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From an idea to a Minimum Viable Product

From an idea to a Minimum Viable Product

In this deck I am presenting the following:

1. A quick introduction to the notion of the MVP – what a Minimum Viable Product is, why you need, and why it is a critical success factor for startups
2. The process – how to move from a problem to a properly-defined MVP - steps, activity and best practices to follow
3. Hints and best practices on how to prototype in a rapid mode!

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George krasadakis

February 19, 2019
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Transcript

  1. In a startup context George Krasadakis Feb 2019 Photo by

    Hal Gatewood on Unsplash
  2. The structure of this session Background The MVP and why

    it is critical for a startup 1 From an idea to an MVP Steps to follow to properly define your MVP 2 Rapid prototyping Techniques to help you experiment and capture feedback 3
  3. From a problem to an MVP Problem Idea(s) Concept(s) Prototype(s)

    MVP A learning process via prototyping, experimentation & feedback loops Problem statement Users involved Stakeholders Market scan Possible competitors Failed attempts Ideas – one pagers State-of-the-art Competition Solutions – one pagers Wireframes Users and personas Product Architecture Technology Architecture Feasibility & cost estimates Realistic UX Technical description Exit criteria Feedback summary Product Backlog Product Roadmap Tech architecture Market strategy Feedback mechanisms Experiments
  4. The product management function Problem Ideas Concepts Product Management Function

    Prototype MVP MVP +1 MVP +n Product Backlog … Targets Planning Insights KPIs User Feedback Priorities Ideas Inflow: User feedback, telemetry Outflow: New releases, new features
  5. Product Management is critical for startups 75 percent of venture-backed

    startups fail1 1 FastCompany, "Why Most Venture Backed Companies Fail," Harvard Business School -Shikhar Ghosh. 1. Startups have extremely limited resources 2. They are ‘driven by passion’ 3. They have little or no structure The product risk: To build something nobody wants or poorly build a product with great demand
  6. Source: https://www.cbinsights.com/research/startup-failure-reasons-top/ Why do Startups fail? It’s the product!

  7. Why do Startups fail? My own list of failure reasons!

    1. Over-engineered products Even if the MVP is properly defined, the engineering work become far more sophisticated than needed; this leads to waste of energy and resources – with huge opportunity cost. Engineering-heavy teams need to be aware of this risk and follow a lean, agile approach. 2. Ignore or mis-interpret user feedback Startups may ignore the signals from their userbase; or confirmation bias may responsible for reading only the ‘compatible’ patterns; this is where predefined Success criteria – specific metrics and KPIs could make a difference. 3. MVP – they just don’t get it They don’t get the notion of the MVP and, as a result, they fail to focus and set the right priorities
  8. Why do Startups fail? It’s the product! Make sure you

    have the right product management skills in your team!
  9. The MVP 1. The definition of the MVP 2. Popular

    misconceptions regarding the MVP 3. Why a good MVP is critical for startups 4. Characteristics of a good MVP 5. Signs of a poor MVP 1
  10. But what is an MVP anyway? “In product development, the

    minimum viable product (MVP) is a product with just enough features to satisfy early customers, and to provide feedback for future development” — Minimum_viable_product Ries, Eric (August 3, 2009)
  11. But what is an MVP anyway? “In product development, the

    minimum viable product (MVP) is a product with just enough features to satisfy early customers, and to provide feedback for future development” — Minimum_viable_product Ries, Eric (August 3, 2009)
  12. But what is an MVP anyway? “In product development, the

    minimum viable product (MVP) is a product with just enough features to satisfy early customers, and to provide feedback for future development” — Minimum_viable_product Ries, Eric (August 3, 2009)
  13. But what is an MVP anyway? “In product development, the

    minimum viable product (MVP) is a product with just enough features to satisfy early customers, and to provide feedback for future development” — Minimum_viable_product Ries, Eric (August 3, 2009)
  14. Frequent misconceptions about MVP People confuse the MVP with the

    Prototype People confuse the MVP with the Proof of Concept People think of the MVP as ‘just something to start with’ People think of the MVP as a ‘quick and dirty’ product
  15. With a proper MVP you will be able to: Think

