KPIS in Context - Stephanie Lawrence, Randy Clinton - REcon 18
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KPIS in Context - Stephanie Lawrence, Randy Clinton - REcon 18
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In this session, we will discuss how UX/Product and Analytics teams can work together to understand user behavior by examining the relationships between specific KPIs and doing advanced analysis of behavioral patterns using machine learning. We will talk about why UX/design researchers would want to take this approach to researching user behavior, how Machine Learning is used to analyze user behavior, and the pros and cons of using this method.
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KPIS in Context - Stephanie Lawrence, Randy Clinton - REcon 18
- 1. KPIs in Context: Working with Analytics to Better Understand User Behavior Stephanie Lawrence Randy Clinton RECon18 - October 20, 2018
- 3. Quick Intro to KPIs and Measuring User Behavior 3
- 4. Quick Summary » KPI = Key Performance Indicator » Measurements of user behavior you care about » Can be quantitative or qualitative Examples: NPS scores, conversion/sign up rates, page views, click-through rates, bounce rates, task completion rates, mentions of confusion during usability testing, task abandonment 4
- 5. Our Story 5
- 6. What did our team care about? » Favoriting » Number of search result items » Read reviews » Purchase 6
- 7. What do these metrics mean in relation to one another? Or do we just want these numbers to go up? 7
- 8. How do we measure this story? How do we start to map out different stories, based on the data we have? 8
- 9. Analytics/Data Science » K cluster analysis/Machine Learning 9 Source: K Means Clustering : Identifying F.R.I.E.N.D.S in the World of Strangers, towardsdatascience.com, link
- 10. What we found Group 1 2 3 4 Results view 25 25 100 60 Favoriting 0 0 10 3 Read reviews 0 5 20 10 Sample size 60K 30K 10K 5K Purchase no no no yes 10
- 11. Sub- Group 4A 4B 4C 4D Results view 50 50 150 300 Favorite 0 1 5 10 Read reviews 7 0 10 20 Sample size 3K 1K 750 250 11 What we found
- 12. Rethinking our KPIs » Not just thinking in terms of numbers going up or down » Also considering ratios/percentage 12
- 13. What did we obtain from this effort? » A better understanding of our users’ story » Alternative to a persona » A way to inform prioritization for our product team 13
- 14. Takeaways 14
- 15. Work with your analytics team, or any analytics resources you have, to start exploring these relationships 15
- 16. Pros » You can get scientific » You don’t look at metrics in isolation » Mixed methods = gold » It doesn’t have to be complex 16
- 17. Cons » It takes time ⋄ BUT: These things can also be automated » Feeding the flames of the quant vs qual battle ⋄ BUT: Embracing mixed methods techniques is a great way of combating this » Comprehension can be a challenge ⋄ BUT: It can help make things that are already complex (relatively) easier to understand 17
- 18. Think about how you would do this, and how it could benefit your team 18
- 19. Consider for yourself » Think about 2-3 metrics you have for a product you’re working on ⋄ It should be able to be grouped or numbered » Think about how they can all relate to one another in terms of a story » Think of ways to represent that relationship through data analysis 19
- 21. THANK YOU! contact@stephcl.com @sclawr Presentation template by SlidesCarnival Images from Unsplash and Towards Data Science randyclinton@gmail.com @raclinton https://www.linkedin.com/in/racli nton/