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Data informed design - UX Australia august 2015

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Data informed design - UX Australia august 2015

There’s a thing called “time to value” – it’s how long it takes a team to uncover and actualise value from a product. It’s a hard problem for most software products, because they aren’t architected and designed to solve the “time to value” problem. It’s usually an afterthought.

Building onboarding experiences that may or may not improve the customer experience can be both costly and time consuming, especially in enterprise software solutions – so how do you know that what you build will really add value?

Data, research or just building things in silos won’t solve the problem. Often too much data or research can make things worse by paralysing teams into inaction, or worse they just start building something, anything without understanding the impact it will have to the experience.

Working with large scale enterprise products with millions of customers, and navigating through long roadmaps can be a tough place to try and build fast growth into a product. It is hard to apply startup thinking when you need to care and value the experience that millions of customers have with your software each and everyday. But in order to survive and continually grow, you need to find a way.

Atlassian approached and solved this problem by leveraging a combination of growth hacking, user research, data analytics and A/B testing at scale to dramatically increase customer engagement with our products. I’ll describe the variety of approaches we started with and how we learned which ones to pursue and which ones to discard. The design and growth hacking teams worked together to pull off some pretty amazingly fast ways to modify and test variations of an enterprise product experience — without interfering with the product team. Finally, I’ll show how to design and centralise improved onboarding experiences that can be scaled across all of your products.

There’s a thing called “time to value” – it’s how long it takes a team to uncover and actualise value from a product. It’s a hard problem for most software products, because they aren’t architected and designed to solve the “time to value” problem. It’s usually an afterthought.

Building onboarding experiences that may or may not improve the customer experience can be both costly and time consuming, especially in enterprise software solutions – so how do you know that what you build will really add value?

Data, research or just building things in silos won’t solve the problem. Often too much data or research can make things worse by paralysing teams into inaction, or worse they just start building something, anything without understanding the impact it will have to the experience.

Working with large scale enterprise products with millions of customers, and navigating through long roadmaps can be a tough place to try and build fast growth into a product. It is hard to apply startup thinking when you need to care and value the experience that millions of customers have with your software each and everyday. But in order to survive and continually grow, you need to find a way.

Atlassian approached and solved this problem by leveraging a combination of growth hacking, user research, data analytics and A/B testing at scale to dramatically increase customer engagement with our products. I’ll describe the variety of approaches we started with and how we learned which ones to pursue and which ones to discard. The design and growth hacking teams worked together to pull off some pretty amazingly fast ways to modify and test variations of an enterprise product experience — without interfering with the product team. Finally, I’ll show how to design and centralise improved onboarding experiences that can be scaled across all of your products.

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Data informed design - UX Australia august 2015

