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CSE 440: Introduction to HCI User Interface Design, Prototyping, and - PowerPoint PPT Presentation

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CSE 440: Introduction to HCI User Interface Design, Prototyping, and Evaluation Lecture 09: James Fogarty Tasks in Testing, Kailey Chan Patterns Dhruv Jain Nigini Oliveira Tuesday / Thursday Chris Seeds 12:00 to 1:20 Jihoon Suh Project

  1. Controlled A/B Experiments Example: Amazon Shopping Cart Recommendations Add an item to your shopping cart Most sites show the cart At Amazon, Greg Linden had idea to show recommendations based on cart items From Greg Linden’s Blog: http://glinden.blogspot.com/2006/04/early-amazon-shopping-cart.html

  2. Controlled A/B Experiments Evaluation Pro: cross-sell more items Con: distract people from checking out Highest Paid Person’s Opinion: Stop the project Simple experiment run: Wildly successful From Greg Linden’s Blog: http://glinden.blogspot.com/2006/04/early-amazon-shopping-cart.html

  3. Marketplace: Solitaire vs Poker Experiment run in Windows Marketplace / Game Downloads Which image has the higher clickthrough? By how much? A: Solitaire game B: Poker game

  4. Marketplace: Solitaire vs Poker Experiment run in Windows Marketplace / Game Downloads Which image has the higher clickthrough? By how much? A: Solitaire game A is 61% better B: Poker game

  5. Never Underestimate Solitaire

  6. Never Underestimate Solitaire

  7. Checkout Page Conversion rate is percentage of visits that include purchase A B Which version has a higher conversion rate? Example from Bryan Eisenberg’s article on clickz.com

  8. Checkout Page Conversion rate is percentage of visits that include purchase A B Which version has a higher conversion rate? Example from Bryan Eisenberg’s article on clickz.com

  9. Checkout Page Conversion rate is percentage of visits that include purchase A B Coupon Code decreases by factor of 10

  10. 61 Office Online Feedback A B Feedback A puts everything together, whereas feedback B is two-stage: question follows rating. Feedback A just has 5 stars, whereas B annotates the stars with “Not helpful” to “Very helpful” and makes them brighter. Which one has a higher response rate? By how much?

  11. 62 Office Online Feedback A B Feedback A puts everything together, whereas feedback B is two-stage: question follows rating. Feedback A just has 5 stars, whereas B annotates the stars with “Not helpful” to “Very helpful” and makes them brighter. Which one has a higher response rate? By how much? B gets more than double response rate.

  12. Another Feedback Variant Call this variant C. Like B, also two-stage. Which one has a higher response rate, B or C? C

  13. Another Feedback Variant Call this variant C. Like B, also two-stage. Which one has a higher response rate, B or C? C C outperforms B by a factor of 3.5

  14. Office Online Clicks on revenue generating links (red links) A B

  15. Office Online Clicks on revenue generating links (red links) A B A B A gets many more clicks

  16. Office Online Clicks on revenue generating links (red links) A B A B B gets more revenue

  17. Examples Where Data Is Wrong If something is “amazing,” find the flaw! If you have a mandatory birth date field, and people think it’s unnecessary, you will find lots of 11/11/11 or 01/01/01 If you have an optional drop down, do not default to the first alphabetical entry, or you will have lots of: jobs = Astronaut Traffic to doubled between 1-2am Nov 6, 2011 for many web sites, relative to same hour week prior

  18. MSN US Home Page Proposal: New Offers module below Shopping Control Treatment

  19. Experiment Results Ran A/B test for 12 days on 5% of MSN US visitors

  20. Experiment Results Ran A/B test for 12 days on 5% of MSN US visitors Clickthrough: Page views per person-day:

  21. Experiment Results Ran A/B test for 12 days on 5% of MSN US visitors Clickthrough: decreased 0.49% Page views per person-day: decreased 0.35%

  22. Experiment Results Ran A/B test for 12 days on 5% of MSN US visitors Clickthrough: decreased 0.49% Page views per person-day: decreased 0.35% Value of click from home page: X cents Net = Expected Revenue – Value Per Click * Direct lost clicks – Value Per Click * Lost Due to Decreased Views

  23. Experiment Results Ran A/B test for 12 days on 5% of MSN US visitors Clickthrough: decreased 0.49% Page views per person-day: decreased 0.35% Value of click from home page: X cents Net = Expected Revenue – Value Per Click * Direct lost clicks – Value Per Click * Lost Due to Decreased Views Net was negative (in millions of dollars), offers module did not launch

  44. 95 Data Driven Methods Not Just Online

  45. 96 Data Driven Methods Not Just Online

  46. 97 Data Driven Methods Not Just Online

  47. Limitations of Data Driven Testing Drives hill-climbing, but not overall design A design may be better, but is it good? Impossible for new designs to compete Can be difficult to scale to many features Now we step through a larger example

  48. 1 99

  49. 2 100 CSE440 - Autumn User Interface Design, 2007 Prototyping, and Evaluation

  50. 3 101 CSE440 - Autumn User Interface Design, 2007 Prototyping, and Evaluation

  51. 4 102 CSE440 - Autumn User Interface Design, 2007 Prototyping, and Evaluation

  52. 5 103 CSE440 - Autumn User Interface Design, 2007 Prototyping, and Evaluation

  53. 6 Quick-Flow Checkouts 104 CSE440 - Autumn User Interface Design, 2007 Prototyping, and Evaluation

  54. Testing in a Larger Design

  56. Today Ethics in Testing Tasks in Testing Wizard of Oz Methods in Testing Remote Usability Testing Patterns

  57. Design Equals Solutions Design is about finding solutions Designers often reinvent Hard to know how things were done before Why things were done a certain way How to reuse solutions One option is patterns But this is also why we point you at research

  58. Design Patterns Design patterns communicate common design problems and solutions First used in architecture [Alexander] How to create a beer hall where people socialize?

  59. Design Patterns

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