Overview

The Incubation phase is a critical stage in the innovation journey, focused on rigorously testing hypotheses and validating the business model in the market to achieve Product-Market-Fit (PMF). This phase prioritizes learning over building, testing over perfecting, and evidence over opinion in all decision-making, recognizing that initial concepts, even if user-validated, still contain numerous untested assumptions regarding market dynamics, business model viability, and technical feasibility. It leverages an integrated approach combining Design Thinking, Lean Startup, and Agile Engineering methodologies.

By the end of this phase, the expected outputs include:

• Defined and tested hypotheses.
• Defined and tested coded or non-coded Minimum Viable Product(s) (MVPs).
• Documented observations and learning cards.
• A developed product vision and Objectives and Key Results (OKRs).
• A refined business model based on validated learning. The product should be ready to scale.

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Step 1: Assumptions | Hypotheses | Risk Prioritization

Before diving into experimentation, it’s crucial to solidify your understanding of underlying assumptions:

• Recap Problem Definition: Revisit the desirability assumptions from Discovery. This involves clearly articulating:

  • The problem your customer wants to solve (e.g., what they struggle with or needs they want to fulfill).
  • Why your customer cannot solve this problem today (e.g., obstacles preventing them).
  • The outcome your customer wants to achieve (e.g., qualitative/quantitative outcomes in their life). If you’re starting directly in the Incubation phase, it’s highly recommended to go through this desirability assumption mapping exercise first.

• Prioritizing Assumptions:

  • Identify “Leap of Faith Assumptions” – these are assumptions that are both important and unknown, meaning if they prove false, they could cause your product or service to fail. These are the riskiest assumptions and should be the primary focus of your efforts and energy in experimentation.
  • Use an Assumption Risk Register to systematically track these key initiatives and their associated risks, linking them to Desirability, Viability, and Feasibility categories.

• Translate Assumptions into Hypotheses:

  • Convert your assumptions into falsifiable hypotheses that can be rigorously tested. A falsifiable hypothesis is one you can prove wrong, which helps in delivering clear positive or negative signals and overcoming cognitive biases.
  • A common hypothesis format is: “We believe that [specific user segment] has a need to [user need/problem] because [insight/evidence]. We will know this is true when we see [specific measurable outcome/behavior]“. This format helps quantify expected outcomes, even if not perfectly precise.
  • Ensure your hypotheses align with your prioritized assumption risks, especially your “Leap of Faith Assumptions,” which should have the highest risk scores.

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Step 2: Minimal Viable Product (MVP)

The Minimum Viable Product (MVP) is a core concept for validating hypotheses and learning rapidly:

• What is an MVP?

  • Eric Ries defines it as “that version of a new product which allows a team to collect the maximum amount of validated learning about customers with the least effort”. Its goal is to begin the process of learning, not end it. It eliminates any additional work beyond what is required to start learning, considering it waste.
  • Steve Blank describes an MVP as the minimally scoped product that gets some job done for your chosen customer archetype.
  • Dan Olsen defines it as “the minimum amount of functionality that your target customer considers viable, that is, providing enough value”. He emphasizes gaining faster learning with fewer resources by testing hypotheses before building the full product. Olsen also notes that an MVP is “what gets you the most learning about a specific part of the Business (or Mission) Model Canvas”. He often refers to experiments as “MVP Tests” to distinguish them from a fully developed product.
  • The Project Management Institute (PMI) views an MVP as an “investment in learning, an experiment where your goal is to explore what a potential customer wants”.
  • In Lean UX, an MVP is a “small and fast way of learning something,” which might be a drawing, a landing page, or a prototype, with the primary concern being to create learning, not necessarily value.
  • The concept highlights that “Minimum Viable Product is not a Product but a Process“.
  • VMware Pivotal Labs emphasizes that an MVP “contains the smallest possible feature set that delivers a valuable, end-to-end experience” and “is of sufficient quality that it appears finished” and “has been validated with users”.

• MVP Philosophy:

  • The primary goal of an MVP is to maximize learning while minimizing risk and investment.
  • It represents the right-sized product – “big enough to cause adoption, satisfaction, and sales, but not so big as to be bloated and risky”.
  • The focus is squarely on validated learning, which enables you to de-risk the desirability, viability, and feasibility of your idea before committing to a full build and launch.

