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Data Informed Design - Good Tech Test - May 2018

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When it comes to design, everyone has an opinion! However, during reviews and discussions it’s those with more than an opinion that fair the best. Successful design solutions require a deep understanding of audiences, clear strategy, and good ole data.

In this session you’ll learn:
- Common data sources for design
- How to build a data-informed approach (not data-driven)
- What data-informed design looks like in the wild (aka case studies).

Whether you’re trying to prove a point, make an improvement, or discover something new, data-informed design moves your team from gut-feelings to fact-based decisions.

Publicado en: Diseño

Data Informed Design - Good Tech Test - May 2018

  1. 1. May 23, 2018 Good Tech Fest DATA INFORMED DESIGN
  2. 2. Pied Piper Design Concept from Silicon Valley (HBO)
  3. 3. I have no idea what you’re going to want. Source: HBO's Silicon Valley, Pied Piper Design Concept
  4. 4. Who are you? 1
  5. 5. Courtney Clark Managing Director of User Experience @circlish https://www.linkedin.com/in/clarkcourtney/
  6. 6. What is the difference between data-informed and data-driven? 2
  7. 7. Intuition Data-informed Data-driven No or minimal data used. Design intuition, hunches. Using both expertise and data to analyze and make decisions. Blindly following the data. No humans or intuition used.
  8. 8. Source: Metrics Driven Design, Joshua Porter
  9. 9. Source: Metrics Driven Design, Joshua Porter
  10. 10. Why data-informed design? 3
  11. 11. Discover Improve Justify Tell Better Stories Support discussion. Justify decisions. Support marketing. Support your cause. Uncover new information. Discover possibilities. Optimize. Increase conversions. Measure before / after. Grow Skill Communicate with analysts. Improve your portfolio. Be Strategic Confirm you’re headed in the right direction.
  12. 12. Create the Best Possible Product Achieving business goals. Supporting primary audience.
  13. 13. You Probably Have Some Data Use it!
  14. 14. What data can I use to inform my design? 4
  15. 15. Da·ta (ˈdadə,ˈdādə/) n. facts and statistics collected together for reference and analysis
  16. 16. Digital Analytics Search Data Social Media Data Email Engagement Data Grant Data Fundraising Data Financial Data Impact Data Research Data Volunteer Data Event Attendance Data Demographic Data Brand Sentiment Data Brand Lift Data Competitor, Comparator Data So much data!
  17. 17. What People Do What People Say Why & How to Fix How Many, How Much First-click Testing Interviews Usability Testing Source: Nielsen Norman Group A/B Testing Feedback Widget Email Surveys
  18. 18. HEART Framework Source: Google Ventures Happiness, Engagement, Adoption, Retention, Task Success
  19. 19. Source: Google Ventures, Digital Telepathy
  20. 20. Source: Google Ventures, Digital Telepathy H E A R T Happiness Engagement Adoption Retention Task Success Measures of user attitudes, often collected via survey. Level of user involvement. Gaining new users of a product or feature. The rate at which existing users are returning. Efficiency, effectiveness, and error rate. Examples ● Satisfaction ● Perceived ease of use ● Net-promoter score Examples ● Number of visits per user per week ● Number of photos uploaded per user per day ● Number of shares Examples ● Upgrades to the latest version ● New subscriptions created ● Purchases made by new users Examples ● Number of active users remaining present over time ● Renewal rate or failure to retain (churn) ● Repeat purchases Examples ● Search result success ● Time to upload a photo ● Profile creation complete
  21. 21. Source: Google Ventures, Digital Telepathy H E A R T Happiness Engagement Adoption Retention Task Success Measures of user attitudes, often collected via survey. Level of user involvement. Gaining new users of a product or feature. The rate at which existing users are returning. Efficiency, effectiveness, and error rate. Examples ● Satisfaction ● Perceived ease of use ● Net-promoter score Examples ● Number of visits per user per week ● Number of photos uploaded per user per day ● Number of shares Examples ● Upgrades to the latest version ● New subscriptions created ● Purchases made by new users Examples ● Number of active users remaining present over time ● Renewal rate or failure to retain (churn) ● Repeat purchases Examples ● Search result success ● Time to upload a photo ● Profile creation complete Survey Analytics Usability Testing
