

Value Story: How effectively can you tell a unique value that your AI-enabled product offers?

Positioning: How sharp are you in naming, claiming and framing your positioning?

Baseline Positioning Score: From all these answers, you are able to generate a score. This score gives you a quick and clear snapshot of your company’s positioning strength relative to competitors, and whether you need to make adjustments to stay ahead.

How the Self-Assessment Tool Helps Tech Founders

The Positioning Self-Assessment Tool doesn’t just give you a score—it provides an evaluation that offers actionable insights into how you can improve your positioning.

Here’s how it helps:

1. Understand Your Current Position: The baseline score helps you quickly understand how well your company is positioned in the market today. You’ll see where you stand compared to competitors and get a clear sense of your positioning’s strengths and weaknesses.

2. Identify Gaps in Your Positioning: The scoring system highlights areas where your positioning may be weak or where competitors might have an edge. For example, if your Value Story isn’t clear, or if your Product Factor is vague, you’ll know exactly where to focus your efforts.

3. Plan for Repositioning: If the assessment shows that your positioning is no longer effective, it helps you begin a path to repositioning, by outlining key steps.

4. Maximize Your Use of AI: The assessment helps you understand how AI can be a competitive advantage, not just table stakes in your roadmap. By focusing on how AI drives value and differentiates your product, you can create a positioning that resonates with customers and sets you apart.

Positioning Self Assessment Tool

Why You Should Take This Assessment

As AI continues to reshape industries, staying competitive means constantly evaluating and refining your positioning. This self-assessment tool is specifically designed for tech founders who need to adapt to the evolving AI landscape. Here’s why it’s essential:

• Stay Ahead of Competitors: As more companies adopt AI, the marketplace will become more crowded. The companies that succeed will be those that position themselves strategically and communicate their unique value effectively.

• Avoid Positioning Pitfalls: Many companies make the mistake of thinking that simply using AI is enough to differentiate them. This assessment helps you understand that how you use AI is what sets you apart, not just the fact that you’re using it.

• Tailored for Tech Founders: This tool is specifically designed for founders in the tech space. It focuses on the unique challenges of AI adoption and provides insights that are directly relevant to your business.

• Actionable Insights for Improvement: The tool doesn’t just identify gaps—it offers specific, actionable recommendations on how to improve your positioning and strengthen your competitive edge.

Positioning Self Assessment Tool

Bonus Offer

If you are a tech founder, I am happy to offer 1:1 confidential feedback session to your self assessment. Just complete and submit the worksheet to me at aw@anuragwadehra.com

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Together, we explored how AI is driving a fundamental shift from traditional user or usage-based pricing to more sophisticated, outcome-driven models. Below is a detailed summary of the key points I made during the webinar, with Keerthi’s expert commentary adding valuable context.

The Current State of SaaS Pricing: User and Usage-Based Models

Traditional SaaS Pricing

SaaS pricing today is predominantly rooted in user or usage metrics. While this approach has served the industry well, it often fails to align with the ultimate value that customers seek. For instance, platforms like AWS charge based on API calls or storage consumption, while Salesforce prices according to the number of seats a customer utilizes.

The SaaS Value Gap

A significant challenge in the current model is what I call the “SaaS Value Gap.” This gap refers to the disconnect between what SaaS applications enable (user-level productivity and task efficiency) and what buyers actually want (tangible business outcomes like cost reductions or improved customer satisfaction).

This gap can lead to inefficiencies and dissatisfaction, as the pricing models do not always reflect the true value delivered to the customer.

AI’s Role in Bridging the Value Gap: Tying Intelligence into SaaS Pricing

AI as an Optimizer, short term

AI can be a powerful tool in optimizing existing SaaS pricing models. It allows for more personalized pricing strategies, enhances revenue management, and improves overall operational efficiency.

Personalized Pricing: AI enables dynamic adjustments to pricing based on user behavior, geographic location, or specific usage patterns. For example, Adobe’s Creative Cloud adjusts pricing based on the tools each user frequently utilizes.

Revenue Optimization: AI facilitates automatic adjustments in pricing and resource allocation, which ensures optimal revenue capture. A good example is Salesforce’s ability to automatically allocate seats based on actual usage patterns.

Efficiency Gains: AI allows for real-time monitoring of competitive pricing, enabling companies to stay competitive without the need for manual intervention.

AI as a Disruptor: Moving Toward Goal-Oriented Applications

Concept: The future of SaaS lies in AI-first applications that start with a business goal—such as reducing customer service costs—and then work backwards to determine the necessary workflows, user actions, and automation. This represents a fundamental shift from traditional user-centric models to outcome-centric models.

Example: In customer service, rather than focusing on the number of users or features, AI-first applications would prioritize outcomes like improving first-call resolution rates or reducing average handling time.

Emergence of Goal-Oriented AI Pricing Models

AI-First Value Ladder

Historically, SaaS value has been created from the bottom up—beginning with user productivity, moving to team collaboration, and eventually driving business outcomes.

AI-first applications invert this model. They start with the business outcome and then work backward to determine the minimal necessary workflow, user engagement, and automation required to achieve that goal.

This inversion represents a major change in how SaaS value is both delivered and monetized. Rather than charging for the number of users or features, SaaS companies will increasingly charge based on the outcomes their software helps achieve.

New Pricing Levers Introduced by AI

These are based directly on the new AI capabilities now possible with different AI algorithms, models and methods.

Predicting Events:

AI’s ability to forecast events (e.g., equipment failure) can be a basis for pricing.

Prescribing Actions:

AI that offers actionable recommendations (e.g., financial advice or prescription plan) can be priced based on the value of those actions.

Generating Insights:

AI-driven insights, such as customer behavior analytics, or summaries generated, can become new pricing metrics.

Controlling Processes:

AI-driven automation that manages processes (e.g., robotic process automation) could be priced based on efficiency gains.

Intelligent Interaction:

AI’s capability to engage with users through chatbots or virtual assistants can lead to pricing models based on interaction quality or resolution success.

Challenges and Barriers to AI-Driven Pricing Models

Cost Factors and Input-Based Pricing

Many companies remain anchored to input-based pricing models, such as paying for the number of AI calls made, which doesn’t necessarily reflect the value delivered.

Overcoming the traditional cost-plus mindset is essential to transitioning to outcome-based pricing models that better align with customer value.

Predictability and Revenue Models

Transitioning to outcome-based pricing can introduce variability in revenue predictability, which is a concern for SaaS providers and customers who prefer stable, predictable costs.

Companies can implement pricing models with predefined bands or ranges to provide a level of predictability while still aligning with outcomes.

Customer and Organizational Readiness

Customers need to evolve their IT infrastructure and organizational processes to fully leverage AI-driven solutions. Pilots and gradual implementation are often necessary before full-scale adoption can occur.

Sales teams need to be equipped to sell these more complex, outcome-based solutions, and companies must educate customers on the value of AI-driven outcomes.

Measuring and Defining Outcomes

For outcome-based pricing to be effective, outcomes must be clearly defined, measurable, and agreed upon by both the vendor and the buyer. This requires precise definitions and robust tracking systems.

Strategic Recommendations for SaaS Companies Embracing AI-Driven Pricing

Tailored Solutions

SaaS companies must move away from one-size-fits-all pricing models. Instead, they need to develop tailored solutions that align with the specific business goals and strategies of each customer.

Enhanced Strategic Planning

Effective planning is crucial for identifying the right use cases, defining pricing metrics, and ensuring the successful implementation of AI-driven solutions.

Focus on Outcome-Based Models

While challenging, transitioning to outcome-based pricing models will be essential for staying competitive in the AI-driven SaaS landscape. Companies that can effectively scale and deliver measurable outcomes will have a significant advantage.

Conclusion: The Future of SaaS Pricing

The future of SaaS pricing is being reshaped by AI, leading to the rise of goal-oriented applications and outcome-based pricing models. While there are significant challenges to overcome, particularly around cost structures, predictability, and customer readiness, the potential benefits are enormous. Companies that can navigate this transition effectively will be well-positioned to capture new value and lead in the evolving SaaS market.

“AI is not just an optimization tool but a catalyst for transforming the entire SaaS business model. The shift from user-centric to outcome-centric pricing represents a paradigm change, one that will redefine how SaaS companies deliver value to their customers in the years to come.” Keerthi Sivakumar

Together, Keerthi and I provided a roadmap for how AI is poised to disrupt and redefine the SaaS pricing landscape. Our insights emphasize the importance of strategic planning, tailored solutions, and a focus on measurable outcomes as companies transition into this new era of AI-driven value creation.

In the previous post, I covered how to set B2B SaaS and Marketplace pricing and what are the key user and buyer focused pricing value metrics used by SaaS companies today. Please review that post and set your the pricing strategy before your proceed to bundling your products.

In this post I describe how to take your pricing value metrics and bundle them with other into different compelling packages for each customer segment.

In the SaaS industry, bundling your product into offerings is crucial for delivering comprehensive value to your customers. A product offering goes beyond the core product itself, including pricing, terms and conditions, service capabilities, and additional network or marketplace integrations. Understanding this broader perspective helps in creating packages that enhance user experience and drive sales.

The Difference Between a Product and an Offering

A product refers to the core software or service that you are selling. An offering, however, is a broader concept that includes not just the product, but also the pricing, terms and conditions, service capabilities, and any additional features or integrations. Multiple offerings can be created from the same product by bundling these different aspects in various ways to cater to different customer needs and segments. Bundling is the process of combining these elements to form distinct packages that provide a cohesive and attractive solution to customers.

The Importance of Bundling an Offering

Bundling effectively combines various aspects of your product to provide a cohesive and attractive package to customers. It’s not just about selling a product; it’s about delivering a complete solution that addresses the customer’s needs in a way they can easily understand and appreciate. This approach differentiates your offering in a crowded market and helps build long-term customer relationships.

Why Free Offerings are Necessary

Free offerings are an essential strategy in SaaS marketing. They allow potential customers to experience the product without initial financial commitment, lowering the barrier to entry. The key to converting these free users into paying customers is the “time to aha”—the moment when the user realizes the value of your product. This moment of value realization is critical in building trust and demonstrating the product’s worth, increasing the likelihood of conversion to paid plans.

Three Levels of Free Offerings

1. Freemium:

• Description: The basic product is available for free indefinitely, with premium features available for a fee.

• Examples: Trello allows free basic project management, while advanced features require an upgrade.

• Time to Aha: In the freemium model, the “aha moment” might occur when a user completes their first project using Trello’s basic features, realizing the efficiency and organization it brings.

• Purpose: To let users form a habit and appreciate the product’s value over time.

2. Low-Tier and Premium-Tier Free Trials:

• Description: Users can access the basic or premium product for a limited time.

Examples:

• Low-Tier: Slack offers a 30-day free trial of its basic product.

• Premium-Tier: Adobe Creative Cloud provides full access to its premium features for a limited time.

• Time to Aha: For Slack, the “aha moment” might occur when a team successfully collaborates on a project within the 30-day trial, experiencing the ease of communication and increased productivity. For Adobe Creative Cloud, the moment could be when a user completes their first complex design project, realizing the powerful tools at their disposal.

• Purpose: To allow users to experience either the full range of features or just the basic ones, encouraging them to upgrade after the trial period.

3. Reverse Trial:

• Description: Users start with full access to premium features for a limited time and then downgrade to a basic version if they don’t upgrade.

• Examples: Dropbox offers the professional edition initially, then downgrades users to a basic or lower-priced edition after the trial.

• Time to Aha: The “aha moment” for Dropbox might occur when a user utilizes the advanced sharing and collaboration features during the initial period, realizing the efficiency and ease of use compared to the basic version.

• Purpose: To showcase the full capabilities first, making it easier for users to appreciate the premium features and consider upgrading.

Structuring Paid Packages

Creating effective paid packages involves offering 3-4 bundles, which helps in providing clear options to customers and optimizing revenue. Most SaaS companies structure these bundles based on different criteria.

Common Bundling Strategies

1. Value Metrics-Based Bundling:

• Description: Packages are created based on the value delivered to the customer. As the bundle goes from good to better to best, significant discounting on a per unit of the value metric is applied.