• Types of MVPs: MVPs can take various forms, from no-code solutions to physical prototypes:

  • No-Code/Low-Code MVPs:
    • Pre-order/Crowdfunding: Takes real payment before you build. A pre-order or deposit is a payment, so it shows willingness to pay; a pledge or sign-up without payment is an interest signal only.
    • Landing Pages: Simple webpages describing the product or service to test interest and gather customer information (e.g., email sign-ups, pre-orders).
    • Concierge MVP: The service is delivered manually to early customers, allowing direct interaction and shaping the service based on real-time feedback. A famous example is Food on the Table.
    • Wizard of Oz MVP: The front-end appears to be automated, but human operators perform the tasks behind the scenes, allowing testing of demand for automated solutions or interaction flows without full automation. Zappos is a classic example.
    • Email/Survey Campaigns: Used to test messaging and gather initial feedback.
  • Design-based MVPs:
    • Sketches: Initial concept exploration, often on paper or whiteboards, great for team iteration.
    • Wireframes: Low-to-medium fidelity digital representations focusing on layout and flow, often grayscale to prevent focus on visual design. Can be clickable to simulate user journeys.
    • Mockups: Higher fidelity static visual designs with colors, fonts, and images, resembling the final product but not necessarily functional.
    • Interactive Prototypes: Clickable user journeys, allowing testing of user experience before coding. These can include intentionally “missing functionality” to test specific hypotheses and settle debates without building.
    • High-Fidelity Prototypes: Near-final visual design, highly interactive.
    • Live Prototypes: Basic functional implementations, stress-testing solutions in real-world conditions.
  • Other MVP Types:
    • Explainer Video MVP: A video that demonstrates the intended product as if it already exists, common on crowdfunding platforms to gauge viability.
    • Fake-Door MVP: Adds a non-functional UX trigger in an existing product to measure customer interest in a proposed feature by tracking clicks.
    • Mechanical Turk MVP: Simulates complex features (like AI) with a manual backend to test interaction and user expectations.

• MVP Feature Identification: Before building, identify the minimum functionality needed for validation:

  • Product Landscape Audits: Benchmarking competitor products provides insights into “table stakes” features, user feedback, and usage statistics.
  • Revisit Concept Sheets, Solution Sketches, and Idea Templates from Discovery’s ideation work, as they likely already contain initial thoughts on minimum functionality.
  • Kano Model: Use this to rank potential features by benefits (e.g., Performance, Must-have, Delighters) and select the top 1-3 features per benefit for the MVP.
  • Value Proposition and Jobs-to-be-Done: Map customer needs (derived from Jobs-to-be-Done and Value Proposition Canvas) to features, ensuring the MVP addresses core unmet needs.
  • MoSCoW Approach: Categorize features as Must Have, Should Have, Could Have, and Won’t Have to narrow focus and channel efforts on essential elements.

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Step 3: Experimentation Process | Testing Plan | Go-to-Market | Service Blueprint

This step focuses on systematically testing hypotheses and planning for market introduction.

• The Lean Experimentation Process:

  • The core is the Hypothesize-Design-Test-Learn loop. This systematic framework, derived from the Lean Startup’s Build-Measure-Learn process, emphasizes starting with a falsifiable hypothesis, designing a minimum viable experiment, executing the test to gather data (both qualitative and quantitative), and then learning from the results to make informed decisions.
  • About Experiments: The objective is to gain empirical evidence that what you intend to create will actually deliver value. A good experiment is precise, replicable, and generates usable data.
  • Components of an Experiment: Each experiment should clearly define its Hypothesis, the Test (who is involved, how it will be conducted, cost), the Metrics (what will be measured), and the Criteria (what constitutes success or failure).
  • Types of Tests: Experiments can be categorized as:
    • Product MVP Tests (Qualitative): Focus on customer value from the actual product. Examples include Sketches, Wireframes, Mockups, Interactive Prototypes, Wizard of Oz MVPs, Concierge MVPs, and Live Products. Moderated testing is recommended for early validation to gather rich customer feedback.
    • Marketing MVP Tests (Quantitative): Focus on market demand and messaging. Examples include Ad Campaigns, Marketing A/B Tests, and Crowdfunding.
    • Tests can also be classified as Discovery Experiments (to determine general direction and gather initial insights) or Validation Experiments (to test specific hypotheses).
  • Develop a Test Script: For moderated testing, create a script outlining the design artifacts to show, tasks to ask the customer to perform, and specific questions to ask.
  • Identify Metrics and Criteria: For each hypothesis, define what data will be measured and what success looks like based on those metrics.

