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Show Me the MVP - Digital PM Summit 2016 Natalie Warnert

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Show Me the MVP - Digital PM Summit 2016 Natalie Warnert

  1. 1. @nataliewarnert #dpm2016 Show me the MVP!Natalie Warnert – #dpm2016
  2. 2. @nataliewarnert #dpm2016
  3. 3. @nataliewarnert #dpm2016 Natalie Warnert Agile Coach & Trainer www.nataliewarnert.com @nataliewarnert
  4. 4. @nataliewarnert #dpm2016 A Minimum Viable Product is that version of a new product which allows a team to collect the maximum amount of validated learning about customers with the least effort. – Eric Ries, Lean Startup ”
  5. 5. @nataliewarnert #dpm2016 What is a Minimum Viable Product? • Building just enough to learn and validate a hypothesis • Learning not optimizing • Find a plan that works before running out of resources • Provide enough value to justify charging THE MVP
  6. 6. @nataliewarnert #dpm2016 What is Lean UX? • Interaction design • Constant measurement and learning loops • Business, development, customer, and UX unification • Less focused on deliverables, more on data LEAN USER EXPERIENCE
  7. 7. @nataliewarnert #dpm2016 *The difference between BUILDING the right thing and LEARNING the right thing THEPOINT
  8. 8. @nataliewarnert #dpm2016 Problem/Solution fit Do I have a problem worth solving? Product Market Fit Have I built something people want? Scale How do I accelerate growth and maximize learning? WHERE TO START
  9. 9. @nataliewarnert #dpm2016 Problem/Solution fit Do I have a problem worth solving? Product Market Fit Have I built something people want? Scale How do I accelerate growth and maximize learning? WHERE TO START Ideas are cheap! Acting on them is expensive $$
  10. 10. @nataliewarnert #dpm2016 Problem/Solution fit Do I have a problem worth solving? Product Market Fit Have I built something people want? Scale How do I accelerate growth and maximize learning? WHERE TO START Ideas are cheap! Acting on them is expensive $$ Learning over growth Don’t scale
  11. 11. @nataliewarnert #dpm2016 Customers don’t care about your solution. They care about their problems. - Dave McClure, 500 Startups ”
  12. 12. @nataliewarnert #dpm2016 UNDERSTAND THE PROBLEM • What is the customer’s problem? • Fit into the business model
  13. 13. @nataliewarnert #dpm2016 UNDERSTAND THE PROBLEM • What is the customer’s problem? • Fit into the business model
  14. 14. @nataliewarnert #dpm2016 UNDERSTAND THE PROBLEM • What is the customer’s problem? • Fit into the business model What is the customer hiring your product to do?
  15. 15. @nataliewarnert #dpm2016 DEFINE THE SOLUTION Smallest possible solution to speed up learning Build only what is needed (MVP) Pick bold outcomes to validate learning Business outcomes over solution
  16. 16. @nataliewarnert #dpm2016 DEFINE THE SOLUTION Smallest possible solution to speed up learning Build only what is needed (MVP) Pick bold outcomes to validate learning Business outcomes over solution More relevant product recommendation + in-cart experience = increased
  17. 17. @nataliewarnert #dpm2016 most Plan A’s don’t work
  18. 18. @nataliewarnert #dpm2016 VALIDATE QUALITATIVELY What are our customers doing? Continuous feedback loop with customers Get out of the building!
  19. 19. @nataliewarnert #dpm2016 VALIDATE QUALITATIVELY What are our customers doing? Continuous feedback loop with customers Get out of the building – or get customers in the building!
  20. 20. @nataliewarnert #dpm2016 A startup can focus on only one metric. So you have to decide what that is and ignore everything else – Noah Kagan, ”
  21. 21. @nataliewarnert #dpm2016 VALIDATE QUANTITATIVELY Pirate metrics Acquisition Activation Retention Revenue Referral
  22. 22. @nataliewarnert #dpm2016 VALIDATE QUANTITATIVELY Pirate metrics Acquisition Activation Retention Revenue Referral What is your one metric to rule them all? What are you trying to validate (hypothesis)
  23. 23. @nataliewarnert #dpm2016 Product risk Getting it right Customer riskRight path Market risk Viable business BUT RISKS!
  24. 24. @nataliewarnert #dpm2016 Run a Business Satisfy the Customer BALANCING NEEDS
  25. 25. @nataliewarnert #dpm2016 Run a Business Satisfy the Customer BALANCING NEEDS
  26. 26. @nataliewarnert #dpm2016 BUILD MODEL Build right thing Build thing right Build it fast
  27. 27. @nataliewarnert #dpm2016 LEARNING MODEL Build right thing Build thing right Build it fast Learning FocusSpeed
  28. 28. @nataliewarnert #dpm2016 WHERE IS THE LEARNING? Requiremen ts Dev QA Releas e
  29. 29. @nataliewarnert #dpm2016 WHERE IS THE LEARNING? Requiremen ts Dev QA Releas eLittle learning some learning Most learning
  30. 30. @nataliewarnert #dpm2016 You stand to learn the most when the probability of the expected outcome is 50%; that is, when you don’t know what to expect -Lean Analytics ”
  31. 31. @nataliewarnert #dpm2016 SHOW ME THE MVP! Declare assumptions and hypothesis Create an MVP Run an experiment to prove or disprove hypothesis Customer feedback (qual and quant) and research alternatives
  32. 32. @nataliewarnert #dpm2016 WHAT DID WE LEARN? Reduce scope Shorten the cycle time to feedback Get out the deliverables business
  33. 33. @nataliewarnert #dpm2016 QUESTIONS ? Running Lean - Ash Maurya Lean UX – Jeff Gothelf, Josh Seiden

