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Continuous discovery - Caitlin Blackwell

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Caitlin shares with Product Anonymous how Seek have been working with Teresa Torres to improve their product management practice with continuous discovery + use of Opportunity tree's.

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Continuous discovery - Caitlin Blackwell

  1. 1. Continuous Discovery - Worth the effort? Product Anonymous Caitlin Blackwell 27 June 2019
  2. 2. Caitlin Blackwell Acting Head of Product – Candidate Experience SEEK ANZ
  3. 3. Agenda 1. What is discovery and why do it? 2. Continuous Discovery Framework 3. Successes and failures
  4. 4. “McKinsey investigated 1,048 business decisions over 5 years, tracking both the way decisions were made and the subsequent outcomes. These were major decisions such as launching a new product or service, changing the structure of an organisation or an acquisition. They found that the decision making process mattered more than analysis by a factor of 6.” Discovery is about driving better decision making
  5. 5. 5 What do Product Managers do?
  6. 6. Unstructured Variable approaches A developing craft 1. Discovery Deciding what to build Work out the most valuable thing to build Highly structured Agreed approaches Mature 2. Delivery Building it Delivering the most value quickly ? ?
  7. 7. 1. Discover what the customer solution needs to be If you get discovery wrong then what you deliver could be wasted effort
  8. 8. We encounter the ‘villains of decision making.’ Why are we wrong? Narrow framing: we unduly limit the options we consider and so we don’t focus on the biggest opportunities & don’t generate enough compelling solutions Lack of clarity on the problem Short term emotion Confirmation bias Overconfidence
  9. 9. We don’t test our critical assumptions early enough and they fail to be true Why are we wrong? Customers don’t want it (the value isn't there) It’s too hard to use (the usability isn't there); There are business constraints that block the solution from launch (the business viability isn't there) It’s too costly or time consuming to deliver (the feasibility isn't there); It’s not the right thing to do (we uncover ethical issues);
  10. 10. So how can we be right more often?
  11. 11. Continuous Discovery 3. Size needs and pick one 2. Speak to customers regularly, map their needs 1. Agree desired business outcome (i.e. your OKR) 4. Ideate and validate assumptions through experiments
  12. 12. Step 1 3. Size needs and pick one 2. Speak to customers regularly, map their needs 1. Agree desired business outcome (i.e. your OKR) 4. Ideate and validate assumptions through experiments
  13. 13. 1. Agree Team’s Desired Outcome Objectives Key Results Key ideas/initiatives • Help SME hirers efficiently identify suitable candidates to interview • Increase hirer sat score from x to y • Increase shortlist rate from x to y • Hirer filters • Add more detail to candidate card • Matching tool Objectives • Grow use of product
  14. 14. Step 2 3. Size needs and pick one 2. Speak to customers regularly, map their needs 1. Agree desired business outcome (i.e. your OKR) 4. Ideate and validate assumptions through experiments
  15. 15. 2. Speak to customers and map their needs
  16. 16. 2. Speak to customers and map their needs Desired outcome Opportunity/Need Solution
  17. 17. “What’s in the spotlight will rarely be everything we need to make a good decision. Sometimes we’ll forget there’s a spotlight at all dwelling so long in the tiny circle of light that we forget there’s a broader landscape beyond it.” Daniel Kahneman
  18. 18. Step 3 3. Size needs and pick one 2. Speak to customers regularly, map their needs 1. Agree desired business outcome (i.e. your OKR) 4. Ideate and validate assumptions through experiments
  19. 19. 3. Size Opportunities & pick one
  20. 20. Step 4 3. Size needs and pick one 2. Speak to customers regularly, map their needs 1. Agree desired business outcome (i.e. your OKR) 4. Ideate and validate assumptions through experiments
  21. 21. 4. Ideate and validate assumptions through experiments
  22. 22. 4. Ideate and validate assumptions through experiments
  23. 23. 4. Ideate and validate assumptions through experiments
  24. 24. 4. Ideate and validate assumptions through experiments Theresa May
  25. 25. 4. Ideate and validate assumptions through experiments Important and unknown
  26. 26. 4. Ideate and validate assumptions through experiments
  27. 27. 4. Ideate and validate assumptions through experiments
  28. 28. 4. Ideate and validate assumptions through experiments
  29. 29. 4. Ideate and validate assumptions through experiments
  30. 30. 4. Ideate and validate assumptions through experiments Fogg Model
  31. 31. Use case: I don’t want to commute too far, so I want to ONLY see jobs close to me
  32. 32. 4. Ideate and validate assumptions through experiments
  33. 33. 4. Ideate and validate assumptions through experiments
  34. 34. 4. Ideate and validate assumptions through experiments 34 From this… … to this … and finally this
  35. 35. Where to start? 1. If you can only do one thing, start by talking to customers and asking them how they use your product. Everything in discovery flows from this. 2. You don’t need to be using OKRs to pick your desired outcome, just find a metric in your product you are looking to shift eg revenue, churn, acquisition
  36. 36. Where to start? 3. Go broad with ideas – go for quantity first. Blind vote. 4. Some form of assumption mapping is essential if you want to learn before you build
  37. 37. Where to start? 5. Visual sensemaking tools help you stay aligned, and avoid some of the opinion battles 6. In experimenting aim to learn something on your assumptions, rather than the most definitive answer. Challenge yourself: what can I learn in a week?
  38. 38. Further reading • An Introduction to Modern Product Discovery - Teresa Torres YouTube • Decisive – Chip and Dan Heath • Inspired – Marty Cagan • Teresa Torres blog producttalk.org, videos and podcast interviews

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