• Document the Experiment Plan:

  • Test Cards: For each hypothesis (starting with the riskiest), complete a Test Card outlining the experiment. You might create several for a critical hypothesis.
  • Rank Test Cards: Prioritize experiments based on their learning value, cost, and time required. The initial goal is to run tests that yield the most learning with the smallest investment of time and resources. The aim is to reduce uncertainty as much as possible before building anything substantial.
  • Kanban Board: Visualize the progress of experiments through stages like To Do, Design (build something to test), Test (measure), Learn, and Done.
  • Experiment Timeline: Develop an overarching timeline for all experiments, especially if conducting testing in waves. This helps keep the team focused on completing experiments and gathering insights.

• Go-to-Market (GTM) Strategy and Plan: This describes how your product will be sold to customers and should be planned alongside experimentation. Elements of the GTM strategy can be tested during experiments. It’s closely linked to the Business Model Canvas.

  • 4 Ps Framework: This popular GTM framework includes:
    • Product: Maps to the Value Proposition.
    • Price: Maps to Cost Structure and Revenue Streams.
    • Place: Maps to Channels, Customer Segments, and Customer Relationships.
    • Promotion: Maps to Customer Relationships and Key Partners.
  • Key Preparation Aspects: Define objectives (revenue, user acquisition), identify target audience (personas), conduct competitive analysis, determine product positioning, decide pricing strategy, and craft compelling messaging.
  • Execution and Launch: Includes internal training, soft launch/beta testing, marketing campaigns, sales kickoff, and ensuring customer support readiness.
  • Post-Launch Evaluation: Monitor Key Performance Indicators (KPIs), gather continuous customer feedback, and be prepared to adjust and iterate for long-term success.

• Service Blueprint and Operations:

  • A service blueprint is a visual diagram that maps customer interaction points with the product along their journey, aligning them with product features and the underlying operational systems.
  • It helps to analyze and align touchpoints to create a positive customer experience.
  • The blueprint provides a big-picture perspective, showing how a set of features fits into the overall user journey and how other systems or products might connect.
  • It includes mapping product features to each touchpoint vertically and keeping technical stacks at three levels: front-end UI, back-end processes, and data layer. This helps visualize the operational support needed for new offerings.

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Step 4: Run Experiments | Gather User Feedback | Document Observations

Execution and learning are central to this step.

• Run Experiments: Move through the iterative cycle, continuously testing your assumptions. At each decision stage, rely on your defined metrics and criteria to decide whether to:

  • Kill: If evidence strongly disproves a fundamental hypothesis, killing the idea is a success, as it saves resources from pursuing a non-viable path.
  • Pivot: This means changing strategy without changing the core vision, often based on invalidated hypotheses. Types of pivots include zoom-in/out, customer segment, value proposition, platform, business architecture, value capture, engine of growth, channel, or technology. Humility in accepting empirical truth is key.
  • Persevere: Continue testing the next most important hypothesis on your prioritized list.
  • Update your Business Model Canvas as you validate, refute, or adjust your hypotheses. A “Progress Board” can visualize how experimentation ties back to these updates.

• Gather Evidence:

  • Information collected during testing is considered evidence, which can be data from an experiment or collected in the field (e.g., quotes, behaviors, conversion rates).
  • Document this evidence, along with insights and decisions, on Learning Cards for each associated hypothesis/test.

• User Feedback Collection and Analysis: Comprehensive feedback is gathered across multiple dimensions:

  • Feedback Dimensions: Focus on Functionality (benefits, features), User Experience (UX) (ease of use, friction points, emotional experience), and Messaging (clarity of value proposition, brand resonance). Also, gather feedback on Usability and evidence for Product-Market-Fit.
  • Collection Methods:
    • Direct Engagement: Conduct User Interviews and Usability Testing. For early testing, “Roman user testing” (unfussy, direct with users in their context) is effective, focusing on building rapport, asking non-direct questions, being a “fly-on-the-wall,” and taking detailed notes for immediate debriefing. Avoid leading questions.
    • Behavioral Data: Leverage Analytics, A/B Testing, and Usage Metrics.
  • Synthesis and Iteration:
    • Organize feedback using grids or categories like feature set, UX design, and messaging.
    • Track positive and negative feedback.
    • Use quantifiable scores (e.g., value, ease-of-use) to track progress.
    • Identify top negative feedback to prioritize and react to in subsequent iterations.
    • Iterate on designs and features based on feedback, conducting waves of testing until problems are resolved and positive feedback increases, demonstrating progress towards product-market fit.

