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User Research London 2018: Adventures in UX Research & Data

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I presented this talk at User Research London on 21st June 2018.

What is the talk about?
Across different teams and organisations a common problem researchers often face is the ability to show the value and impacts of the designs delivered, and then confidently communicate those impacts to a wider audience, such as C-suite or within product teams. This gap can make it difficult for the wider audience to buy-in to the process or appreciate the value of user-centred design.

In this talk I will draw on examples in delivering digital services in the UK Government and from running lean research for start ups, to show how design teams can measure the impacts of the services and products delivered by using qualitative and quantitative data.

Published in: Technology
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User Research London 2018: Adventures in UX Research & Data

  1. 1. @melonxdesign User Research London 2018 Adventures in UX Research & Data Christina Li @chrissy0118 @melonxdesign
  2. 2. Hello! @chrissy0118
  3. 3. What I will cover today ● What do we mean by ‘data’? ● Why we should care about data? ● Tips on how you can add data into your practice @chrissy0118
  4. 4. Are the designs making an impact? @chrissy0118
  5. 5. ● Change in an outcome? ● Left an impression? @chrissy0118 @chrissy0118
  6. 6. What do we mean by ‘data’? @chrissy0118
  7. 7. Quantitative vs Qualitative data Cost per transaction (£) Volume of call (%) Cost savings (£) Call wait time Bounce rate (%) Observation insights Videos Call transcripts User needs User tasks Pain points Revenue @chrissy0118
  8. 8. Data complements research insights @chrissy0118
  9. 9. Supporting each other Cost per transaction (£) Volume of call (%) Cost savings (£) Call wait time Bounce rate (%) Observation insights Videos Call transcripts User needs User tasks Pain points Revenue What is happening? Why is this happening? @chrissy0118
  10. 10. User Researcher (You!) Cost per transaction (£) Volume of call (%) Cost savings (£) Call wait time Bounce rate (%) Observation insights Videos Call transcripts User needs User tasks Pain points Revenue Why is this happening? What is happening? @chrissy0118
  11. 11. ‘Speak’ enough data @chrissy0118
  12. 12. Why we should care about both data @chrissy0118
  13. 13. 1. Help us prioritise work @chrissy0118
  14. 14. 2. Reduce bias @chrissy0118
  15. 15. 3. Calculate cost of not doing something @chrissy0118
  16. 16. An example 30 mins phone call 6 phone calls a day 3 hours a day 15 hours a week 2 full time employees day £200 a day £400 each week @chrissy0118
  17. 17. An example ● What if call volume increased? ● What if the problem starts appearing in multiple channels? ● How costly would that be? @chrissy0118
  18. 18. 4. Support feedback loop @chrissy0118
  19. 19. What can we do better? @chrissy0118
  20. 20. Possible outcomes ● Is this an issue (or similar) that’s in the backlog already for re-work or improvement? ● Is this an issue we should stop other work for? ● Is this an issue that needs further investigation before we can make a decision? @chrissy0118
  21. 21. 4 tips to using data in your research @chrissy0118
  22. 22. 1. Create a ‘success measures’ culture @chrissy0118
  23. 23. Defining success 1. Why are we doing this work? 2. What problems are we solving? 3. Who is this work for? 4. What outcomes are you expecting? 5. What are the key metrics? http://www.hollidazed.co.uk/2015/07/28/frame-the-problem/ @chrissy0118
  24. 24. 2. Use hypothesis-driven design to track measures @chrissy0118
  25. 25. We believe that [behaviour] So if we [do this action] We will see [this outcome] @chrissy0118
  26. 26. We believe that customers want to try new cuisines, So if we show them cuisines they haven’t tried before We will see customers choosing a new cuisine Alpha @chrissy0118
  27. 27. We believe that customers want to try new cuisines, So if we show them cuisines they haven’t tried before We will see customers making an order from a new cuisine Beta @chrissy0118
  28. 28. We believe that customers want to try new cuisines, So if we show them cuisines they haven’t tried before We will see customers making repeat orders from new cuisines Live @chrissy0118
  29. 29. Backlog Priorities Being designed Ready to test (research) Ready to build @chrissy0118
  30. 30. 3. Track your design impacts @chrissy0118
  31. 31. How might we create a work coaching experience to help people get back to work? Example 1 @chrissy0118
  32. 32. Co-contributing to job search Example 1 1 2 3 @chrissy0118
  33. 33. Measurements ● How many people completed the background information digitally? ● How much time did the work coach took to review the information? ● How engaged did the people feel about the process? @chrissy0118
  34. 34. How might we reduce the stress & anxiety in getting a civil claim in on time? Example 2 @chrissy0118
  35. 35. Old processing time: 20 working days New processing time: Instant (digital) This is simple & powerful
  36. 36. 4. Talk business to communicate findings @chrissy0118
  37. 37. Who needs to know about the findings? @chrissy0118
  38. 38. Indirect Direct Core You & your team Product Project manager Legal CEO CTO InvestorsPublic Lobbying groups @chrissy0118
  39. 39. What do they care or worry most about? @chrissy0118
  40. 40. … Profit? Cost savings? … Can we launch the product? … Usability? Overall experience? Indirect Direct Core @chrissy0118
  41. 41. In summary: 1. Data complements research insights 2. ‘Speak’ data to frame and communicate research questions 3. Use data to help prioritise work & reduce research bias 4. Show the size & scale of problem 5. Add data into your feedback process 6. Track your design decisions and measurement outcomes 7. Think about which data is appropriate for the target audience @chrissy0118
  42. 42. Thank you Christina Li @chrissy0118 @melonxdesign hello@melonxdesign.com ©Melon Experience Design Ltd. Company number 10643995. Registered in England & Wales..

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