• Examples: Dropbox provides varying levels of storage and features, with significant discounts for larger storage packages.

• Purpose: To clearly differentiate value and offer discounts on higher-value packages.

• Example:

• Good Package: $10/month for 100GB of storage.

• Better Package: $15/month for 500GB of storage (significantly cheaper per GB).

• Best Package: $20/month for 1TB of storage (even more discount per GB).

2. User Persona-Based Bundling:

• Description: Packages tailored to different user types such as individual users, teams, or enterprises.

• Examples: Slack offers packages for small teams as well as larger enterprises.

• Purpose: To cater to the specific needs of different customer segments.

3. Product Feature-Based Bundling:

• Description: Packages are differentiated by the features they include.

• Examples: Trello’s basic, premium, and enterprise packages.

• Purpose: To allow users to choose based on the features they need.

4. Service Level-Based Bundling:

• Description: Packages differentiated by the level of service provided.

• Examples: Silver, Gold, and Platinum support levels.

• Purpose: To offer varying degrees of support and service, catering to different customer requirements.

Setting Up the Packages

1. Define Your Packaging Criteria:

• Determine whether the bundling will be based on value metrics, user personas, product capabilities, or service levels. This step is crucial as it lays the foundation for how your offerings will be perceived and utilized by different customer segments. Each criterion should align with how customers derive value from your product, ensuring the bundles are relevant and attractive.

2. Develop a Free Offering:

• Ensure it allows users to experience the value of your product initially. A well-designed free offering lowers the entry barrier and helps in building trust, giving users a taste of what they can expect from your paid packages. The focus should be on delivering an “aha moment” where users realize the significant value of the product, increasing the chances of conversion.

3. Construct the Paid Bundles:

• Combine product components, service levels, and pricing into distinct packages. This involves strategically selecting features and services that provide incremental value and justify the price differences between packages. By clearly defining what each bundle offers, you can cater to different customer needs and preferences.

4. Establish Clear Pricing Anchors:

• Signal the price and corresponding value for each package clearly. Pricing anchors help in setting customer expectations and making the value proposition of higher-tier packages more appealing through perceived discounts and added benefits. Clear communication of these anchors ensures customers understand the benefits of upgrading to higher tiers.

Conclusion

Combining free offerings with structured paid bundles creates a coherent and effective pricing strategy. This approach not only attracts potential customers but also provides them with clear pathways to upgrade as they experience the product’s value. By strategically packaging and pricing your offerings, you can maximize customer engagement and revenue while delivering a seamless and satisfying user experience.

REFRENCE

Setting B2B SaaS and Marketplace Pricing

The State of B2B SaaS Pricing

Based on the success of the live, interactive course, I have developed a self-guided version, combining project-based learning with comprehensive tutorials to ensure every founder can master PMF at their own pace. I’ve put everything I’ve learned into this course. And, I will continue to evolve it over time.

Introducing Product Market Fit Master Class

This self paced video course has eight modules, organized to be taken one per week, with over 6 hours of video, 80 lessons and 10 + worksheets. All the materials you need to make progress at your pace. Check it out

Product Market Fit Master Class

Key Benefits:

A clear assessment of your current PMF: Gain a baseline understanding and score of your PMF, identifying strengths and areas for improvement.

A new customer and company story: Develop a sharper company narrative ready to test with customers, investors, or partners, built around your value hypothesis.

An actionable PMF plan: Create a smarter PMF plan ready for market testing, reducing key risks of failure.

Testimonials

This is what a few students who took my live class had to say about the videos and materials.

Summary of Course Modules

Module 1: Setting Your Baseline

• Objective: Establish a clear understanding of your current PMF status.

• Key Activities: Conduct a baseline PMF audit, identify gaps and strengths, and score your current PMF using the Five Factors Framework.

• Project: Complete the Baseline PMF Scorecard to assess your current position.

Module 2: Calibrate - Who is Your Hungry Customer?

• Objective: Identify and engage the right customers.

• Key Activities: Define your ideal customer profiles, segment early adopters, and develop targeted engagement strategies.

• Project: Develop and submit your Ideal Customer Profile (ICP).

Module 3: Calibrate - What is Your Magical Product?

• Objective: Position your product effectively in the market.

• Key Activities: Craft a compelling value proposition, differentiate your product from competitors, and communicate the unique benefits.

• Project: Create a Value Proposition Canvas for your product.

Module 4: Calibrate - Where is Your New Game in Category?

• Objective: Develop a unique category point of view (POV).

• Key Activities: Establish a compelling category vision, articulate your category narrative, and position your product as a leader.

• Project: Write a Category Point of View (POV) Statement.

Module 5: How To - Create & Test Your Value Story

• Objective: Build and validate your brand story.

• Key Activities: Create a value hypothesis, test your brand story with customers, and refine based on feedback.

• Project: Develop and test your Brand Story with potential customers.

Module 6: Calibrate - How to Find Repeatable GTM?

• Objective: Develop effective go-to-market (GTM) strategies.

• Key Activities: Build GTM pathways, prioritize GTM strategies, and integrate these with your overall business plan.

• Project: Outline a GTM Strategy Plan.

Module 7: Calibrate - With Whom to Build Ready Talent?

• Objective: Assemble the right team for successful execution.

• Key Activities: Assess and acquire key talent, align team roles with PMF goals, and plan for scalable growth.

• Project: Create a Talent Acquisition and Development Plan.

Module 8: How To - Build & Test Your PMF Risk Mitigation Plan

• Objective: Identify and mitigate risks associated with achieving PMF.

• Key Activities: Develop a risk mitigation plan, identify potential risks, and strategize on how to lower these risks effectively.

• Project: Develop a PMF Risk Mitigation Plan.

Module 9: Your Final Cut - Testable PMF Calibration

• Objective: Prepare your PMF plan for market testing.

• Key Activities: Review and refine your PMF story, finalize your test plan, and prepare for execution in the market.

• Project: Complete and submit your Final PMF Calibration Plan for market testing.

Additional Course Features:

• Multiple FREE chapters within the module allow you to preview

• Lifetime Access: Revisit course materials anytime to refine your strategies.

• Reference Materials with links to dig deeper

• Special Offers for Students to pursue additional courses and workshops

• 1:1 (email) feedback from me to students completing the course and submitting their final worksheets

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COMING SOON

While I still enjoy live cohorts, I’ve reduced the frequency of my live PMF course to twice a year. My main focus now is coaching CEOs on AI-driven growth. For the past six months, I’ve been developing frameworks and patterns on how AI disrupts B2B companies in product, GTM, and operations, providing guidance for CEOs to navigate these changes. I’m excited about this work and will share more soon.

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Here, we explore three common pricing strategies, provide real-world examples, and offer insights into determining your ideal value metrics, highlighting the differences between SaaS and marketplace pricing.

Three Common Pricing Strategies

Competitive Environment: Common

Pricing based on the competitive environment involves anchoring your prices to what others in your category or market are charging. This approach is often used in crowded markets with price-sensitive buyers. For example, cloud storage services like Dropbox and other small competitors commonly price based on the amount of storage provided. While not ideal, this method is practical and widely used in markets where differentiation is minimal.

Product Cost Structure: Worst

This strategy focuses on covering the product’s cost structure and ensuring a fair margin. Companies with fixed costs and fixed delivery expectations often adopt this approach. Basecamp, a project management software, is an excellent example. Basecamp uses a fixed pricing model that allows it to recover costs while delivering value to its customers. However, this approach can limit pricing power in crowded markets, especially if the product is not sufficiently differentiated to command a premium. Sometimes, there may be unique costs that a company may wish to pass onto customers (OEM product or integration costs). This may make sense but may weaken the value perception if this is not tied to value the component delivers.

Customer Value: Best

The most advantageous pricing strategy is to base prices on the value delivered to the customer. This method allows for the highest potential revenue, particularly if the product can clearly demonstrate its unique value. This also allows for feature-based capabilities that are necessary for different purposes. Salesforce, for example, offers multiple editions and tiers of its products, each with different capabilities, support levels, and custom solutions. This approach aligns pricing with the perceived value, making it the ideal strategy for businesses with unique and high-value offerings.

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Understanding Value Metrics

Determining the right value metrics is essential for aligning your pricing with the value your product delivers. For B2B SaaS companies, value metrics typically fall into two categories: user-focused and buyer-focused. For B2B marketplaces, additional considerations are needed due to the presence of both buyers and sellers.

B2B SaaS Pricing Metrics

1. User-Focused Revenue Metrics:

These metrics are tied to how users interact with and derive value from the product.

Examples include:

• Usage-Based Pricing: RESOURCE. Companies like AWS charge based on the amount of compute resources used. This makes it transparent to user the precise resource being consumed and how it is measured for pricing.

• Subscription-Based Pricing: TIME. Many SaaS Services like Zoom charge on a monthly basis. This is the simplest since pricing is time based.

• Seat-Based Pricing: VOLUME. Also, many SaaS apps charge by number of users. Salesforce charges based on the number of sales reps using its product. Most productivity apps which have large user base in a company price based on seats.

2. Buyer-Focused Revenue Metrics:

Buyer cares about total value delivered within a predictable budget over a fixed period of time. They may be open to additional services along the way. So, their pricing considerations get reflected in annual contracts and SOWs, even MSA (master service agreements).

Examples include:

• Annual Contracts: BUDGET. Companies like Oracle, SAP, and Adobe Cloud often use annual contract values (ACV) and service bundles for long-term agreements with buyers.

The key is to understand each of these value metrics and what combination of user & buyer metrics are appropriate for your SaaS product and company business model.

B2B Marketplace Pricing Metrics

B2B marketplaces need to consider both the buyer and seller sides, often resulting in potentially more complex pricing models:

1. Transaction-Based Pricing:

This is the most common market place pricing model. It involves taking a percentage of the transaction value passing through the platform. Examples include:

• Take Rate: Marketplaces like eBay or Etsy charge a fee based on the transaction value between buyers and sellers.

There are additional revenue sources in a marketplace, based on demand (buyer) and supply (seller) side dynamics.

2. Subscription-Based Pricing:

Either the buyers or the sellers, or both based on the market dynamics, may be charged subscription fees to access the marketplace. Typically, if the market place is built on demand aggregation, buyers access the services for free and sellers are charged . The opposite may be true if the supply aggregation is of high value.

Examples include:

• Seller Subscriptions: Shopify charges sellers a monthly fee to use its platform.

• Buyer Subscriptions: LinkedIn Sales Navigator charges buyers based on the number of seats.

3. Usage-Based Pricing:

Similar to SaaS, marketplaces may charge either the buyer or the seller (or both) for usage of a marketplace resource. For instance:

• API Call Charges: Google Cloud Marketplace charges based on the number of API calls or resources consumed by buyers.

Again, a B2B marketplace should understand the right mix of these value metrics for their marketplace pricing.

Applying the Principles

Choosing the right value metrics requires a deep understanding of your business model and the value you create for your customers. Here are some key principles to consider:

• Align Metrics with Business Model: Choose metrics that closely reflect the value you create.

• Combine User and Buyer Metrics: In some cases, a combination of metrics may be necessary.

• Proximity to Customer Value: Select metrics that serve as a proxy for the value generated, making it easy for buyers to understand and purchase.

Conclusion

Setting the right pricing strategy and identifying the correct value metrics can significantly impact your business’s success. Aligning your value metrics with how your product generates customer value will help you create a sustainable and profitable pricing strategy.

Whether you are a SaaS company focusing on user or buyer-based metrics or a marketplace balancing the needs of both buyers and sellers, these principles can guide your pricing decisions.

Key Takeaways

1. Contrarian but Correct Viewpoints:

I believe that having a unique perspective that challenges the consensus can significantly differentiate AI offerings. A contrarian point of view, one that is held by a minority but is correct, can set you apart in the competitive landscape.

2. Reformulating Category Problems:

Identifying novel insights within a category can redefine problems and open new avenues for innovation. It’s crucial to look at the problems from a buyer’s perspective and rethink the current solutions and substitutes being used to address those problems.