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Step 5: MVP Build | Product Team | Roadmaps

With validated hypotheses, the focus shifts to building and planning for future development.

• Using Agile to Build a Working MVP:

  • Agile development is essential for incrementally building and refining a working MVP once hypotheses are validated. Features are built bit by bit, with rapid, short, continuous cycles of delivery, customer feedback, and iteration.

• Product Team:

  • Success relies on a balanced product team. This team typically consists of:
    • Product Design: Focuses on desirability and user experience, acting as the “voice of the user”. They practice design thinking and derive insights from user research.
    • Product Management: Focuses on viability and Lean principles.
    • Agile Software Engineering: Focuses on feasibility and the technical solution.
  • This multi-disciplinary structure is crucial for making rapid and informed decisions.

• Prioritized Features and Roadmap:

  • After testing and validation, eliminate any features from your MVP that do not contribute to the core value proposition.
  • Create roadmaps for future releases based on these prioritized features. Common types include Story Maps, Product Roadmaps, and Outcome-Based Roadmaps.
  • Outcome-Based Roadmaps are particularly useful, as they prioritize product outcomes with defined metrics for success, rather than specific features by fixed dates. This approach optimizes for continuous learning and responsiveness to evolving customer needs.
  • User Stories: Transform validated hypotheses into development user stories. These should be prioritized based on learning objectives and user value, with clear acceptance criteria derived from testing requirements.

• Continuous Learning and Iteration:

  • Product designers often work a few weeks “ahead” of engineers, designing new features or determining the next build.
  • This involves identifying the next outcome on the roadmap, using MVP feedback to plan necessary iterations or pivots, and monitoring the backlog of prioritized user problems. This cyclical process embodies continuous validation.

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Step 6: Metrics | Learning to Product Market Fit

Measuring progress and achieving product-market fit are key outcomes of the Incubation phase.

• Product-Market Fit (PMF): This is conceptualized as climbing a mountain; the higher you ascend, the greater your product-market fit. The process aims for continuous ascent, but sometimes a pivot is necessary to find the right path.
• Key Metrics and Measurement:
  • Utilize both quantitative (e.g., analytics, A/B testing, Net Promoter Score) and qualitative (e.g., usability testing, user interviews) methods to track progress.
  • The Net Promoter Score (NPS) is a useful measure of customer satisfaction. It is supporting evidence of product-market fit, never proof on its own: retention and repeat payment lead. It should rise over time as the product improves.
  • Innovation Accounting focuses on measuring progress, setting milestones, and prioritizing work specifically for new ventures, allowing objective assessment of learning milestones.
  • The Investment Readiness Level (IRL) is an analog to NASA’s Technology Readiness Level (TRL). It provides a common, simple, and visual language to communicate the maturity and readiness status of a startup or innovation project. The IRL helps management make evidence-based funding decisions by demonstrating a project’s progress in validating its business/mission model.
• Customer-Guided Development: When user research is conducted effectively and feedback is genuinely embraced, customers will guide the product development process, effectively pulling the market in the right direction. This means that you don’t need to be perfectly right from the start; the goal is to get your ideas out there and test them to learn and adapt.
• Learning Organization: The entire process contributes to building a learning organization with systematic learning and adaptation capabilities. This fosters an innovation culture that embraces experimentation and calculated risk-taking, viewing even “failure” as a valuable learning opportunity.

What is Product-Market-Fit?

Product Market Fit (PMF) is a fundamental concept for startup success, although there is not always a single, universally agreed-upon definition of what it entails.

Here’s a breakdown of what Product Market Fit means

Core Definitions of Product Market Fit

• Marc Andreessen’s Definition: Product/market fit means being in a good market with a product that can satisfy that market. A “good market” is characterized by having a large number of real potential customers, where the market itself “pulls” the product out of the startup. This means the product almost sells itself, leading to intense growth that can even strain the organization.
• Common Contemporary Usage: In practice, PMF often refers to having built something that a particular group of users genuinely loves, regardless of how small that initial market might be. However, a more complete understanding emphasizes “product/great market fit,” where a “great market” is defined by its substantial size, the intensity of the pain points it experiences, and its immediate willingness to buy a solution.
• Achieving the Right Balance: An important aspect of PMF is having the right product at the right time. It signifies that your product satisfies a strong market demand by effectively solving a specific problem or need.