  1. 1. ALASTAIR SIMPSON • DESIGN MANAGER • ATLASSIAN • @ALANSTAIRS Building fast growth into your products Data driven informed design
  2. 2. Time to value - TTV - Time to realise value for customer - Tricky to understand. - Fast growth = fully understand TTV equation.
  3. 3. The Parsonage B&B San Francisco - Trip to SF to speak at conference - Stayed at the Parsonage B&B
  4. 4. - Flew from Sydney, Zoe, Frankie - 1st trip for 6 month old - Daunted, 15 hour flight - Stay; safe, quiet, away from other guests
  5. 5. - For when my daughter was jet lagged and crying in the middle of the night
  6. 6. - Love to host us - So not disturb other guests, own apartment, private floor, separate room, same cost - Taxi with car seat - Personal, honest and tailored to my needs. - TTV was instant.
  7. 7. Time to value - This example = quick to wow - Worked hard at this. - How do you replicate in digital products, at scale? - How long customer see value? - Products not built to optimise for TTV - Built to solve initial problem - Grow, more features added - More complexity - Great for power users - Terrible for a new customer, lost and confused
  8. 8. 50% of new users spend less than 30 minutes trying our products before leaving. - Analysed data - 50% new customers less than 30 minutes with our products.
  9. 9. 30 minutes - Products solve complex problems - 30 minutes isn’t long enough realise value - onboarding flow was clunky - No quick to wow moment - Very steep learning curve in order to realise value - Too many just not sticking around - 10 minutes from now you will walk out of here with a blueprint for how to build fast growth into your products using data informed design
  10. 10. ✓ Set up core teams ✓ Clear success metrics ✓ Centralised patterns Won’t dig into these things; but we: - Changed org structure. Setup TTV teams, 1 owner, 1 person accountable - Clear success metrics - Centralised design patterns that worked and didn’t into our design guidelines, meant we could scale across products.
  11. 11. Data driven informed design - Main thing I want to talk about is data informed, not driven, design. - Subtle difference. - Let me illustrate a data driven approach to you.
  12. 12. Stand up please - Everyone stand up please - Donna kindly gave me some data that she had on attendees.
  13. 13. 50% are “UX Designers” Stay standing - According to that data 50% of you are UX Designers
  14. 14. 55% are “male” Stay standing - 55% are male
  15. 15. 10% are from Brisbane Stay standing - 10% are from Brisbane - Based on this data driven approach I could make an assumption that you look like this.
  16. 16. - Oh…so looking around I am wrong. - This is a data driven approach… - Lets imagine I asked a few questions from those left standing - Where do you shop? - Do you buy hats very often? - Do you wear glasses? - and I build up a small bit of empathy for my customer and better understand what you they look like.
  17. 17. - And I now can see that they look like this… - Please take a seat. Thank you for indulging me.
  18. 18. Data Empathy Gut feelingGreat product design Data informed design - Amazing product design - Understand data what customers are doing - Empathy for customers problems facing - Some intuition - Making decisions is what we are paid to do. - Lets look at data.
  19. 19. AAA Data - Atlassian we talk about AAA data - what does that mean? - We love acronyms
  20. 20. Accurate - Data must be accurate. - Basing product decisions on this, especially at enterprise level. Data must be accurate. - Ask plenty of questions as sometimes the data is not always what it seems.
  21. 21. Accessible - Data must be accessible for all. It wasn’t in our case. - To access our data you had to know SQL and have access to the right database to get the data. - Designers couldn’t get access to it.
  22. 22. Actionable - Data without actionable insights is useless. - It must be actionable and contain insights. - So once we had our AAA data, we need to focus on 1 data point, or metric that gave us the best possible proxy for customer value in our products.
  23. 23. Engagement (Time spent) Conversion - So for us, our TTV data point was engagement (Time spent) and conversion. - Having analysed our data these were the best possible proxies for customer value. - Conversion was also a clear indication of value, as customers had to put CC in at day 7. - So these were the data points we optimised for.
  24. 24. A/B Test Noun | A/B Test |ˈā-ˈbē ˈtest An experiment in which a single element of a web page is altered to create one or more variations of a page. - A/B Test - 2 variations same page to 50/50 split - Can work out better - Push directly into production or iterate.
  25. 25. Workshop Design + Development Running Analysis Experiment lifecycle (3 weeks) - Lean UX methodology - Workshops, design and dev, run and analyse. - 3 weeks to get results - Quick turnaround - Couple of months - Tested 6 to 10 different ideas in few months.
  26. 26. FIRST ITERATION -12% engagement -10% conversion - Time passed - Experiments only on data and gut feeling at this point - No customer empathy - One experiment high hopes totally bombed. -12% conversion. - Driven down conversion by in theory improving onboarding
  27. 27. - Self sealing logic - Stuck in our own belief bubble - Get out and experience customers
  28. 28. Data Empathy Gut feeling Data informed design - So empathy, or qualitative feedback from customers - Get out of office - Speak to customers - Anyone can do this - You don’t have to have a dedicated researcher
  29. 29. I’m trying to understand how something could have that amount of power and be that difficult to pick up. (ANOTHER) POTENTIAL CUSTOMER ” “ - Build up customer empathy within your organisation for problems facing your customers - Amazing quotes from customers - Ran Usability testing. - Customer interviews.
  30. 30. - Diary studies. first 10 days tumblr. They were all mini-stories. Organisation and us could not deny them. - Great insights - Paired with data, understood what and why - Brought the pain home that customers experienced when first using our tools.
  31. 31. Put these customer journeys up on walls. Days. Yellow = customer actions - blue = questions - pink = pain These diaries really helped us build up an understanding of how people used and saw value in our products. It gave us the why for what we were seeing in our data. It also brought the pain home that customers experienced when first using our tools.
  32. 32. 2. Help me set up and complete critical tasks 3. Show me practical value in everyday work 4. Demonstrate immediate value to subsequent users 1. Identify my problem & potential solution Time to value conceptual model - Created TTV model - Why is a model important? - No framework = hacking blindly - Won’t result in fast growth, unless you get very lucky. - Model = backbone of all future experimentation efforts.
  33. 33. Workshop Design + Development Running Analysis Experiment lifecycle (3 weeks) Qualitative insights → - Now we were still running fast, getting results in a matter of weeks. - But now we had qualitative insights instead of assumptions and just numbers - We were informed….
  34. 34. ~ engagement +22% conversion SECOND ITERATION - Same experiment = -11% result - Are you ready? - Made some tweaks based on our increased customer understanding. - +22% conversion. - Not easy to add 22% to your bottom line overnight, but we did. - Released this directly to production within a few weeks -
  35. 35. Data Empathy Gut feeling Data informed design - Lastly gut feeling. - Some designers worry that data takes way their creative freedom. I disagree I think it enhances it. - First experiment right track even though the result was negative - Didn't throw it away. - The qual feedback gave us insights into why and we iterated and made it a success.
  36. 36. Data and A/B tests are valuable allies, and they help us understand and grow and optimize, but they’re not a replacement for clear-headed, strong decision-making. JULIE ZHUO (FACEBOOK DESIGN DIRECTOR) ” “ - Julie Zhuo is Facebooks product design director. - Data / research great but - No substitute for clear headed thinking and decision making.
  37. 37. Stand up if this experiment was a success
  38. 38. - Added a product tour, the type you see on every web app
  39. 39. -8% engagement ~ conversion
  40. 40. Stand up if this experiment was a success
  41. 41. - Same page. With intro mode on removed ~60% features
  42. 42. +22 engagement +17% conversion
  43. 43. Thanks! design.atlassian.com ALASTAIR SIMPSON • DESIGN MANAGER • ATLASSIAN • @ALANSTAIRS

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