3. Generating Unique Solutions:

Combining new capabilities with existing ones can create a distinctive competitive edge. This involves reformulating your tech offering to address the redefined problems in a novel way.

Examples

There are many ways to reformulate problems within a category, such as customer discovery, root cause analysis, and five-factor forces analysis.

For simplicity, let’s focus on examining functional and structural constraints and trade-offs in a category. By understanding these trade-offs, we can identify novel insights and create innovative solutions.

Here are a few hypothetical examples to illustrate the process.

Manufacturing:

Reformulating the Problem:

In the manufacturing sector, a head of procurement typically grapples with challenges like cost-effectiveness and reliability of suppliers, optimizing inventory levels, and managing supplier risk. The trade-offs they face include balancing cost versus quality and lead time versus inventory.

Generating a Novel Insight:

By introducing AI, we can dynamically balance these trade-offs. For instance, AI could predict and optimize procurement processes, leading to better cost management and quality assurance.

Reformulating the Solution:

This approach not only enhances efficiency but also provides a fresh perspective on managing procurement challenges.

Retail:

Reformulating the Problem:

In retail, particularly for a head of merchandising, the main challenges include optimizing product mix, driving pricing strategies, and enhancing customer experience across channels. The trade-offs here often involve shelf space versus product variety and turnover versus stock availability.

Generating a Novel Insight:

AI can revolutionize this space by continuously analyzing consumer behavior and dynamically adjusting the product assortment.

Reformulating the Solution:

Imagine an AI system that personalizes product recommendations in real time, thereby optimizing both shelf space and inventory turnover while enhancing customer satisfaction.

Banking:

Reformulating the Problem:

For the banking sector, focusing on the head of customer service, the primary challenges are customer satisfaction versus cost efficiency and response time versus quality of service.

Generating a Novel Insight:

AI can significantly impact this area by providing personalized assistance, optimizing customer service solutions, and integrating seamlessly with legacy systems.

Reformulating the Solution:

For example, AI could automate routine tasks, freeing up human agents to handle more complex queries, thus improving overall customer satisfaction and reducing operational costs.

Framework for Contrarian Viewpoints

To generate a contrarian point of view, start by looking at the existing trade-offs in your industry and ask novel questions. For instance, in each of the examples above, I examined the current constraints and trade-offs to identify opportunities where AI could offer a new solution.

Here are the steps I recommend:

1. Reformulate the Problem: Look at the current structural and functional constraints and trade-offs in your category.

2. Generate a Novel Insight: Ask what-if questions to explore new ways of solving the existing problems using AI.

3. Reformulate the Solution: Combine new AI capabilities with existing ones to create a differentiated offering.

By following this framework, you can create a contrarian point of view that sets you apart from competitors. This isn’t about positioning; it’s about understanding where to play and how to carve out a unique space in the market.

Conclusion

To conclude, reflect on companies like Salesforce, Amazon, and Stripe, which all started with a fresh take on existing problems. Salesforce revolutionized CRM with SaaS, Amazon disrupted IT infrastructure with cloud services, and Stripe simplified online payments with easy-to-integrate APIs.

Each of these companies asked the critical questions: What is our fresh take on the problem? How is our tech offering different? What is the unique customer outcome we deliver?

I encourage you to apply this template to your own business and explore the possibilities of creating a contrarian point of view.

Let’s continue to challenge the status quo and unlock new opportunities for innovation and growth.

• ​how do you decide where to layer AI into your current roadmap?

• ​how do your business buyers & users assess AI value?

• ​how do you change your positioning and GTM with AI?

Recently, I had an opportunity to present my framework for "Layering AI into Your B2B Products” in a conversation with Ibrahim Bashir, VP Product Management at Amplitude.

In this video, we cover several key aspects of integrating AI into B2B products, including challenges, and practical approaches. We had a lively discussion at the end by product leaders who had attended. 

Here is a quick summary

Introduction: We kick off with a discussion of why integration of AI into B2B products is needed, highlighting the excitement and challenges of developing an AI strategy in response to increasing demands from leadership, customers, and partners.

Framework for Integrating AI: I discuss the need for a mental model that simplifies understanding where AI can be beneficial in a product and buyer journey. 

Separation of Concerns: The B2B environment typically involves buyers focused on business metrics and goals, users executing tasks, and teams collaborating on workflows. These are typically divided into three layers: users, teams, and buyers, each with distinct needs and challenges. 

Challenges with Current Frameworks: The current value frameworks across three layers often suffer from slow human intelligence feedback loops. When goals are not met, the process of identifying issues and implementing changes can be lengthy and inefficient.

AI Modes: I introduce four practical AI modes:

1. Redundant: Eliminate tasks that no longer require human involvement.

2. Repetitive Routine: Automate repetitive and low-risk tasks, such as email filtering and customer support triage.

3. Real-Time: Provide real-time assistance in tasks requiring immediate responses, like live chat support.

4. Augmentation: Support complex tasks that require human insight and reflection, such as strategic analysis.

Practical Applications Across Layers:

I apply these AI modes to each of the layers, and discuss implications

1. User Layer: AI can improve user productivity by eliminating redundant tasks (e.g., data extraction), automating repetitive tasks (e.g., meeting scheduling), providing real-time assistance (e.g., sales calls), and enhancing performance feedback.

2. Team Layer: AI can streamline team workflows by automating manual processes (e.g., resume screening), improving collaboration, and real-time workflow adjustments.

3. Buyer Layer: AI can help buyers with decision-making by automating routine reports, providing predictive analytics for maintenance, and supporting strategic decisions with comprehensive data analysis.

Even if current products are enhanced with AI capabilities, there is a possibility of getting disrupted by AI-first products which follow a different value ladder based on AI feedback loops versus the current value structure of human feedback. 

AI First Value Ladder

• The main feature of AI first value ladder is the transition from improving current tasks and workflows to directly achieving business goals.

• The potential disruption AI can bring by starting with the goal and working backward to streamline workflows and tasks.

Go-To-Market (GTM) Implications:

• Considerations for targeting and differentiating AI-enhanced products from existing products on one hand and AI-first products that may not be realized for a while.

• The impact of AI on GTM strategies, including the potential need for new pricing models, packaging, and positioning.

Conclusion: We stress the importance of a strategic approach to AI integration, focusing on enhancing value across user productivity, team collaboration, and buyer decision-making. We also highlight the potential for AI to disrupt traditional frameworks, urging businesses to prepare for the evolving landscape.

I would love to get feedback on the framework and discussion. 

https://x.com/naval/status/1794574861987434883

Here’s a set of tweets, seven in total, that neatly define what product-market fit is for an early stage founder. As with all the work of Naval Ravikant, it is pithy but packed with insights, worth reflecting on. Like a good shloka (a hymn) it has layers of meaning. 

Let’s unpack some of the ideas behind product-market-fit as described in this shloka. 

A product is a theory of how to solve a problem

Typically, the most common way of thinking about a product is to view it as a utility, a tool in a toolbox. This is the perspective of the job-to-be-done theory popularized by Clayton Christensen in which the product is “hired” by a user to get a job done. 

This verse defines a product very differently from this utility view. Here a product is NOT defined as a set of capabilities or a utility.  Of course, a product can be all of these, yet in this formulation, the product is called a theory. And what is this theory good for? To solve a problem. 

So the product is defined in terms of an aim, its purpose, And the purpose is to solve a customer problem. How does a product solve a problem? Instead of defining its capabilities, a product is defined as a theory, a set of hypotheses, and assumptions that will solve a problem if implemented. This is a powerful way to conceive a product.

A theory of product includes not just a set of features, capabilities, architecture, user experience, data model, and supporting implementation, but ALL of these things combined as a way to solve a problem.

Once deployed, the product solves the problem according to the theory embedded within it.

In fact, this is an Aristotelian view of an entity. A product has a unique form and function directed at a final cause, which is to solve a problem. Its form and function embodies the theory of how it solves the problem, encompassing all the elements and their arrangements that go into achieving its purpose. 

Another way to think about a product as a theory of how to solve a problem is by considering the view of W. Brian Arthur, a complexity theorist and economist. In his book, The Nature of Technology, he defines a technology product as a way of encapsulating a phenomenon in a new configuration for a commercial purpose. According to Brian Arthur, the phenomena captured in the product can be of three different types: digital (such as an algorithm), physical (as in chips and sensors), or social (such as sharing messages). This perspective aligns with the vision of a product as a theory for solving a problem.

In summary, the first verse establishes the foundation for product-market fit: the primacy of the problem and the role of the product in solving that problem.

Marketing is a unique explanation of a solution

If a product, with its unique theory of addressing and solving a problem, is the first foundation of PMF, the second foundation is how exactly the solution creates customer value. 

Typically, solution explanations focus on the capabilities of a product, with marketers spending a lot of energy discussing these features, use cases and so forth. Sometimes, they might emphasize the multitude of benefits that a product unlocks. However, in most cases, marketing can be off the mark, inadequate, or not credible. 

According to this verse, what is needed is much simpler and harder: a unique explanation of how the solution solves the problem.

Such an explanation must be unique, if the product is to be seen as a better alternative to existing solutions or alternatives.

Once again, consider what this verse is not emphasizing. It is not emphasizing the typical market considerations, like the large addressable market (TAM) or a technology-led opportunity, such as genAI in a domain. Instead, it focuses on whether the solution to address the problem can be explained in a unique way. This uniqueness can stem from two sources: either from the new theory of the product (e.g. a new phenomenon your product captures) or from a unique insight into the problem that competitive products and their inferior theories don’t address. Today, most AI startups suffer from solution explanations that are not different from their AI competitors. 

In summary, the second verse establishes the second important aspect of product-market fit: the need to have a solution with an explainable difference against alternatives.

A founder is uniquely qualified to build a company. 

Some founders start with the ability to build a product, others from their experience of a customer problem. A rare few may have a deep tech-based vision (a unique explanation). What makes a founder uniquely qualified to build a company? 

When the founder has the right mix of these three elements: a clear view of the problem, a unique theory of the product that solves the problem, and an ability to explain the uniqueness of the solution.

Not all of these are needed at inception.  Which of these are important to have from the start, and which may be acquired during the product-market-fit process varies by each situation. But the founder must build these capabilities on their own or bring them together within their team until they have fine tuned their instincts across each of these three areas. Over time, this requires the founder to build their judgment across each of these areas.

In summary, the third verse establishes the uniqueness of the founder in building a one-of–kind company. 

Good products are hard to vary. 

What does it mean that a good product is “hard to vary”? Wouldn’t a product that is hard to vary be fragile? To understand this, we need to understand the idea of “hard to vary” explanation in science. 

A “hard to vary” explanation is a powerful concept articulated by the physicist David Deutsch and influenced by science philosopher Karl Popper. An explanation that is hard to vary loses its coherence and explanatory power if any of its elements is altered.

Consider the theory of evolution by natural selection. This theory is hard to vary because its explanatory power relies on specific mechanisms (variation, inheritance, and selection). Altering these core components would lead to a fundamentally different and less coherent explanation of biological diversity. 

Naval applies this concept to product, marketing and founders. A good theory of product should be hard to vary. If major elements of your product can be modified arbitrarily without affecting how the problem is solved, then your product is not a good fit for solving the problem. That is, your product should be designed precisely to solve the core customer problem elegantly.  

Good marketing is hard to vary. 

Similarly, good marketing should be hard to vary. Your explanation of how your solution works should be cogent, rational and clearly linked from your product theory (how your product works) to the problem resolution.  Too much irrelevant detail of features or too many tall claims (“10X ROI”) weaken the story. A “hard is vary” explanation is also hard to copy by others by simply using the same words or claims. 

Good founders are hard to vary. 

Each company is built to solve a hard customer problem with a unique theory of product. A “hard to vary” founder brings their unique talent to building a company, based on their judgements on problem space, product theory and its explanatory power. Therefore a good founder is hard to replace, since their judgment can not be replaced especially during inception and growth stages of a company. If founders were fungible, you would not be able to build a good company.

