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You'll Never Look at Interactive the Same After this Deck

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Games and interactive systems are different from any other medium in that they require their users to constantly make decisions.

The decisions' type and the intensity of interaction define the global experience.

These two elements form the TIMT (The Interaction Mapping Tool). A tool that will help you quantify the user experience of any interactive activity and adjust it's variables properly to fit your users' state of mind.

Granted, you will not look at interactive media the same after this talk.

Publié dans : Design
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You'll Never Look at Interactive the Same After this Deck

  1. 1. YOU WILL NEVER LOOK INTERACTIVE MEDIA ~the same~ AFTER THIS DECK at
  2. 2. Hi! I’m Dori. I’m a game & product designer. And I’d like to ask you a question.
  3. 3. What is the main difference between the two activities?
  4. 4. Well, unlike TV… Games and interactive systems require their users to...
  5. 5. Constantly analyze the system and make decisions (Which color to tap?)
  6. 6. These decisions lead to actions... (Obviously the disco balls…)
  7. 7. And these actions are followed by some feedback from the system. (Yippeee!)
  8. 8. These are the 3 legs of interaction. I also call it the Interaction Loop. Analysis + Decision Action Feedback
  9. 9. This loop cycles dozens of times during the activity. Analysis + Decision Action Feedback
  10. 10. Analysis + Decision Action Feedback Every leg in the loop has major effect on the UX. But today, we will focus on the analysis & decision part.
  11. 11. You will see how the decisions difficulty and the frequency of interaction define user experience.
  12. 12. Working with these two elements, you will be able to design specific, emotion-first user experiences
  13. 13. And innovate cleverly with new products and features.
  14. 14. The Interaction Mapping Tool Say hello to
  15. 15. The Interaction Mapping Tool is a simple, 2 axis graph that reflects on decisions and the frequency of interaction.
  16. 16. The Y axis is the decisions meter, ranging from automatic decisions to hard decisions.
  17. 17. Take this picture for instance
  18. 18. Have you started spot the differences? (You probably have)
  19. 19. Were you aware of the decision to start doing so? (Probably not)
  20. 20. You just took an AUTOMATIC decision you were not even aware of. As most of our decisions are.
  21. 21. What types of decisions we take automatically? Gas or brake? Pattern scanning First impression Skip button Fight or flee?
  22. 22. 13 x 18 =
  23. 23. 13 x 18 = If you are like me, you’ve probably decided NOT to solve this equation.
  24. 24. 13 x 18 = 234 A decision that you were aware of.
  25. 25. You were asked to perform a task that requires brain power. By any means - not automatic.
  26. 26. Nobel prize winner Daniel Kahneman called it “slow thinking”, as opposed to automatic, fast thinking. (Awesome book!)
  27. 27. What type of decisions require slow thinking? Calculations Too much information Where to build? High stake decisions Too many options
  28. 28. Back to the our model. The slower you think, the harder it is to make a decision. Therefore, the Y axis range from automatic (so easy we don’t even think about it), to hard.
  29. 29. Hard decisions. Analyzing the board takes a lot of brain power. Deep planning and strategizing are a must. CHESS is a game with hard thinking involved.
  30. 30. BEJEWELED BLITZ is a lightweight game. Automatic decisions. Little planning is involved, analyzing the board is done automatically.
  31. 31. The X axis represents the frequency of interaction. The amount of interactions expected from the user over a short period of time.
  32. 32. Chess is a game of Bejeweled Blitz is a game of many automatic decisions
  33. 33. The decisions’ difficulty and the frequency of interactions define the user experience.
  34. 34. Let’s divide our model to quadrants to better comprehend it.
  35. 35. Activities in the top left corner require a few hard decisions. Few Hard Decisions Turn based games Poker Chess
  36. 36. The experience is slow paced, and users’ spend a lot of time thinking about their next move. Turn based games Poker Chess Analysis + Decision Action Feedback FEW CYCLES DURING THE ACTIVITY Few Hard Decisions
  37. 37. The Few Hard Decisions quadrant Decisions’ type + What should I do next? + Where should I go? + Should I buy this item at this price? + What should I write? Perceived User Experience + Problem solving + Contemplative + Detail oriented + Thorough + Slow
  38. 38. Activities on the bottom right corner require many automatic decisions. Many Automatic Decisions Shooter games Hyper casual games Casual games
  39. 39. Shooter games Hyper casual games Casual games Analysis + Decision Action Feedback These activities are fast and deliver a lot of action-related feedback to the user. MANY CYCLES DURING THE ACTIVITY Many Automatic Decisions
  40. 40. The Many Automatic Decisions quadrant Decisions’ type + Swipe left or right? + Do these patterns match? + Decide when to tap/swipe Perceived User Experience + Repetitive + Fast + Frantic at times (shooters) / Soothing at times (casual) + Automatic - instinctive + Saturated with action-related feedback
  41. 41. Most products “live” in these two quadrants. But the other two are important nonetheless. Many Automatic Decisions Few Hard Decisions
  42. 42. Many Hard Decisions - is the top right quadrant. Many Hard Decisions
  43. 43. This is where training and learning happens. Many Hard Decisions
  44. 44. Think about Tetris and the brain power that is required from the player who plays it.
