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The author provides a great practical workshop format for aligning stakeholders around an MVP definition. However, use this method with caution. The framework relies heavily on stakeholder opinions and prioritisation exercises, and I’ve sat in enough of those to know they can lead to biased outcomes. For best results, ensure you incorporate plenty of user research, data, and customer insights into the workshop to guide decision-making.
Key Messages
Lean Inception is a ThoughtWorks process designed to help you define an MVP and drive alignment amongst stakeholders. It doesn't replace the need for other research activities.
An MVP is the simplest version of a product that can validate the key business assumptions. It helps us understand if it's worth building further versions of the product. An MVP helps us understand if we're directionally correct.
Taking an MVP approach will get you to market quickly and cheaply and give you the opportunity to incorporate user feedback into the product development process sooner. All of which should increase the speed at which you're creating real value.
Your initial MVP is just the beginning. The magic happens in the increments and iterations that follow. Think of MVPs asevolutionary creation. Short development cycles and cheap experimentation to validate key hypotheses. Each future incrementtests a small part of the general hypothesis each time. In turn, this makes sure each aspect of the product is validated.
An MVP should provide a full slice of end-to-end value. It should be valuable, usable and feasible. You should also include something that creates an impression, a 'Wow!' moment. An MVP doesn't mean low quality.
Lean Inception 1 Week Structure:
- Monday: Kickoff; Product Vision; 'Product Is/Isn't Does/Doesn't'
- Tuesday: Describe Personas; Features Brainstorm
- Wednesday: 'Technical, UX & Business Review'; User Journeys
- Thursday: Feature Sequencer; MVP Canvas
- Friday: Showcase
Lean Inception Workshop Activities
Write the product vision. Split into a few groups and complete a vision sentence template. Then work together to create a sentence that makes sense.
- Template: For [Users] Who Have [Problem that needs to be solved] the [Name of Product] is a [Product Category] that [key benefits, reasons to buy] and unlike our [alternatives/competition] we're different because… [key difference].
The Product Is / Is Not. The Product Does / Does not. Explain the product by spending equal time defining what it does and doesn't do, what it is and what it isn't. You can do this by placing post-its on a 2-by-2.
Clearing the Objective. Each team member states their understanding of the 3 most important objectives for the product. Group for similarity on a canvas. Rewrite the objectives as a group.
Understanding trade-offs. Create and document key aspects that may need to be traded off (simplicity, security etc). Get folks to rank them and display their preferences. Then work together to build an agreed trade-off rank. Details here.
Describe the personas. Split into groups to create persona templates (Name, Profile, Behavior, Needs). Then present them back. Change groups and repeat until personas emerge. Create an empathy map for each persona (Think, Hear, See, Say, Pain, Gain). Then update the persona descriptions.
Feature Brainstorming. Describe features as simply as possible (e.g. print invoice). Focus feature creation around persona goals. Ask the team to state and prioritize the user goals (as columns) and personas (as rows). Limit the number of goals and personas (if we could only cater to a couple of these, what would they be?). Then add features that are necessary for the users to reach the goals. You can prioritize further by getting folks to vote or place tokens on important functionality.
Technical, user experience and Business Review. Assess each feature in turn, in terms of effort to build it, business value and user experience. Rank them on a scale of 1 to 3. Then assess how much uncertainty you have for each, in two dimensions (confidence in what to do, confidence in how to do it). You can combine these into a single measure of uncertainty. You must tackle the cards with high uncertainty - by dropping them, redefining them or clarifying them.
Show the users' journeys. Select a persona, identify a goal of theirs. Decide the starting point and describe each of the steps on a post-it note until they achieve their goal. Overlay the features onto the journey. Expect to identify missing features, and have features that don't support any important journeys.
Sequence the features. Keep asking which one of these two is a priority. Then ask what is the minimum combination of features that can be available to validate a small set of hypotheses of the business. Plan a sequence of waves. Make sure each wave has only a small number of features, no more than one that's highly uncertain, limit total effort scores, make sure each wave is valuable and if one feature is dependent on another that must have come in the wave before. Number your MVPs (could be a combination of waves).
Calculating Time Effort and Cost. If you need to, break features into tasks so they can be T-shirt sized individually.
Build the MVP Canvas. It should include:
- MVP Proposal - What is the proposal?
- Segmented personas - Who is it for? Can we segment and test this MVP in a smaller group?
- Journeys - What journeys are going to be improved?
- Features - What are we building? Which actions are going to be simplified or improved?
- Expected Outcome - What learning or result are we seeking?
- Metrics to validate the business hypothesis - How we'll measure the results of this MVP
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Quick Links
John Cutler targets Marty Cagan’s purist approach · Article
Future of Work with AI Agents · Paper
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The Product Operating Model explained · Article
Common probability errors to avoid · Article
k-ANONYMITY: A MODEL FOR PROTECTING PRIVACY
Latanya Sweeney. 2002. (View Paper → )
Consider a data holder, such as a hospital or a bank, that has a privately held collection of person-specific, field structured data. Suppose the data holder wants to share a version of the data with researchers. How can a data holder release a version of its private data with scientific guarantees that the individuals who are the subjects of the data cannot be re-identified while the data remain practically useful? The solution provided in this paper includes a formal protection model named k-anonymity and a set of accompanying policies for deployment. A release provides k-anonymity protection if the information for each person contained in the release cannot be distinguished from at least k-1 individuals whose information also appears in the release. This paper also examines re-identification attacks that can be realized on releases that adhere to k-anonymity unless accompanying policies are respected. The k-anonymity protection model is important because it forms the basis on which the real-world systems known as Datafly, µ-Argus and k-Similar provide guarantees of privacy protection.
We need medical healthcare data for research. Anonymising it is hard. This paper is more than 20 years old but it teaches an important lesson. Re-identification is a real threat - especially when second or third datasets can be combined. We needed to develop stronger techniques. Today newer techniques, including differential privacy, are often preferred for more robust privacy guarantees, especially in high-dimensional datasets and complex use cases.
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Book Highlights
The process is a continuous cycle comprising four key steps: (1) data analysis and insight gathering; (2) idea generation; (3) experiment prioritisation; and (4) running the experiments, and then circles back to the analyse step to review results and decide the next steps. Sean Ellis and Morgan Brown · Hacking Growth
To understand your situation, reflect on the following questions: What’s your ideal customer profile? What kind of experience do you aim to deliver? What’s your ultimate success metric? What defines your quality standards? What’s your unfair advantage? How do you ensure progress? David Pereira · Untrapping Product Teams
The persistence of underestimation of costs and over-optimistic assumptions about performance raises questions about why industry has not been able to adjust its expectations over the years. One simple answer is that industries don’t think; people do. Richard Rumelt · The Crux
Publicly available data from similar products or solutions can help define your users and engagement targets. Nir Eyal · Hooked
Choice architects (such as Carolyn the cafeteria director) have many opportunities to choose defaults, and they can do so in ways that are self-serving or welfare enhancing Cass R Sunstein and Richard H Thaler · Nudge
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Quotes & Tweets
Design your routine. Don’t negotiate with yourself. Unknown
Remembering that I’ll be dead soon is the most important tool I’ve ever encountered to help me make the big choices in life. Because almost everything - all external expectations, all pride, all fear of embarrassment or failure - these things just fall away in the face of death, leaving only what is truly important. Remembering that you are going to die is the best way I know to avoid the trap of thinking you have something to lose. You are already naked. There is no reason not to follow your heart. Steve Jobs