How Product Market Fit Feels (or Doesn’t)

Marc Andreessen vividly describes the feeling of having (or lacking) PMF:

• When Product Market Fit is Happening:
  • Customers are buying and using the product as fast as you can produce it.
  • Money from customers is piling up in your company’s accounts.
  • The company is rapidly hiring sales and customer support staff.
  • Reporters are calling due to the product’s buzz.
  • There is more demand than needed, and progress feels effortless, like a “boulder starting to roll down the hill”.
• When Product Market Fit is Not Happening:
  • Customers are not quite getting value from the product.
  • Word-of-mouth is not spreading.
  • Usage is not growing fast.
  • Press reviews are unenthusiastic, and the sales cycle is long, with many deals failing to close.

The Process of Achieving Product Market Fit

Achieving PMF is an iterative journey, often compared to “climbing a mountain” where higher elevation means more PMF. It involves systematic processes to reduce risk and uncertainty:

• The Lean Product Process: Dan Olsen’s framework outlines six steps, starting from the bottom of a “Product Market Fit Pyramid” and building upwards:
  1. Target Customer: Clearly define whose life you’re trying to improve and whose pain points you’re addressing. This involves peeling back layers of generalization (e.g., from “Millennials” to “Millennials who want to cook at home but are too busy”).
  2. Underserved Needs: Identify the specific needs or problems of your target customer that are not well-served by existing solutions. This emphasizes the “problem space” over the “solution space”.
  3. Value Proposition: Define what your product promises to do for customers and how it will meet their needs better than alternatives.
  4. Feature Set: Determine the specific functionalities that deliver on the value proposition. This is where the concept of a Minimum Viable Product (MVP) comes into play to avoid over-building.
  5. User Experience (UX): Design how users will interact with the product to access its functionality and benefits.
  6. Testing with Customers: Close the loop by testing your UX design (often through prototypes) with customers to assess product market fit.
• Iterative Cycles: The process involves continuous “Hypothesize, Design, Test, Learn” loops. You form assumptions, design tests, measure results, learn from feedback, and revise your hypotheses.
• MVP for Learning: An MVP is defined as what gets you the most learning about a specific part of the business model at any given time, not necessarily a smaller or cheaper final product. Its primary goal is to create learning rather than immediate value. MVPs can range from simple landing pages and explainer videos to wireframes and interactive prototypes.
• Pivoting: If testing reveals that initial assumptions are flawed, a “pivot” (a substantive change to the business model, like shifting the target customer or value proposition) might be necessary to find a better market opportunity.
• Focus on Problem-Solution Fit: Before PMF, the focus is on achieving “problem-solution fit,” which means having evidence that customers care about certain jobs, pains, and gains, and that your value proposition addresses them. PMF then builds on this by showing that the value proposition gains market traction.

Indicators and Measurement of Product Market Fit

• Retention and repeat payment (leading evidence): Flattening retention curves, showing customers keep returning over time, and customers who keep paying or buy again are the leading signs of fit.
• The 40% Rule (Sean Ellis Test), supporting evidence only: 40% or more of surveyed customers saying they would be “very disappointed” if they could no longer use the product supports a fit claim. This is an Exponential Scout rule of thumb. A survey answer is a statement, not behavior.
• Customer Engagement and Usage: High levels of engagement and growing usage are strong qualitative and quantitative indicators.
• Word-of-Mouth and Referrals: Customers actively recommending the product is a supporting sign of fit.
• Depth of Customer Insight: Thorough documentation of customer understanding is also an indicator.
• Testing Entire Business Model: Unlike Design Thinking which focuses on product or service, Lean Startup emphasizes testing assumptions about the entire business model to achieve PMF, including desirability, viability, and feasibility.

Post-Product Market Fit

Once Product Market Fit is achieved, the strategic focus shifts significantly:

• The primary objective becomes driving exponential growth and establishing a sustainable market position. This involves scaling proven business models through growth optimization, market expansion, and operational excellence.
• Product-Led Growth (PLG): Strategies where user acquisition, expansion, conversion, and retention are primarily driven by the product itself become central, assuming PMF has been established. This includes optimizing for “Time to Value” (TTV) to ensure new users quickly experience the product’s benefits.
• The next step after finding PMF is to find a repeatable and scalable sales process.