    Big, but start small, iterate fast Build your product with less Test your product with real users, faster Go to market faster Pivot, earlier
  16. A good MVP … Focuses on the user Reflects tested

    user needs Has great feedback loops Solves the core problem
  17. A bad MVP … Is over-engineered or not engineered :)

    Is not aligned with user needs Does not enable user feedback loops Is over-complicated or oversimplified
  18. None
  19. The Problem Statement Make sure you don’t solve the wrong

    problem ☺ Describe the problem you are solving with a solid problem statement: ”… a concise description of an issue to be addressed or a condition to be improved upon. It identifies the gap between the current (problem) state and desired (goal) state of a process or product https://en.wikipedia.org/wiki/Problem_statement
  20. Validate the Problem Is it really a problem worth solving?

    1. Who are the key-users – the ones impacted by this problem? 2. What are the pain-points you are trying to eliminate? 3. Did you validate your problem statement with your team, your stakeholders and selected users – does it reflect the real problem?
  21. Articulate your solution Describe in a single page: 1. The

    context – the situation 2. How your product solves the problem? 3. Start describing your personas 4. How you address the major pain points for your users? 5. Think big at this stage – describe your product vision 6. State your assumptions
  22. Identify your users Who are you solving for? 1. List

    all different classes of users –who will benefit from your solution? 2. Document your users, their needs, their pain points 3. Describe the ideal scenarios/ experience for each class of users 4. Collect metadata for your users – anything that could be correlated with needs, expectations, point of view 5. Define named personas
  23. Understand your users Who the users are vs what the

    users need 1. Construct user profiles and personas; use empathy 2. Interview users – capture signals, pain points, expectations 3. Analyse available studies and metadata – public domain 4. Validate your problem with selected users 5. Validate your solution with selected users
  24. Define your product Think as a user: define your product

    with user stories 1. Describe product features <as a user> 2. Apply empathy – use what you know for your users/ personas and try to express their needs and the desired user experience 3. Think Big – write Epic user stories 4. Think Small – its OK to write user stories at the lowest level of detail 5. Don’t bother about feasibility and priorities at this stage
  25. Define your MVP Post-process your user stories; rank them; get

    your MVP 1. Your product backlog should have all the user stories/ product features you can think of 2. Process each user story to estimate [a] its expected value for the user/ its importance in solving the problem and [b] its feasibility 3. For each story, you can combine these estimates into a single score 4. When all your stories have a score, rank them to reflect the priority
  26. Define Success You need a solid definition of success …

    to get there 1. At this point you have a prioritized product backlog; you need to describe what ‘success will look like’ 2. Identify the key metrics which will be used to measure success 3. Combine the metrics to the right KPIs 4. Prepare your data capturing mechanisms to support your metrics 5. Design a single ‘product performance dashboard’ as your source of truth
  27. Problem Ideas Concepts Product Management Function Prototype MVP MVP +1

    MVP +n Product Backlog … Targets Planning Insights KPIs User Feedback Priorities Ideas You are here How can you get there… faster?
  28. None
  29. The Prototype Defined Types of prototypes 1. Static prototypes –

    wireframes could serve the purpose in certain cases 2. Clickable prototypes – approximating the experience but with no real back-end and data services 3. Functional prototypes – but under numerous assumptions and conventions; they can look realistic enough to support real user interaction scenarios
  30. Rapid prototyping techniques Why build a prototype? 1. To get

    a realistic, functional instance of your product, really fast 2. Expose it to selected users and capture feedback 3. Test certain aspects of your product – the ones which have high uncertainty and/ or implementation cost 4. Test certain technologies or experiences which might be new to end- users – for example voice-driven interactions
  31. Prototype ≠ MVP MVP 1. Minimum but Production ready and

    real product 2. Secure and Reliable 3. Accessible by all users 4. Integrated with real data services Prototype 1. Does not address production requirements 2. Security/ Reliability not concerns (static/ limited security risks) 3. Accessible by limited number of users only 4. Reusing existing components and artificial data and static content vs
  32. How to speed up your prototyping Build only what needs