Founder, product, market, fit.

When done right, the founder, the product and the market fit together in a coherent relationship. The Product-Market-Fit (PMF) emerges when the founder builds a one-of-a-kind company based on their talent, anchored on a new product theory they build to solve a hard customer problem with a unique explanation in the market. 

This view of PMF is not counter to conventional PMF views, such as finding repeatable customer growth in an early market niche. In fact, it underpins these views with philosophy of scientific explanations. 

REFERENCES

David Deutsch 

Explanatory Power

Brian Arthur

In my previous post I covered why category games matter, and how to define and set a new game. 

In this post, I cover how to segment, target and position your new game, once you have defined it. I also give an example of Apigee API management software (now Google Cloud) where I was part of this game play. Finally, in the appendix, I touch upon the challenge of creating a new game in a new category.  

How to Segment, Target & Position the New Game

Segment the Market by Your POV

Most tech companies make the common mistake of chasing the wrong customers. Despite polite noises to the contrary, most people don’t like to change. And any new technology that triggers change, hopefully to create value, disrupts the status quo. As we noted in the previous post, most customers will reject your contrarian POV as it would require changing their current criteria of tech purchase or usage. They may find your provocative vision of an inevitable future too risky and ambiguous. 

Your should NOT spend any time with customers who reject your contrarian POV.

To make sure this happens, you need to segment the market. You need to find only the customers who are willing to take a chance on you. Talking to any and all customers is a common mistake among early stage founders and product leaders. Each of your discovery calls need to be tagged for those who have an appetite for risk in your category. 

Most tech companies begin with established segmentation of the market. Some common criteria are: customer size, geography, industry. Others might be by product type, customer attitude to tech adoption, etc. And then they choose a criteria unwittingly, such as enterprises where they have a network, start-ups who are growing, or small businesses that are easy to access.  They find mixed responses from a scatter plot of prospects who are across the tech adoption spectrum (early adopters to laggards). 

There is a better way to segment for early adopters, based directly on your contrarian POV. 

Here are a few sharp questions that help you segment for early adopters. 

1. How well does your contrarian POV fit with the needs of each current segment? Talk to prospects in each segment and check for what aspects of your POV resonate and what are considered too risky or irrelevant. Based on the responses, can you re-segment the market, based on your contrarian POV? 

2. Who are the users most willing to change their selection criteria to adopt your product? What are their needs? Can you re-segment the user audience based on this criteria? 

3. Who are the buyers most willing to change their purchase criteria? What are their needs? Can you re-segment the buyer audience based on this criteria? 

4. Which of these new segments have the most hungry and desperate customers? 

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Target Early Adopters who Subscribe to the Inevitable Future

Once you have re-segmented the market, define the ideal profiles of users and buyers in the segment.

You need to search and find such an Early Adopter Segment because you are unlikely to find product market fit outside of it.  

This is also where you set your new category game in motion.

Position Your POV to Your Magical Product

This chart shows how to position your new game in an existing category by anchoring it to your magical product. 

The goal should be to create and deliver a laser-sharp positioning to early adopters, even if all other customers reject it.

This is counter-intuitive and hard for many tech leaders. 

For early adopters, this positioning should be a coherent narrative that works top-down, bottom-up or middle out.

Tech company founders and leaders need to get fluent in presenting the narrative to buyers and users in the segment, based on business context.  

For instance, your top down buyer story could go from painting an inevitable future that will unfold from a contrarian POV through a category dynamic (differentiate, dominate or disrupt) generated by solving a burning problem which is enabled by a new value prop created by a magical product that encapsulates a new phenomenon but requires changing the purchase criteria.  

Your bottom up user story could demonstrate how the tech “phenomenon” in your product unlocks a better way to progress their job, but requires accepting and learning a new way of selecting and using the product, which unlocks previously unrealized value for the company, and takes them into a new future. 

Or this story may be abbreviated or combined differently for emphasis.  

Playing the New Game: Apigee Case Study

Here is an example of how to play the new game, based on my experience at Apigee software.  

From 2012-2016, API management was a fast growing dynamic category with vendors offering different solutions to help enterprises build, secure and manage their portfolio of internal and external APIs. Apigee was an early stage company (<$100M) in this category surrounded by several competitors (Mulesoft, IBM, CA, Tibco), each significantly larger than us. 

Here is the new category game we defined, set and played: 

Category landscape

The API management category is complex. Back in 2014, most of the large vendors had origins in legacy technologies of EAI (enterprise application integration). Their solutions required significant IT resources and skills to build and maintain internal APIs. These were needed for internal interoperability, but were not developer friendly. As the world moved to mobile apps, companies had to open up access to their internal back end systems for developers to build new nimble apps, internally for employees or externally for customers and partners. Rest APIs were the new technology to connect such apps. 

A novel insight 

Most companies needed a simpler way to expose and manage data from internal systems through rest APIs. This was understood by many, but appreciated by only a few players. Our novel insight was that simpler ways to build and manage APIs would change how large companies modernized their legacy systems. 

Our contrarian POV

We understood that large enterprises like retailers, banks and healthcare systems had to build modern apps to serve their customers. This meant they had to give access to developers who were building such apps access to their internal data strewn across systems. Yet these legacy systems were not built to offer easy access to such data. Our POV was that businesses would need a developer friendly front end to discover, build and connect data - and their IT teams would need a secure and scalable way to manage this access. We offered the most developer friendly API management platform. This POV was contrarian, since it was acknowledged by many but practiced by few customers or vendors. 

Changed criteria

We advocated for new criteria to evaluate API management, such as ease of developer use and time-to-hello world.  We shifted the business discussion to the value of delivering digital projects on time, rather than managing data integration on the IT roadmap. And we emphasized the role of IT to manage developer-friendly APIs across backends to achieve these business outcomes. This was different from large competitors (IBM, CA technologies) who were tied to IT driven roadmaps. 

A new game

We positioned Apigee as a (niche) differentiator who could deliver major digital transformation projects across multiple cloud platforms. This set a new dynamic in the category. While we worked with IT teams, we also sought business leaders who had to deliver major digital projects that depended on modern APIs. 

An inevitable future

Our view was that business transformation was a matter of survival. Companies across sectors like retail, banking, healthcare and manufacturing had to modernize their IT. They needed to empower developers with modern APIs to build and connect new apps, data and devices for their employees, customers and partners. Business and IT leaders had to champion this new way of working and building within their companies. We defined the vision of this API-enabled future and evangelized it more than anyone else in the category. 

Apigee Game Play

Playing a new category game requires a sustained and coordinated effort across company leadership to win the game. It is not just marketing talk.

Here are a few tactics of our game play in different arenas:

Customer journey 

• We aligned sales, marketing and delivery teams to this POV. 

• Our sales pitch started with our POV. 

• Our demos showcased our product magic for developers. 

• Our pre-sales teams pushed prospects for developer hackathons to showcase our differentiation. 

• We had a team of experts who ran change management courses for customers going through API-enabled transformation. 

• Our customer champions & testimonials spoke of the business impact of APIs - not just IT benefits. 

Product and communities 

• We aligned our product roadmap with our POV to showcase developer portals and the cloud-first capabilities.

• We supported and built for FHIR, an open source API framework and community in healthcare.

• We supported and built for the Open Banking API regulations in Europe.

• We built industry specific APIs frameworks in healthcare, banking and retails to help our service partners build solutions. 

Analysts and influencers 

• We successfully advocated changes to how industry analysts like Gartner and Forrester scored the category. 

• We funded and partnered on research with MIT on how APIs changed business metrics. 

• Our S1-IPO filing included this vision. 

Market awareness

• We named our customer conferences “Adapt or Die'' which became our call to arms. Our branding even included making a series of short narrative fictional films on this theme. [post, film, film]

Outcome

In 2016, Apigee was acquired by Google as we were rated as #1 visionary leader by Gartner in their Magic Quadrant despite being one of the smallest vendors. By 2018, Google became a leader in multi-cloud API management platform. This was a result of multi-year effort across the company to set, play and win the category game.

APPENDIX: Creating a New Category, Not Just a New Game 

The craft for creating new categories is very different from simply playing by the rules of an existing category. According to Category Pirates, if you are creating a new category, you need to get three things right: product design, company design, and category design. 

Most startups are not radical enough to create a new category. So I haven't covered new category creation at length. However, some tech companies may evolve over time to create a new category. The framework we have developed above can be extended playing a new game in a new category.   

To do so in our framework, we need to develop four ideas upfront - or over time. 

1. Your tech offering has to radically change the category criteria to create value in a different way. 

2. Your business model has to radically change to capture value in a different way. 

3. Your GTM ecosystem (partners, channels, etc.) has to radically change to deliver value in a different way.

4. The virtuous data feedback loops need build value in the three areas (product, business model, ecosystem) 

Examples:

Since this takes us on a new tangent, I will come back to this topic in a future post.  

This is Part 2 in a blog series on Category Management 

Part 1: What’s Your Category Game

References 

About Apigee

• Why did an API software company make a spy movie?

• Brand narrative films: here and here 

Every ambitious tech founder wants to create a new category. Yet, every repeat founder knows how hard it is to do so.

The market you choose to enter and the category you play in is one of the most existential questions for a tech company.

Even then, many startups fail having never understood the dynamics of the category they are part of - willingly or not.  

In B2B, how is your market different from your software category? Why should you care about the category? Why isn’t having loyal customers or a cool product enough? And, if you need to play in a category, how do you set your game that helps you win?

These are the questions I explore in this post and the next. 

Category versus Market

Often, founders conflate the market with the category. All tech products need to solve a problem to be viable. And in business, all products need to find buyers.

Category is the problem space you apply your product to, and the market is where you find the buyers. 

You can categorize a problem space based on many attributes: some external (use cases, Job-To-Be-Done, purchase criteria, etc.) and some internal (product capabilities, underlying tech etc.).

Let us take an example. Suppose you have an AI-based data annotation product. The problem space or the category is “how to annotate data for ML purposes at scale”. This can be further categorized in many ways - by users (marketers, agencies), buyers (VP Marketing), jobs (creating blogs, writing web copy) and so on. Not only do you have direct competitors, you may have custom-built solutions, open source alternatives and substitutes that compete in the problem space. 

The market is simply where the buyers are for your category. Market can be segmented by geography, size, vertical and so forth. 

Tech founders often view the category-market combination as an amorphous landscape. They may choose to select a corner based on chance or opportunity. This is dangerous because how you segment your category and how you select your early market determines your short-term success in finding product market fit and long term success in reaching scale. 

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Category Dynamic - An Underrated PMF Factor

Most founders focus on building a useful product for early customers. This is as it should be. However, the category is the water you swim in.

The category dynamic can either help or hinder you to find the right customers early on, and to scale with the right customer segment later on. This helps you find the right entry point into a market, and sets the right trajectory for growth.

Category is the most underrated factor in finding product market fit. It is not enough to understand the category, you need to know how to play the category game in a way that improves your odds of success. 

Why Category Games Matter

Category leaders capture 75% of the profits.

This is true across various businesses. Categories are created and destroyed through new tech.  Today, Chat GPT and LLMs are wreaking havoc by reconfiguring existing categories - from software infrastructure to business apps to consumer solutions.

New business models can also create and destroy categories.

SaaS subscription model powered Salesforce, and obsoleted most on-premise software, whereas CPC (cost per click) in advertising changed the fate of Google. 

So, winning a category game is high stakes.

Category wars are funded, won and lost years in advance of mass adoption.

By the time Microsoft Azure and Google Cloud entered the cloud computing category, Amazon AWS had more than five years head start. 

Many founders find the category dynamic confusing because their ground reality appears to be different. Their early adopter customers are hungry to solve a problem, and usually don’t care about category definitions. It is the late majority & laggard customers who adopt a product based on category leadership - but only after a category is proven, and the leader evident.

There is a survivor bias to category leadership.

Only those innovative companies who survive in creating new categories get to be category leaders. Therefore, it may appear that new categories are “created” in hindsight. They are usually anointed by industry and financial analysts for the buyers who don’t want to get “fired for choosing IBM.” 