  45. 45. As players get better at the game, they begin to slide down towards the Many Automatic Decisions quadrant. Many Automatic Decisions Many Hard Decisions The process of turning hard decisions to automatic is, in fact, skill building.
  46. 46. The Many Hard Decisions quadrant Decisions’ type (same as in Few Hard Decisions, but fast) + What should I do next? + Where should I go? + Should I buy this item at this price? + What should I write? Perceived User Experience + Uncomfortable + Exhausting at times + Intense + Challenging + Daunting at times
  47. 47. Few Automatic Decisions Activities at the bottom left quadrant require a Few Automatic Decisions.
  48. 48. Few Automatic Decisions The user experience is semi lean-back. Think about gazing at a slot machine reels… or at the Facebook feed
  49. 49. Few Automatic Decisions The link between the action and the feedback is week (we do not have control over the reels or on the content of the feed), thus the activity is saturated with non-action-related feedback. Analysis + Decision Action Feedback
  50. 50. The Few Automatic Decisions quadrant Decisions’ type + Tap one more time? + Scroll further? + Zap the channel? Perceived User Experience + Almost lean-back + Gaze + Compulsive + Addictive + Saturated with non-related-action feedback
  51. 51. The Interaction Mapping Tool Many Automatic Decisions Few Hard Decisions Few Automatic Decisions Many Hard Decisions
  52. 52. Now that you’ve learned the model, let’s see some real world applications to it.
  53. 53. Take a dating app such as OKcupid for example.
  54. 54. This app belongs to the upper left quadrant, as “what should I write to this person” and “How should I approach” are not lightweight decisions. Few Hard Decisions
  55. 55. What happens if we drag OKcupid over to the bottom right corner? Few Hard Decisions Many Automatic Decisions
  56. 56. We should reduce the decisions’ difficulty, and add a lot of interaction points for the user. Few Hard Decisions Many Automatic Decisions
  57. 57. This is TINDER. Few Hard Decisions Many Automatic Decisions
  58. 58. The overall trend these days is to transform “few hard decisions” products to “many automatic decisions”, as we, humans, prefer to save brain power. Few Hard Decisions Many Automatic Decisions
  59. 59. But there’s much more to it. Think how innovation can happen when dragging standart MOOC apps a little bit to the right? Few Automatic Decisions Many Hard Decisions
  60. 60. Smartly happens. A beautiful app that teaches courses in management and economy, enforcing interaction every 5-7 seconds. Many Hard Decisions
  61. 61. To generate creative ideas using the model, ask yourself how you can add or reduce complexity to the decision making process
  62. 62. And how to add or reduce to the interaction frequency.
  63. 63. How would an e-commerce app look at the bottom right quadrant? Many Automatic Decisions Few Hard Decisions Few Automatic Decisions Many Hard Decisions ? Standard ecommerce
  64. 64. How can we teach a online creative course in an interactive way? Many Automatic Decisions Few Hard Decisions Few Automatic Decisions Many Hard Decisions ? Standard online course
  65. 65. How can our onboarding can actually teach users how to use our service? Many Automatic Decisions Few Hard Decisions Few Automatic Decisions Many Hard Decisions Meaningful onboarding skip>
  66. 66. Here’s another important thing regarding the model. Most products should have presence in at least two quadrant.
  67. 67. Tinder’s core experience is swiping left/right. Many Automatic Decisions Few Hard Decisions Few Automatic Decisions Many Hard Decisions
  68. 68. But writing a message adds complexity to the decision making progress. This adds depth to the product. Many Automatic Decisions Few Hard Decisions Few Automatic Decisions Many Hard Decisions
  69. 69. Off course, Tinder offers help to reduce difficulty for struggling users ... ...
  70. 70. We see this trend in many classic upper left quadrant products like emails, that utilize bottom right features.
  71. 71. Bigger products tap into every quadrant to cater to different moods and use cases Many Automatic Decisions Few Hard Decisions Few Automatic Decisions Many Hard Decisions What should I write as a status? In the midst of a FB argument Like | Love | Haha | CryKeep on scrolling?
  72. 72. Though eventually most of them end up on the bottom left, Zombifying their users. Many Automatic Decisions Few Hard Decisions Few Automatic Decisions Many Hard Decisions
  73. 73. One of the reasons that these products mentioned above are so successful is that they are compatible to their users’ state of mind.
  74. 74. At the end of a long day, or during tasks at work, many people are in a “few automatic decisions” mindset.
  75. 75. When they feel the need to control the pace of feedback, they cross over to the “Many automatic decisions” quadrant, where casual games await.
  76. 76. One of the mistakes that product designers do, is to fail to understand their users state of mind when the interact with their product.
  77. 77. Trying to add features of heavy decisions (skill) to a slot-machine crowd,
  78. 78. Or trying to teach skills people who think they are up to it, but actually prefer staying in the automatic thinking mode.
  79. 79. When using the model, imagine the context of the people using it. Office? Toilet? End of day?
  80. 80. What is their state of mind? Are they in an automatic mode? Or they have time to dig in deep?
  81. 81. Think about yourself. What is your state of mind when you use a social app, or play a certain game?
  82. 82. The expected state of mind will serve as your northern star when adjusting the intensity and difficulty of the experience you are crafting.
  83. 83. Remember. the decisions’ difficulty and the frequency of interaction define user experience.
  84. 84. Thank you for reading! Find more www.doriadar.com

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