  22. 22. Source: Google Ventures, Digital Telepathy Goals Signals Metrics Get goals from different team members. Build consensus. Best predictors of associated goals. Data you’ll track over time. Example For people to enjoy, discover, and engage with content. Example The amount of time people spend engaging with content. Example Average engagement time with content per day. Key Questions ● How will the user experience help? ● Are you interested in increasing the engagement of existing users or in attracting new users? Key Questions ● How easy or difficult is each signal to track? ● Is your product instrumented to log the relevant actions, or could it be? ● Is this signal sensitive to changes in your design? Key Questions ● Will you actually use these numbers to help you make a decision? ● Do you really need to track them over time, or is a current snapshot sufficient?
  23. 23. Source: Google Ventures, Digital Telepathy
  24. 24. You only need to use categories or data relevant to your product.
  25. 25. How to build a data-informed approach? 5
  26. 26. 1. Set up a meeting with your business analyst, analytics team, data person 2. Inventory the data you have on your project now 3. Fill out the HEART worksheet 4. Design! 5. Iterate, test, improve 6. Reflect and debrief with your team Get Started
  27. 27. Ask yourself: What data do we have to support this? How will we get data to validate this?
  28. 28. What does data-informed design look like in the wild? 6
  29. 29. Quantitative data tells you what is happening. Qualitative data tells you why it’s happening.
  30. 30. Usability Testing
  31. 31. “But I want to know more about the work, so I’m going to click on ‘Our Work.’ Oh! I can’t do that for some reason.” “The very first link is ‘Our Work,’ so I believe I would just click on that. It doesn’t seem to be an accessible feature or maybe I’m already on that page… oh, it’s just not an accessible feature at this point.”
  32. 32. A/B Testing
  33. 33. Search Analytics
  34. 34. Search Analytics Question Why ask? Search + Time on Site How much time are users spending on the site after they've conducted a search? If users are spending a significant amount of time on the site after a search, and the average search depth is high, it suggests users are finding value in search and combing through the site to learn more, especially when a site is content rich the way this site is. It is also an indicator that the user is well engaged. Top Terms What are the top search terms? This will help us understand the type of content people are looking for and can help inform content hierarchy. Top Terms + Exits What are the top terms that have high percentages of search exits and search refinements? This may indicate that the content users are searching for doesn't exist. Channels + Search Which traffic channel segments drive the most internal searches? If they are using the search to refine, it could mean that they didn’t find the site from the right landing page. Pages + Search What pages do users start their searches on the most? And, what search terms do they use on those pages? From there we can look at those pages and determine how those pages are structured, and if the information they were looking for is obvious and easy to find on that page. Search + Page Depth What is the average search depth (the average # of pages people viewed after running a search)? An average search depth higher than 2 usually means people don’t find what they want from the first search.
  35. 35. What challenges will I face with data-informed design? 7
  36. 36. Data Availability & Skills
  37. 37. GOOD BAD UGLY 90% of nonprofits are collecting data 49% don’t know ways their org is collecting 13% never or rarely use data. Source: Everyaction • 2016 • The State of Nonprofit Data white paper
  38. 38. Not collecting enough data 36% Source: Everyaction • 2016 • The State of Nonprofit Data white paper Lack of tools to help analyze data Data isn’t kept in one place Don’t have enough experience using data Not enough time, or personnel to focus on data 42% 46% 55% 79%
  39. 39. Over-indexing on Data-driven
  40. 40. I had a recent debate over whether a border should be 3, 4 or 5 pixels wide, and was asked to prove my case. Source: Goodbye Google, Douglas Bowman
  41. 41. Source: Metrics Driven Design, Joshua Porter
  42. 42. Wait, why should I care about this? 8
  43. 43. How Designers are Building Careers in Silicon Valley from KPCB
  44. 44. To achieve great design, you need great business thinking / doing… Source: How Designers are Building Careers in Silicon Valley
  45. 45. To achieve great design, you need great business thinking / doing… Yay data-informed design!
  46. 46. Now what? 9
  47. 47. Keep Reading Data Informed Design, Not Data-Driven How to Choose the Right UX Metrics The Agony and Ecstasy of Building with Data Data-informed Design (5 Things I Learned the Hard Way) Data-driven vs Data-informed Design in Enterprise Products
  48. 48. Questions & Answers

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