This paradox makes early stage founders complacent about their category. They find product market fit and start building for the growth stage, but by then the die may be cast for their category game. For this reason, your category game may set the upper limit to your growth. 

Choice: To Create or Join a Category

You can either create a category or join one. This is a consequential choice - so you need to consider it carefully.  

Creating a new category requires a radical rethinking of your business. We consider Uber, AirBnB, Tesla, Apple as creators of new categories for a good reason. They offer more than innovative products. Each of them built a new business model, a new ecosystem of partners, and a new go-to-market around their tech.

So, category creation is a different craft than simply finding the product-market fit for a novel offering among an existing set of competitors. 

There are lessons to be learned from category creation to compete better in an established category. Majority of tech companies join an existing category. So that is what I focus on in this post. However, I will cover the topic of creating a new category briefly in the appendix of the next post. 

Share Growth Matters

Competing in an Existing Category with a New Game 

To join and compete in an existing category, you need to set up and play a new game to win against incumbents. The new game is about advocating a point of view (POV) to solve a hard set of problems, trade offs and compromises that have not been addressed in the category by other companies to date. Your category POV has to be a non-consensus viewpoint else it would be common practice among customers already. For this POV to have teeth, the users and buyers must change the criteria by which they select and purchase products respectively. 

Notice that this new category game has to be rooted in your product differentiation but it is not about the product.

It is about changing the category scorecard in your favor so that some customers - the early adopters - who are aligned to these new, changed criteria, choose your product to solve hitherto hard problems. 

Here is how you do this. 

Understand the Current Games

Any good problem space already has various solutions and multiple vendors working to deliver these. Each might have a slightly different take on the problem space - and would optimize for different trade-offs to solve the category problems. Yet, there is often conventional wisdom in your category.  

Start with these two questions. 

What is the consensus POV that majority players in the market agree on?  

And what’s the projected future based on it?

Example: Generative AI

Gen AI technologies are a seismic shift that are upending virtually every software category. There are consensus views on how these will change industries, work practices and economies at scale, as described by analysts like McKinsey or summarized by LinkedIn. These are informative at macroeconomic level but of little consequence for tech startups. 

In problem spaces (categories) like gen AI for healthcare payers, gen AI for shopping online, or gen AI for coding, or chat bots like Bard there are consensus POVs emerging from large incumbents. Dozens of startups in every category, such as AI content creation, are attacking the problems with similar insights.

In the sea of sameness, it is important to begin with how you are different from the very start. 

How to Set a New Game

Here is the summary dynamic of a new game. 

You begin with a novel insight into solving a category problem, tradeoff or constraint that has not been addressed by incumbents.

To realize this insight you offer a new way contrary to conventional wisdom. This contrarian POV now can be the basis of your market differentiation. Note that we are still not talking about product features per se.

For it to be effective, your POV must change or elevate the criteria for using or buying the category - contrary to consensus. 

It is important to note that such a contrarian POV will not be accepted by most customers. In fact, usually it would be rejected by the vast majority of customers who are not willing to acknowledge or accept the importance of the novel insight - or your approach to solving it.  

This sets up a challenge for the tech company.

They need to identify those early adopters who are desperate enough to solve the problem based on the novel insight and contrarian POV.

Ideally, you create a vision of a new but inevitable future that unfolds from your POV that appeals to early adopters.

Let us break each of these steps down with examples. 

Find a Novel Insight into the Category

Each problem space is complex with many unsolved challenges. You should begin with understanding the major problems, trade offs or compromises in your category.  What are your views on the root causes of the category problems? How well can you assess these causes? Do you understand the different customer & market segments affected by these?

The best start to a new game is to begin with a novel insight (tech or business) to address one of the specific category challenges. 

Three examples:

Salesforce started with the insight that on-premise sales software is harder to install, maintain and upgrade. And that was the beginning of the SaaS (software-as-a-service) app revolution. 

Amazon AWS started with the novel insight that developers don’t want to manage complexity and cost of compute infrastructure. And so was born cloud computing that offered developers low cost compute resources whenever they wanted at pay-as-you-go pricing. 

Stripe started with the insight that developers find it hard to implement payments into the commercial apps or websites. This resulted in an extremely easy to use payment API-as-a-product that hid all underlying complexity of implementing and scaling payments on a website.  

In many B2B software categories, an insight is as straightforward as: a better user experience, coherent design, simpler integration, higher service levels, faster time to delivery, cheaper ways to collaborate, etc.

Usually, a novel insight should suggest a new way to solve the problem. Else it is banal. 

Generate a Contrarian POV  

Here are three questions to find the differences:

How is your approach different from others to solving the category problem? 

How is your tech offering different?

A common mistake is to think of a product in technical terms. Of course, it may well have a hard core tech like a new AI model, a new schema or a new patent. But more often, it can also be a method, a framework and an economic model or combination of these. Therefore, I use the term offering - not simply product. There is a big difference between the two. An offering incorporates more than your tech. Brian Arthur calls this a “phenomenon” that your technology captures. Peter Thiel calls this “a secret”. A perfect example of this is Google ad tech offering - it uses both search engine algorithms and PPC pay-per-click ad model. 

How is the customer outcome different for the category problem? Focus on the biggest difference on the value, from customer perspective

Your cogent answers to these questions will become your contrarian POV. This sets up your differentiation at the highest level of the category. 

Salesforce: IT does not need to deploy sales software on-premise because the Salesforce app runs in the cloud. 

AWS: Developers do not need IT to buy and manage expensive infrastructure because Amazon AWS offers flexible compute-as-you-go resources.

Stripe: Developers do not need to build or manage complex payment APIs because Stripe has the easiest way to deploy full payment API without any management headache. 

Canva: People do not need expensive and complex design tools (Adobe Illustrator, Photoshop) to create and collaborate on graphic projects because Canva offers lower pricing and better collaboration for designers and non-designers alike. 

Determine How Your POV Changes Category Criteria

Having an intellectual POV is not enough.

Your POV should affect how your users select and use your product and how your buyers evaluate and purchase your product over all other competitors in the market. 

Identify Your New Game for USERS

From your Contrarian POV, you need to identify the changes to the users’ jobs to be done (JTBD) - and how would you position these changes to them. 

These are the questions to consider along two axes:

JTBD Progress: Better or Worse? 

How does your tech offering change users’ JTBD? Do their jobs improve by using your product or do they need to overcome more obstacles and main?  How would users use your product in either case? 

JTBD Economics: Cheaper or Expensive? 

How does your tech offering change users’ economics? Is it cheaper or more expensive to get the job done? How would users select you in either case? 

This can be summarized in competitive terms as your new game for category usage. 

DOMINATE by redefining category usage

Amazon AWS was adopted by developers who saw the value of both cheaper and better infrastructure management. 

DISRUPT by challenging category usage 

Remember it is possible to have a setback in user JTBD in B2B categories if the economic trade off is clear. A business owner may voluntarily use an inferior or narrower product if it is cheaper. Or, a senior buyer may mandate use of cheaper products among users even if their JTBD gets harder. A lot of regulatory, security and compliance products fit here. 

DIFFERENTIATE by niche-ing category usage

Slack was adopted by engineering teams despite the fact that they had access to cheaper and often free collaboration software like email and chat. 

This new game should be identified and positioned in user growth strategies. 

Identify Your New Game for BUYERS

Similarly, you need to identify the changes to the buyers’ business value stream based on your contrarian POV - and how to position these changes to them. 

These are the questions to consider along two axes:

Business Value Stream: Better or Worse? 

How does your tech offering change buyers’ value stream? Does their business stream improve or get worse by using your product? How would buyers evaluate your product in either case? 

Business Value Stream: Cheaper or Expensive? 

How does your tech offering change buyer economics? Is your offering cheaper or more expensive versus alternatives? How would buyers purchase you in either case? 

This can be summarized in competitive terms as your new game for category purchase. 

DOMINATE by redefining category purchase

Canva has disrupted the graphic design industry by making design more accessible to everyone with its limited features but easy creating and sharing capabilities at lower pricing. This has forced traditional graphic design software companies to rethink their complex products and high pricing.

DISRUPT by challenging category purchase 

Amazon AWS was not adopted by IT buyers initially. Although AWS dominated the developers who loved the cloud compute capabilities, there were a lot of buyer shortcomings in the area of security, customization, control and budgeting. AWS challenged the purchase criteria starting with a small group of early adopter IT leaders, and slowly moved to dominating the category, once the mainstream buyers had accepted the buying criteria of cloud computing. 

DIFFERENTIATE by niche-ing category purchase

Once Slack was adopted by users (engineering teams), the buyers were able to accept that a premium price had to be paid for an integrated chat-email product beyond what was available through incumbent at that time. Only after five years did the Microsoft Team product catch up to overtake the differentiated niche of Slack.  

This new game should be identified and positioned in buyer growth strategies. 

Paint an Inevitable Future from Your New Game

Once you define a new game, it is helpful to paint a future that could play out if your contrarian point of view were adopted by the category. While I am calling this an inevitable future, this may not play out exactly as the tech company expects.

How well this inevitable future co-evolves in the market depends on how you segment, target and position your new game.  I cover that in Part 2 of this series. 

This is Part 1 in a blog series on Category Management.

Part 2: Playing a New Category Game

References 

• Category Pirates

• The Difference Between a First Mover and a Category Creator

• The One Who Defines the Category Wins the Category | by David Sacks | Craft Ventures | Medium

• Business Canvas - Business Models & Value Propositions

• Economic Potential of Gen AI - McKinsey 

• Peter Theil’s seminal book: Zero to One 

• My short blog post on Zero to One: A Radical Manifesto for Tech Pioneers 

• The Nature of Technology W. Brian Arthur

Challenge: Aligning Problem to Promise

Most of the time, founders sweat a lot on what’s inside their tech product. I have written here about how critical it is to get the right product insight that is anchored in tech differentiation. However, having a magical product is not enough. Tech founders must begin by identifying a burning problem their customers face.  After all, the changes wrought by a new product are hard enough without a clear business problem that customers need to solve. That’s the very definition of a hungry customer. So, how do you tie your magical product to the problem? With a bold promise that can be delivered only through your product. 

Therefore you need to align the problem the customer wants solved and the promise your product makes to solve it. For a B2B product especially, this alignment runs from the magical product to an offering which is delivered through a market pathway (“GTM) that reaches the customer. 

The problem-to-promise chain can be visualized as below. I’ve written in my previous articles on different business customer types and the common growth pathways (“GTM”) to reach them. In this note, I want to focus on the core distinction between Product and Offering.

Even if you have identified a hungry customer with a burning problem, how does your product deliver on its promise? What exactly is the offering to your customer? This interplay between product and offering can affect your product roadmap and the growth pathways to the customer. 

Another way to state the alignment challenge is:

Not all products lend themselves to all the growth pathways. So, what’s the optimal way to align your product to your customer and GTM?

Tech Product versus Offering

Tech Product

What is a tech product? The economist Brian Arthur explores this question in his seminal work on the nature of technology. Here is a definition based on his work: 

A tech product harnesses a new phenomenon of a technology to achieve a commercial purpose. 

According to Arthur, there are broadly three areas of new “phenomena” that a technology harnesses, with some that are a blend of these:

Digital or computational, such as software methods, algorithms, artifacts, and architectures. Examples include large language models in AI, virtualization in cloud computing, quantum computing.

Physical, such as new sensors, chips, devices, arrays, and materials that come from new ways to control physics or bio-chemical properties of nature. Examples include cameras, lithium batteries, and various nano-technologies.

Behavioral, based on human psychology at the individual, team, or societal level. Examples include most highly engaging applications from Instagram to Slack to Fortnite, which depend upon unlocking a personal or social behavior.

A tech product evolves over time as the underlying phenomenon is better understood, further developed, and gets embedded deeper in its environment. This tech trajectory has a path of its own, not necessarily tied to commercial purpose. For example, how the large language models improve, how the physics of sensors is better understood is only partly shaped through customer usage and feedback. So, a novel product that encapsulates the magic of technology may not be enough to deliver commercial value. 