    to be tested 1. Set the right focus – do not build ‘conventional features’ 2. Find the features with higher uncertainty 3. Define an overall experience by combine all ‘static’ features and those built for the prototype
  33. How to speed up your prototyping Use static data; reuse

    existing components 1. Don’t spend time building real data models and data stores; 2. Quickly design your key entities as static JSON files 3. Expose them via a simple APIs and you have a realistic integration scenario
  34. How to speed up your prototyping Use existing, 3rd party

    services 1. Even for advanced AI scenarios there are ready to use commercial APIs to quickly integrate and use 2. Even if you plan to build your own AI algorithm, you should be able to approximate your results with existing commercial services 3. For all of your key scenarios – search what is already out there in terms of APIs and use it!
  35. How to speed up your prototyping Use prototyping tools 1.

    There are great prototyping tools out there – especially for designing UI/UX for web and mobile devices 2. There are great prototyping tools even for VR/AR experiences 3. Scan the market, select the right tools for you and use them for quick, static or clickable prototypes
  36. How to speed up your prototyping Make assumptions, move fast!

    1. When prototyping you have to deal with uncertainty, fast! 2. When you do not have all the answers, just make assumptions; just make sure you will go back to validate them as you learn about the problem and your users 3. Maintain simple, to-the-point documentation on the objectives, assumptions and success criteria of the rapid prototyping effort; share it with your team and your key stakeholders
  37. How to speed up your prototyping Rethink Quality 1. Quality

    is great – but you have to put it in the right context 2. You are not building a production system – even if the prototype is hugely successful, chances are that you will through away the code 3. Focus on the user experience; back end processes could be hard-coded, based on static, artificial data and the overall experience supported by just a script
  38. How to speed up your prototyping Define exit criteria 1.

    A prototype is a kind of experiment/ test, to enable you to validate a concept and learn 2. You need to define the key questions and the specific points your are ‘testing’. 3. Document the definition of success and exit criteria; and what you are hoping to get out of the prototype, upfront.
  39. How to speed up your prototyping Build, capture feedback, iterate

    fast! 1. Build a basic UX – wireframes or real UI 2. Connect static data to make it realistic 3. Present it in the right context with a story – the right flow 4. Capture feedback 5. Iterated as needed; but fast!
  40. How to speed up your prototyping Use UI libraries &

    templates 1. There are great resources online – from web page templates, mobile apps, images and videos – even public data sets which could make sense in your scenario; use them! 2. If you plan to prototype frequently, build your own, internal library of resources 3. If you have UI/UX experts in your team, consider setting up a set of reusable UI elements and resources to speed up UI/UX development
  41. How to speed up your prototyping Use DevOps, Automation, Monitoring

    1. Normally you need to host your prototype – so get ready in terms of hosting scenarios and DevOps 2. Assuming a large group of users to expose your prototype to, you need an effective way to capture feedback – via the prototype and/or with online tools 3. You might need to setup monitoring processes to summarize user engagement and interaction, during the prototyping phase
  42. How to speed up your prototyping Set the right expectations

    1. Make sure that your key-stakeholders understand what a prototype is and have the right expectations 2. Make sure your users get the full context when they are asked to interact with the prototype 3. Make sure that you get honest, objective feedback from your users and stakeholders; summarize and communicate appropriately the feedback and insights
  43. Talking about feedback … Did you find this useful? I

    would appreciate your feedback and thoughts! Scan the QR code or use this link https://goo.gl/j8L7uw to submit your thoughts, questions or suggestions. Video version: https://www.youtube.com/watch?v=Buy8Ki-P0T8
  44. Building data-driven and AI-powered products; leading technology innovation programmes; 17+

    US patents on Artificial Intelligence, Analytics and IoT • 20 years of digital product development – from concept to launch • 80+ innovative, data-driven projects • 10 multinational corporations • 3 technology startups • Founder of ‘Datamine decision support systems’ g.krasadakis@gmail.com https://medium.com/@gkrasadakis