Editor's Notes

  • TOM
  • 3 min
  • Corrupted and confused with MMP
  • This is my definition
    What are resources
    It doesn’t mean half baked or buggy
    What are table stakes?

    6 min
  • Designing for the customer and with the customer – how they want to work, not how we want them to, hard shift to make, think testing a product you wrote
    Deliverables – less over the wall and more collaborative
    All of our traditional language is missing

    8 min
  • 9 min
  • This is where the investigation starts – don’t just keep the idea in your head but SHARE it….start with problem
  • 13 min
  • 14 min
  • THIS COULD BE ANY RETAILER – ultimately bought, required, also viewed
    BBY example here with CWBAB and it not working – started with customers wanting add on accessories
    How that ties into the business model and problem of attach rates and conversion
    Business problem and customer problem – our one hypothesis fits both
  • Halloween costume – snoopy book and not even the great pumpkin
    So much data we don’t know what to do with it – so focused on this data but it was the wrong data

    18 min
  • Bold outcomes – change something big over small. Page over button. Or in this case embedded in cart vs. just changing items
    BBY example – we have our problem and our market fit. So what did we do with it.
  • Bold outcomes – change something big over small. Page over button. Or in this case embedded in cart vs. just changing items
    BBY example – we have our problem and our market fit. So what did we do with it.
    Interruption of the data they are used to seeing
    Besides CYP what else do we see in Carts? Buy it again…save for later...guests also bought – we have so much data we don’t know what to do with it.

    Bad example - polaris and having what we were going to build so defined that it couldn’t pivot (large release one, op model/workstation) – total customization vs. Packages what the customer actually needed vs. What we thought they wanted SOLUTION and BDUF

    What is the unique value proposition? This is somewhere in “solution” but we don’t know it...YET

    25 min
  • Remember our plan A here was rich relevance data…
    A – assumption that you know what the customer wants...and you know what you do when you assumption...
  • walk the walls at polaris
    Qualitatively – WHAT are they doing once we get them into cart – conversion from there is higher than browse…we know they are looking for more to buy...UX programs to see where hovers are, time spent, clicks, paper prototyping and contextual inquiry (modified)

  • When did we get out of the building? Went an did customer interviews and showed them what we were thinking (small margin) – is it stat sig? No. Is it sig? yes
    Also by walking through with everyone it’s easier to determine what is too high scope

    30 min
  • Validate quantitatively – analytics and A/B testing –

    How do we quantify what they are doing? Conversion rates, add rates, abandon rates…we wouldn’t have gotten here if we didn’t build something though. (large releases don’t work for this)
  • Validate quantitatively – analytics and A/B testing - one ring to rule them all

    You can tell any story with numbers, so what are the important ones? This is why we also use qualitative. It tells more of the story

    How do we quantify what they are doing? Conversion rates, add rates, abandon rates…we wouldn’t have gotten here if we didn’t build something though. (large releases don’t work for this)
  • But we are addressing these risks up front by talking with customers, experimenting, and validating with data
    Failing fast so oppty cost is less – talk about massive req and traditional stuff

    35 min
  • So what did we learn????
  • So we have to shorten this cycle!

  • Mvp != half baked or buggy

    Deliver enough value to justify charging (or running more experiments)

    Prioritize hypotheses
  • Attach rate went up
    Interaction in cart when the user has already showed more intent to buy
    Language of CYP vs. required, vs. CWBAB vs. Suggested…
    Try other products before we start to scale more – rich relevance data was expensive and didn’t want to “waste the money” – sunk cost
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