Offering

Therefore, a tech product has to be more than a bundle of technology features. In fact, the “whole product,” called the offering, includes all elements needed to generate value from the product for both the user and the buyer. Typically, this includes pricing, terms of use, services to implement the tech for the user or buyer, and capabilities that embed the product in a network of co-existing and adjacent products.  [here]

Here is the core distinction between Product & Offering:

Thick versus Thin Tech Offerings

Not all new products can easily fit a commercial purpose and deliver the value from their core technology phenomenon. Some products are hard to implement while others may be easy to deploy and activate. So, we can make a simple distinction between thin and thick offerings.

Thin offerings are those where the tech products are easy to fit to the customer’s purpose. They are especially easy to adapt for early users.

In contrast, thick offerings are those where technology needs a high level of customization, integration and deployment services to realize customer value. 

Loose versus Tight Tech Coupling

Typically, the product that captures a nascent tech phenomenon matures along a trajectory which requires constant updates to the product.  Today this is the case with gen-AI and large language models. So, a key factor affecting the value of a product is the relative dependence or “coupling” between the changes to the core technology and the efforts to deploy it. This coupling can be either loose or tight.

A loose coupling is where the changes to the core product do not affect the offerings that have been built upon it to serve customers; whereas a tight coupling is when changes to the underlying tech break the offerings that have been previously built, requiring significant reformulation to deliver value.  

Growth Pathway Tradeoffs

These two factors in turn affect the growth pathways (GTM) and product roadmaps you have to pursue in the market. 

Broadly, for thin offerings, it is best to pursue product-led growth tied to users, while for thick offerings, it is best to grow through account-led pathways focused on buyers. 

Also for thin offerings, it is best to pursue a product roadmap that maximizes user reach either directly or through product-led channel strategies of embedding in other products or through product partners.  

For thick offerings, the product roadmap should focus on building the minimal catalog of offerings that maximize reach and retention with an account. And, it may be best to have service partners build and sell these offerings. 

These four quadrants give you different GTM and roadmap strategies. Let’s look at two of these patterns to align the promise of the product to the customer problem.

Alignment Pattern: Product-Led Inside-Out Growth

If your offerings are thin and loosely coupled to the product, you have the freedom to create and try out multiple offerings directed at different user segments. Since product led growth (PLG) is lower in capital investment, it is the best GTM path to identify an early adopter user segment. The bold promise of the product roadmap should be anchored on the burning problem that the ideal users have to address in their job-to-be-done. This gives maximum optionality to a tech company to find paths to such users by testing multiple offerings. Other GTM paths like product-led channels are also interesting since it allows the thin offerings to be embedded in a product ecosystem, or even become a platform to be built upon by other product partners. [details]  

Pros: Maximum tech value creation & optionality

Cons: Risk becoming commodity as tech trajectory matures

Most top products growing through PLG have this pattern. Slack, Calendly, Dropbox, Docusign, Figma etc.

Alignment Pattern: Account-Led Outside-In Growth

If your offerings are thick and tightly coupled to the product, you should follow an outside-in approach of first finding a buyer segment that is open to such offerings. Typically, an account-led approach can afford more service-heavy products and result in higher LTV. Product companies building thick offerings with new technologies like gen-AI, streaming data analytics etc. should begin with the burning problem for a buyer segment at key accounts. They can go deep in the account with offerings that are customized for delivering on a bold promise. The underlying trajectory of the fast moving tech becomes less important than the roadmap of the offering that remains anchored to customer value.  Account led GTM followed by account led channels approach (sell to, through and with service partners) becomes key to expansion and retention. 

Pros: Maximum customer value captured

Cons: Risk becoming services company or too tied to an early customer segment

Most complex technologies deployed in regulated industries or large enterprises have this pattern (cloud computing, analytics, security, AI/ML models) . 

Implications for Product Market Fit

While exploring PMF, it is important to begin with a hypothesis of where your product offering and GTM tradeoff lies. 

1. Be clear about the tech phenomenon your product harnesses

2. Understand the core tech trajectory of this phenomena 

3. Explore the possible burning problems your tech product can solve

4. Determine how thick|thin and loose|tight your offerings will have to be, based on the problems you are trying to solve

5. Select the right pattern of GTM and roadmap based on your product-offering tradeoff. 

6. Iterate in market until you have problem-to-promise alignment

Implications for Product Market Scale

Once you have PMF, you can use this framework to determine pathways to scale. 

If you began on product-led alignment path, 

1. Double down on your product roadmap to increase your tech trajectory and optionality 

2. Deepen your product reach through product-led channel approaches

3. On top of your PLG, add an account-led GTM motion, orchestrating between the two. (This motion is also called product-led sales). 

4. Over time, “offer-ize your product”: create a select few thick & tightly coupled higher service offerings for top accounts - either directly or through service partners.

If you began on account-led alignment path, 

5. Double down on your top few offerings roadmap to increase customer value capture 

6. Deepen your account reach through account-led service partnerships approaches

7. Inside your account-led GTM motion, add product led growth by targeting users within key accounts.

8. Over time, “productize your offerings”: create a thinner|loosely coupled tech product roadmap to enter new or adjacent customer segments - either directly or through tech partners. 

References

Elements of a Tech Brand Story - by Anurag Wadehra

Key Customer Relationships for Tech Offerings

Why Bridging Across Growth Pathways Is Hard

A Pattern For Account Led Growth Relationships

A Pattern For Product Led Growth Relationships

Patterns For Channel Led Growth Relationships

Please share with tech founders who may benefit from this; or they can email me directly at aw@anuragwadehra.com

This course is for the tech founders & CEOs who are serious about speeding up their PMF plans to improve their chances of success, especially in this economic climate. 

It was gratifying to see that founders from different domains (hardware, regulatory compliance, and traditional SaaS) found the course valuable.

I cover several topics like customer archetypes, company story, product positioning, category point of view, go-to-market strategies and finding the right talent. 

The testimonials have also been encouraging. 

“Dramatically helped with every aspect of our company, and subsequently, with fundraising.” Nawar Alsafar, Founder - Bytez, AI research discovery company

“As a super busy founder, it can be challenging to carve out the time for deep thinking strategy and flow work. This class was a mechanism for me to carve out that time -- both during class sessions and later in the week to review material. This was invaluable.” Hannah Sieber, Founder, ArtyC, a cold-chain shipping company

“Not many founders have gone thru this much rigor in PMF. They absolutely need to.” Navneet Singh, Co-Founder & CEO - StepFunction, Customer SaaS company

Check out the course here. https://maven.com/anurag-wadehra/productmarketfit

This long post is inspired by a few ideas and books about human insight, psychology and behavior that I’ve returned to a few times. Each set of ideas offers an interesting framework which I've applied to the topic of how to attract customers. This interpretation is mine, even if the original ideas are from different domains. The books and authors are listed at the end of the post. 

There are only two ways to attract customers. 

One, to have them want your product. 

Two, to have them need your product. 

The first way is based on evoking desire among customers. 

The second way is based on persuading customers.

Most businesses use both ways to attract customers. 

Even if used together, each way is different in how it affects a customer. 

So, each way represents a different type of attraction game.  

An attraction game includes all tactics that pull customers through their journey, over time: from branding, promotion, sales to training, renewal and support.  

Let’s call these two types of attraction games:

Desire Games and Persuasion Games. 

Desire games evoke desire for what's missing in your customers’ lives without your product. 

Persuasion games persuade them of what’s possible in their lives with your product. 

Desire games create motivation, e.g. fear of missing out.

Persuasion games drive action, e.g. an offer.

Desire games help customers aspire to a vision.

Persuasion games help a customer progress towards it.

Desire games are social. 

Persuasion games are rhetorical.

Desire games speak to a person’s subconscious mind. 

Persuasion games speak to their conscious mind. 

Game Purpose

The initial purpose of each game is to attract a customer.

The ultimate purpose of each game is to keep the customer.

Both games attract and retain customers over time. 

But in different ways.

Desire games sustain the want.

Persuasion games deliver on the need.

Game Models 

Each game has a different underlying model of human dynamics.  

“I Want It, Because Others Want It”

Mimetic or imitation-based desire is rooted in social insight: humans don’t desire things independently and autonomously, they desire things socially–they desire things because other people want them, too. [Rene Girard] 

“I Need It, Because I’m Convinced of It” 

Rhetoric is the skillful and combined use of logic (logos), credibility (ethos), and shared values (ethos) to convince others. [Aristotle]

Game Engines

They turn attention into new wants. 

Consumer brands in entertainment, fashion, apparel, travel and food advertise on Facebook, Instagram and TikTok to build desire.  Business brands use LinkedIn to build prestige to attract talent. 

They turn intent into fulfilled needs. 

Consumer brands use Google paid ads, Amazon marketplace to fulfill consumer needs (purchases). Business brands have industry specific B2B marketplaces for customer fulfillment. 

Game Plays

Customers may not be aware that they are part of a desire or persuasion game.  

If they become aware, they may opt out or limit their role in the games. For instance, many consumers limit their social media consumption to control their wants, or give up loyalty cards to limit their shopping. 

Most customers, however, continue to participate in the games. They may do so consciously or for reasons hidden even to them. 

Desire games are harder to detect, limit or opt out of than persuasion games. That is because we hide certain desires and motivations even from ourselves. 

Desires are not bad, per se. There are two types of desires: thin desires that drive destructive cycles of seeking what others have; and thick desires that drive creative cycles based on our values that fulfill us over time. [Wanting, by Luke Burgis]. 

In desire games, often the product is only a small part of the fulfillment of desire. The want is stronger than the product or its experience. 

Therefore, strong consumer brands survive beyond their product life cycles and limitations. Example: Nike, Harley Davidson, Porsche, Coke. 

If these top brands were to disappear, their customers’ desire would be thwarted, but they could fulfill their need for a shoe, bike, car or soda drink elsewhere. 

Persuasion games are more common, especially in the business world, though they are not necessarily easier to build or play. This is because rhetorical skills may be easier to detect but are hard to resist or deflect. 

Businesses use a mix of time tested rhetorical capabilities towards customers for this game, such as building reasoned value propositions, creating trusted relationships, signaling credibility and evoking shared values, and so on. 

Businesses that are excellent at building need-based products are likely to excel at persuasion games. 

Amazon is an ideal example of this with their obsession on fulfilling customer’s daily needs, from discovery to same day delivery. Google paid ads are another example fulfilling our daily information needs from searching to finding critical services. 

Technology is fickle in creating desire. While many users are attracted by novelty, this is mostly variety seeking behavior, similar to trying a new food flavor. Most technologies do not last beyond the novelty stage. Therefore, tech companies with novel products often end up playing persuasion games.  

That’s why most tech products become obsolete once their utility is no longer needed, or get subsumed within other products serving similar or higher utility. If Amazon were to disappear, its customers would not be able to fulfill their needs, but they would not long for it. 

Top Players: Apple and Tesla

While every business plays both games, most excel at only one of them. But, in this generation, there are two brands that have mastered both the desire and persuasion games: Apple and Tesla.

Both companies have built novel tech products (phones and electric cars) that millions covet; and they have also convinced millions of their needs. This is no small feat. To do so, each of them have built their own desire and persuasion game engines. 

Apple has built an symbolic brand that evokes desire, controls its product experience (from apps, devices to chips), owns an app ecosystem and runs direct retail and ecommerce channels. 

Tesla has built a symbolic brand with virtually no advertising, controls its product experience (from software to batteries and factories), owns its sales and configuration platform, and runs a direct showroom and services network.

Attraction Games: Influence, Morality and Society 

Game Influence Levers

There are many levers to psychologically influence customers during the attraction games. The seven most common levers are: social proof, liking, scarcity, reciprocation, consistency and commitment, authority and unity. [Influence, by Robert Cialdini]. 

While all of them can be used in both the games, some levers are better suited to one game over the other. 

Social proof: The social proof of other “customers like you” using a product is a strong motivator for you to do so. For business customers, public references from other similar customers are credible evidence of the value of a product or service. In fact, private back-channel endorsements are perhaps the most powerful social proof of a product, or even a person.     

Liking: Social media influencers build desire among their followers through likes. In business situations, the likeability of a sales person is a major factor in customers wanting to work with a business. 

Scarcity: Flash sales and limited editions create consumer want through scarcity, real or perceived. In business, pre-orders, backlogs and time-bound offers can create a sense of desire through perceived scarcity of the product.   

Unity: Often, brands signal their unity with consumer’s beliefs and identities to build a connection with them. It is a powerful but tricky tactic since consumers may question whether such signaling is real or performative.  

Consistency & commitment: To build trust, businesses need to steadily deliver on their commitments over time. This is the foundation tactic of all persuasion games. Promises made, promises kept. 

Reciprocation: In business, sales builds relationships with customers through slow and steady increase of reciprocal exchanges and expectations. All consumer loyalty rewards depend on this tactic. 

Authority: Expertise and credentialing can persuade customers even when other tactics are limited. So, most ventures begin with some form of authority, real or claimed, to persuade others. 

Influencing Consumers versus Business

Companies attract both consumers and business customers with the desire and persuasion games, using all levers of sociable, relational and personal influence.

But how each game is played out in the public and private domains differs for consumers and business customers.  

Desire Games: Social media influencers, brand ambassadors, endorsement celebrities - all model in public what the consumers should desire. Even scarcity tactics like flash sales, premium concert tickets, are mostly public. This is to achieve the largest number of followers who covet the brand. 

Persuasion Games: Tactics like special offers, loyalty discounts are getting personalized and therefore more private for each consumer. This is to achieve the most effective way to convert each prospect into purchase. 

Persuasion Game: To avoid collusion in the marketplace, companies need to make public as much as possible their product claims, value propositions and pricing. In competitive situations, companies need to deliver written bids and proposals to a business customer that are “public”, though confidential, within customer teams. As the buyers evaluate vendors, tactics like special offers, discounts are contractually reviewed and recorded. In short, a company needs to share their persuasive rhetoric within the customer organization for the buyers to make their purchase decisions and to defend them to the future auditors.  

Desire Game: The personal desires of the business customers are often private, if not outright hidden. Customers covet a promotion, seek to attend an important conference, want to access influential business leaders, and generally raise their prestige among peers. In most cases, companies attract customers by offering to help them fulfill these desires. Tickets to sports games and concerts are given out in private. But all industries have legal and ethical limits to such private desire games. 

Game Shortcomings 

Each attraction game has its own structural shortcomings. 

As is evident from recent studies of social media, cycles of negative desire and addiction (“doom scrolling”) can generate consumer anxiety, FOMO and depression. In its extreme form, this desire can amplify group tribalism and scapegoat outsiders. Product fans can not just support their own cohorts but gleefully tear down competitors. From the Mac versus PC crowd of the last generation to the crypto crowd today [Bitcoin maximalists vs. others], strong communities can fall prey to internal divisions due to unwavering commitment to their group’s fervent desire. In business settings, companies can fulfill their customers' personal desires and gains through bribes or illegal quid-pro-quo.

Persuasive stories fall apart over time due to cognitive biases and errors. The cognitive bias codex classifies four categories of such errors: 

1. Dealing with too much information: results in biases to confirm our beliefs, to notice bizarre changes, find faults with others over self, etc.  

2. Not finding enough meaning: creates fake patterns for conspiracy  theories, stereotyping, etc.

3. Need to act fast: means we act with overconfidence and search for simple solutions, etc.

4. How we remember: results in misattributions, false memories and distorted recall.  

Businesses use these shortcomings in their games wittingly or unwittingly. 

And just as often, we fall for them on our own. 

Either way, when we do we are furious about it. 

Game Morality 

As customers, we are all complicit in these games. We are eager to be seduced by the desire games, and ready to act from the persuasion games.  We fool ourselves all the time - unwittingly or willingly. 

Businesses may play the game poorly or unethically. Just as customers may be complicit explicitly or fool ourselves unknowingly. 

While the immediate purpose of the games is to attract the customers, the deeper purpose is to deliver on the legitimate promise of the attraction. Every game that attracts a customer sets the expectation to fulfill their desire or need over time - in order to keep the customer.  

In an ethical business, what the customer is attracted to (the promise) and what the customer experiences (the product) should be aligned. 

This gap between promise to product may be trivial or profound. Yet the game’s legitimacy depends on this. Closer the product is to its promise, more likely the customers will consider the game legitimate - and more complicit they will be in the game. 

If there is a significant gap between the promise and the product, customers see through the games as disingenuous or even fraudulent. We hate to be fooled by others.

Example: customers get mad when they see a corny ad for a beauty cream or meet a pushy used car sales person. “That’s just marketing or sales bullshit”. They don’t want to be complicit in these games.

Even when a business game is legitimate (such as social media apps like TikTok & Instagram), customers may become complicit in our failure. Doom scrolling is an example of where we fool ourselves.   

Examples of games with huge gaps between their promise and product:

Game Choice & Control 

Otherwise, stopping these games requires control, if not coercion, of either the business or its customers. Society sets controls on these games. 

These controls may be legal (legislative, regulatory) or social (boycott, censure), etc. Examples: Laws against advertising cigarettes; parents preventing kids on social media. 

The purpose of controlling the two games is different. 

Example: Cities are banning sweet, spicy and minty flavors of vapes because teenagers are more likely to be attracted to them. Governments are looking to regulate social media to limit its harmful effects on consumers' desires. 

Example: Pharma industry’s drug claims are closely regulated, and their sales tactics with doctors curtailed. Similarly, the advertising industry is restricted in how consumer’s sensitive information is captured and used.  

For socially taboo categories (porn, drugs, alcohol), attraction games are entirely forbidden or controlled to limit both the desire and the means to fulfill them. 

Ultimately, every society sets different norms for businesses to control their attraction games.  

Games in Society at Large

So far, we have talked about attraction games in the business context. 

What if the product is not a commercial offering, but a non-profit service (a religious charity), a government program (carbon tax policy), or even a social idea (universal basic income)? 

Every day, various government, civic, media and nonprofit institutions bombard us with games to entice us to their ideas or even ideologies. Structurally, the two attraction games are still the same - make the citizens either want or need a specific policy, program or vision.  

There is one categorical difference though. 

In such games, the final purpose (the agenda) is often unclear to citizens or hidden from them, even when the initial stated purpose is clear. So, there is always a gap between the promise and outcome, between the ends and means. Often, what is proposed is not what is delivered. And therefore, citizens are eternally questioning and contesting these games.  

Example: a non-profit may attract donors with a desire to reduce poverty, but the program impact may not be measurable for years. A tax policy may be persuasive initially, but its unintended consequence may not be visible for years.  

Conversely, we may fool ourselves that we are supporting a non-profit for its cause while we may seek only our name on the donor list to signal wealth, generosity or compassion.  

A Word of Caution 

Politicians seduce us to their visions of the future with desire games. 

Bureaucrats convince us of their policies with persuasion games. 

Big business does both to protect its vested interests. 

Social activists and NGOs pursue their games with hidden motives. 

Just like we would opt out from illegitimate business games, as customers;  we should guard against games of false visions [wants] and rich promises [needs], as citizens.  We should question our own willingness to identify with these games. 

We should be careful of institutional intentions, and our complicity in these games. And if the games turn disingenuous or fraudulent, it is our civic duty to dissent, disengage or dissuade others from the power of these games. 

REFERENCES

This post was inspired by the following books & their ideas. 

Finite and Infinite Games

The main inspiration is the short but stunning book Finite & Infinite Games - James Carse. 

"There are at least two kinds of games: finite and infinite. A finite game is played for the purpose of winning, an infinite game for the purpose of continuing the play.”

Mimetic Desires

Luke Burgis: Wanting: The Power of Mimetic Desire in Everyday Life

An excellent book on the ideas of Rene Gerard.

As discovered by Rene Gerard, the social insight is: humans don’t desire things independently and autonomously, they desire things socially–they desire things because other people want them, too.  Mimetic Theory 

Influence 

A classic on how we get influenced.

Influence - Robert Cialdini

Influence at Work

Self Deception

The Elephant in the Brain: Hidden Motives in Everyday Life

“The main thesis of the book is that we are very often not aware of our real reasons for most of our behaviors. Our behaviors are optimized for living in a social group and very often, from the point of view of natural selection, it is useful if we are not consciously aware of our real motivations.”

The Elephant in the Brain - Wikipedia

Rhetoric and Biases

The Rhetorical Triangle: Understanding and Using Logos, Ethos, and Pathos

Cognitive bias codex

Please share with others who may benefit from this; or they can email me directly at aw@anuragwadehra.com

In this course, I cover several topics like customer archetypes, company story, product positioning, category point of view, go-to-market strategies and finding the right talent. 

I’m gratified by the ratings from the two cohorts of tech founders and CEOs: 

The testimonials have also been encouraging. 

“Clarity, confidence, and discipline in how to execute quickly and reach PMF.” Noemi Kis, Founder & CEO - Monittor

“Not many founders have gone thru this much rigor in PMF. They absolutely need to.” Navneet Singh, Co-Founder & CEO - StepFunction

“Dramatically helped with every aspect of our company, and subsequently, with fundraising.” Nawar Alsafar, Founder - Bytez

As you know, I have written about these topics in this newsletter for several years. And now, I am looking to teach them to a wider audience through this course.

Check out the course here. https://maven.com/anurag-wadehra/productmarketfit

This is Part 2 in a series on Customer Loyalty

Part 1 Who Are Your Champions?

In my earlier post Who Are Your Champions, I described the dynamics of building the loyalty pyramid among your Buyers and Users. In this post, I explore why software builders (such as developers, data engineers and analysts) are a very different breed of user than either business or tech users. Therefore, tech companies need to build loyalty among the builders in a different way than among their buyers and users. 

Building Software Gets Easier 

In my customer quartet framework I define two types of users - business or tech.  In the B2B world, business users such as employees, partners and vendors are the most common. For decades now, every business function from sales to manufacturing, and every business process such as order to cash and every business problem from detecting defects to predicting churn has had some software product or solution developed for it. 

To serve these business users, an IT department of a company has many tech users - like engineers, security & sys admins, architects - who operate, manage and support these internally developed or commercially purchased software systems.

Historically, building these software solutions was expensive and onerous. Large companies like banks and telcos made huge investments to build custom solutions through an army of developers in IT service shops. This gave rise to the IT services industry in India that employs over 4 million people today. In the last decade, though, SaaS companies have developed  applications for every niche enterprise function and are beginning to replace the legacy custom solutions. And the cloud native companies from AirBnB to Netflix have proven the power of software by building modern large scale software systems to reshape entire industries. 

With the rise of cloud computing, data and AI technologies, building software has become easier than ever. No-code platforms are transforming every job to be done by business users.

These new products are a novel combination of apps, AI, data, devices and sensors built on foundation platforms (cloud computing AWS), foundation models (OpenAI) and open source software libraries (GitHub).  Today, even a novice can generate decent code using text prompts in ChatGPT. 

Building software is fast becoming as common as reasoning and writing.

Builders are a Unique Type of Users 

For many tech companies, this has given rise to a large class of Users who are the modern day makers of software for and within an enterprise.

They use the major public cloud platforms, thousands of software development tools, and hundreds of open source software libraries, and dozens of AI models to build and run apps, fetch and stitch data, create and operate services - all to enable digital experiences for their business users.

These builders serve the other two types of users. They serve the business user directly by building software and digital experiences for them to improve their job to be done. And they serve the tech user by building software tools to help them accelerate their internal software development cycle, which adds new digital capabilities to further enable business users. 

In summary, developers either enable business jobs to be done directly or provide software tools for other tech users to do so. 

Builders Create Relationship Equity Differently from Other Users 

[In this section, I build on the excellent Orbit Model of community engagement, developed by Patrick Woods and Josh Dzielak. I apply their model to the pyramid framework to compare and contrast builder loyalty with end-user and buyer focused loyalty.]

Most software tool companies now target builders as part of their go-to-market planning since it offers a bottom-up, product led growth pathway to creating value for a customer.  [More on Product Led Growth]. 

However, tech companies build relationships differently with software builders like developers, data engineers and scientists. These developer relationships (DevRel) are very different from the relationships with end users of software in product-led growth or those with buyers in sales-led growth. 

Developers, or builders in general, are passionate about the tools they work with. How they discover, play and adopt new tools is different from business users seeking to get a job done or a tech user working with a tool.

Developers detest “marketing'' because they are not persuaded by “the promise” of a product. Since the concept of purchase funnel is not relevant, the conventional marketing tactics don’t work or backfire with developers.  

The Orbit Model defines this social capital as a combination of gravity (what attracts the members to a community) and love (what deepens their involvement). Community capital is the bedrock on which to build builder loyalty. 

Builder Goal: To Build Better Through Community

Why is the builder's goal and journey different? 

Building software has always been hard, but it is especially difficult to build scalable software that serves millions of customers. As an analogy, the 20th century saw manufacturing go from artisan products to mass customization with engineering focus on efficiency, speed and scale. The factories became more critical than the artifacts they were producing.

Even today, Elon Musk's take on the Tesla car is: "You want to have a good product to build, but that's basically the easy part. The factory is the hard part."

This is now becoming true for information goods as software shifts to the mass customization era with public cloud platforms and open source software stacks. [For excellent discussion, see Flow Framework by Mik Kersten]. 

Today, developers within a company have two software ''factories” or ecosystems to build upon. One, the employer’s internal tools and development platforms, standardized and maintained by the IT department. Second, the external open source communities [GitHub, GitLab, etc]. 

Not just code, but data is following the same path. With the explosion of data and AI/ML, data engineers and scientists now have a large set of tools available to support the data lifecycle. To build new software apps and data products, these builders navigate both external and internal ecosystems to become productive and efficient. 

So, if you want the builders to use your new software product (an API, an SDK, a cloud service, etc.), you need to ensure it is adopted by the customer’s software “factory” and the community. Without doubt, the community is more important.

Builders face the choice of thousands of tools and techniques. They turn to the community to explore, find, learn and use the right tools for their tasks. Everything from sample code, documents, APIs, library, test cases etc. are supported by the community. 

This is consequential - builders often become more loyal to the community than their employer. Their personal growth and future employment may depend on their status and credibility in the community. Especially with the gig economy, developers seek communities around fast growing technologies to stay productive in the market - way before companies adopt them. The recent flurry of developer excitement around ChatGPT and generative AI is a case in point. 

Therefore, to create champions among builders, you need to engage them in their communities.  

The Orbit Model is an excellent example of how you need to engage the builders in their community.  Your focus should not be on transactions like purchase or downloads but on enabling the community to connect with your tool. You need to help builders in their journey to explore, participate and contribute in the community with your tool to build something of value. 

For Champion Builders, Build Love for Your Product in Their Community

We can look at the Orbit engagement model from a cohort lens: How do builders progress from observers to become ambassadors (i.e. champions)? 

Observers: They might not need your product yet. Your dev rel or community manager can help educate them with quality product content and tutorials. The focus is on building and nurturing connections with them. The critical metric is Time to First Interaction when an observer begins to engage with the community around your product. 

Users: For this cohort, dev rel and community managers focus on increasing interactions among the users and other community members. The focus shifts to customer support and deeper product campaigns and channels. The critical metric is Time to Hello World or a similar metric that captures time a user takes for the first trial of your product. 

Fans: This is the cohort of users who are willing to share their product expertise with others in the community. The focus of dev rel and community managers is to help fans contribute to code, build hackathons and engage in community and social channels. The critical timing metric is Time to First Build or a similar metric that captures time a fan takes to build something new with your product. 

Many communities have lots of users, maybe even fans, but they do not take takeoff. They lack the ambassadors (champions) among them who can coordinate and lead the community. This separates developer-led companies from others. 

Ambassadors (Champions) -  This small group becomes not only your product advocate but also coordinates major social and building efforts around your product. These users become your evangelists or VIPs of your community with their own personal brands and status. They need to be supported by your product, engineers, and customer support to keep building community love. The critical timing metric is Time to VIP. 

In Summary: Builder Loyalty Differs from Buyers & Users

This chart shows how the builder loyalty differs from that of buyers and users.

In summary, builder loyalty rests on community love, and is developed through evangelists and other expert builders in the community. Therefore it is critical to keep the community engaged with a roadmap relevant to their needs and initiatives that help the community build and grow.  

References 

• Orbit Model: Why it is better than funnel 

• Orbit Model - Framework for building communities

This is Part 2 in the Customer Loyalty series

Part 1: Who Are Your Champions?

This is a month-long bootcamp based on an approach to PMF I've developed called the Five Factor Framework where I systematically breakdown the factors - Customer, Product, Category, Go-To-Market and Talent - and show how they interact and shape PMF. 

This framework addresses the most common challenges I discussed in my webinar: Finding PMF: Five Traps to Avoid. 

I tested and developed this approach in December to positive reviews by attendees. 

The average recommendation score was 9.3 on 10. 

Founders’ quotes; “This course helped me stitch everything together.” “The most important part - it brought clarity” “Helped me understand how PMF looks like in real-world” “Sharp questions led to deeper insights”

This PMF BootCamp goes deeper than the previous course. I’ve incorporated the Seven Lessons From Tech Founders from the previous session.

The bootcamp is a cohort-based course spanning 5 weeks, and 6 sessions with more worksheets and hands-on practice among peers, guided by me. 

Please share with others who may benefit from this. 

Many thanks to avi@weavr.cafe and kirsty@weavr.cafe for partnering with me on building out the PMF BootCamp. 

Last month, when I gave a webinar to tech founders on the five traps to avoid in finding product market fit (PMF) in the U.S. I was delighted by the engagement and hunger for actionable insights on this topic by the SaaSBoomi founder community. In that talk I had laid out a framework of five factors that affect PMF: customer, product, category, go-to-market strategy and talent.  Based on the positive response from the community, I decided to develop a course. 

The Product Market Fit (PMF) Course 

Last week, I completed a 2-week course on Speeding Up Product Market Fit where I worked with a cohort of tech founders to further develop this framework into hands-on worksheets and guided discussions: all with the goal of building and accelerating their PMF plans.  

I am blown away by the work the tech founders put in, and by their commitment to the PMF journey. And I am glad they found the course valuable. 

Seven Lessons

Here are my top seven lessons from leading this cohort-based course.

1. Finding PMF is a complex problem. 

There are no standard answers, no fixed solutions: only heuristics. Sure, there are several principles, such as finding a powerful use case for early customers, or iterating fast on your MVP (minimum viable product), but these are hard to apply.  

2. Founders need a structured approach. 

Most first time founders are lost in how to navigate product market fit from first principles. Do you go for revenue or repeatability of customers? Do you build growth bottom up or top down? Even serial entrepreneurs, who are wiser, approach PMF with trepidation, especially in newer markets.  

3. A structured approach offers a map, a navigation guide - and a lot of questions.  

I found that all the five factors, both the external ones (customer, category, GTM) and the internal ones (product and talent) need to be taken into account. Importantly, the approach should be built on two premises: one, how these factors interact in the real world and two, how to guide founders through these factors with a set of tools that address gaps in their knowledge and the market risks they face. Throughout, the focus has to be on the right questions, not answers. 

4. Founders still need to do the heavy work, on their own or with the cohort. 

Since there are no ready answers, the founders have to do the work to break down their hidden assumptions, and build back a PMF plan that is grounded in reality. Here, a cohort of like-minded founders going through similar challenges can help each other get better with their PMF plans.  

5. Biggest benefit: strategic clarity for the founders and their teams. 

Coming out of the program, founders need to have a coherent value story, and a concise view of their key risks and unknowns. Such as, should they double down on a customer segment? Or should they redefine their product offering? And what is the key talent needed to achieve PMF? This clarity should drive focused actions to validate PMF in the market, which is the highest point of leverage for any founder. 

6. Innovation is guided, not hampered, by this process. 

Using a structured approach frees the founders to focus on innovation in areas that get them close to PMF. E.g. is my pricing wrong? Or is my growth pathway misaligned? Questions point to different areas to innovate in. 

7. This approach can be applied to most tech domains. 

We had a cohort of founders building companies in various domains (sales and marketing, ecommerce, security), using multiple business models (SaaS to marketplace) in different software categories (apps, business platform, and infra tools).  Based across Bangalore, Tel Aviv and San Francisco, they found common ground in helping each other improve their PMF plans. I found this the most encouraging. 

Announcing PMF Bootcamp

Based on all this, I am launching an expanded 4-week intensive course in February 2023

Product Market Fit Bootcamp: A Market Based Structured Approach to Building & Testing Your PMF Plan.

This is for the founders who are looking to go deep and reconstruct their PMF plans to achieve faster time to success, especially in this economic climate. 

If you know tech founders who could benefit from this, please share with them. 

To join the waitlist, contact me. 

Special shout out and many thanks to Avi Harsh avi@weavr.cafe and Kirsty Hall kirsty@weavr.cafe of WeavrCafe for partnering with me on this course and on incubating a cohort-based community. 

Webinar: Finding Product Market Fit - Five Traps to Avoid

Recently, I gave a talk to India-based tech founders who are looking to enter the U.S. market. The talk was hosted live by the SaaSBoomi community. 

Introducing the Online Course: Speeding Up Product-Market Fit

Based on positive feedback, I am launching a 2-week cohort-based online course in December for tech founders/leaders on Speeding up Product Market Fit - A Structured Approach.

The course will help tech leaders break down the key success factors to finding PMF and help them speed up the time to reach growth stage.

Join or share with others.

Below are the talking points from the webinar. Check out the video for full discussion.

What is PMF in B2B, Anyway?

Product Market Fit (PMF) is a complicated topic for first time entrepreneurs. In B2B markets, even at the basic level, “product market fit” can mean many things: is it product-buyer fit? product-user fit? product-category fit? When we say, product do we mean MVP (minimum viable product) or the whole product offering, including pricing? 

What are we trying to fit? Against what?

Five Common Traps

There are five common traps that founders fall into as they assess PMF. 

These are:

1. Unclear category

2. Fuzzy customer profile

3. Incomplete product offering

4. Unproven go-to-market

5. Hopeful hiring

To avoid these traps, I define five factors of PMF, each requiring a different type of fit. 

These five factors are:

1. Market to Category Fit

2. Category to Customer Fit

3. Customer to Offering Fit

4. Offering to Growth Fit

5. Growth to Talent Fit

I ask twenty simple, but not easy, questions to check for PMF across each of these factors. These questions can help answer gaps. 

Twenty Simple (Not Easy) Questions

Market to Category Fit

1. What is your target market?

2. What’s your category? Existing or new?

3. What’s your market-category hypothesis?

4. What’s your insight to win in this market & category?

Category to Customer Fit

1. Who are your early adopter accounts in the U.S.?

2. Who’s your buyer champion? 

3. Who’s your ideal user in an account? 

4. What’s your value promise to each? 

Customer to Offering Fit

1. What’s your whole product offering (pricing, deployment, support, etc.)? 

2. How’s that different from others?

3. How does it deliver buyer value?

4. How does it deliver user value?

Offering to Growth Fit

1. What’s your GTM hypothesis? 

2. What’s your unit economics?

3. Which growth paths do you need? Product, Account or Channel Led? In what order?

4. How do you validate each?

Growth to Talent Fit

1. What talent do you need in the U.S.? Role in PMF?  

2. How are you building the local team? Top down, bottom up?

3. What will attract them?

4. How will you support them? 

Finding PMF Requires Iterating Across All Five Factors

Companies need to iterate across all of these factors to achieve PMF. Ideally, they need to do so simultaneously to ensure they are not falling into a trap. 

For bonus content on how to transition from PMF to Growth stage, check out the video.

Introducing 2-week cohort based PMF course.

I’m launching a 2-week, cohort-based online introductory course for tech founders/leaders.

Please join or share with others who may be interested.