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.

1. YOU WILL NEVER LOOK
INTERACTIVE MEDIA
~the same~
AFTER THIS DECK
at

2. Hi! I’m Dori.
I’m a game & product designer.
And I’d like to ask you a question.

3. What is the main difference
between the two activities?

4. Well, unlike TV…
Games and interactive systems require their users to...

5. Constantly analyze the system and make decisions
(Which color to tap?)

6. These decisions lead to actions...
(Obviously the disco balls…)

7. And these actions are followed by
some feedback from the system.
(Yippeee!)

8. These are the 3 legs of interaction.
I also call it the Interaction Loop.
Analysis
+
Decision
Action
Feedback

9. This loop cycles dozens of times during the activity.
Analysis
+
Decision
Action
Feedback

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. You will see how the decisions difficulty and the frequency
of interaction define user experience.

12. Working with these two elements, you will be able to
design specific, emotion-first user experiences

13. And innovate cleverly with new products and features.

14. The Interaction Mapping Tool
Say hello to

15. The Interaction Mapping Tool is a simple, 2 axis graph that
reflects on decisions and the frequency of interaction.

16. The Y axis is the decisions meter, ranging from
automatic decisions to hard decisions.

17. Take this picture for instance

18. Have you started spot the differences?
(You probably have)

19. Were you aware of the decision to start doing so?
(Probably not)

20. You just took an AUTOMATIC decision
you were not even aware of.
As most of our decisions are.

21. What types of decisions we take automatically?
Gas or brake?
Pattern scanning
First impression
Skip button
Fight or flee?

22. 13 x 18 =

23. 13 x 18 =
If you are like me, you’ve probably decided
NOT to solve this equation.

24. 13 x 18 = 234
A decision that you were aware of.

25. You were asked to perform a task that requires brain
power. By any means - not automatic.

26. Nobel prize winner Daniel Kahneman called it “slow
thinking”, as opposed to automatic, fast thinking.
(Awesome book!)

27. What type of decisions require slow thinking?
Calculations
Too much information
Where to build?
High stake decisions
Too many options

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. 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. BEJEWELED BLITZ is a lightweight game.
Automatic decisions. Little planning is
involved, analyzing the board is done
automatically.

31. The X axis represents the frequency of interaction.
The amount of interactions expected from the user
over a short period of time.

32. Chess is a game of
Bejeweled Blitz is a game of many automatic decisions

33. The decisions’ difficulty and the frequency of
interactions define the user experience.

34. Let’s divide our model to quadrants
to better comprehend it.

35. Activities in the top left corner require a few hard decisions.
Few Hard Decisions
Turn
based
games
Poker
Chess

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. 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. Activities on the bottom right corner
require many automatic decisions.
Many Automatic Decisions
Shooter
games
Hyper
casual
games
Casual
games

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. 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. Most products “live” in these two quadrants.
But the other two are important nonetheless.
Many Automatic Decisions
Few Hard Decisions

42. Many Hard Decisions - is the top right quadrant.
Many Hard Decisions

43. This is where training and learning happens.
Many Hard Decisions

44. Think about Tetris and the brain power that is required from
the player who plays it.

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. 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. Few Automatic Decisions
Activities at the bottom left quadrant require
a Few Automatic Decisions.

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. 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. 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. The Interaction Mapping Tool
Many Automatic Decisions
Few Hard Decisions
Few Automatic Decisions
Many Hard Decisions

52. Now that you’ve learned the model,
let’s see some real world applications to it.

53. Take a dating app such as OKcupid for example.

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. What happens if we drag OKcupid over to the bottom right corner?
Few Hard Decisions
Many Automatic Decisions

56. We should reduce the decisions’ difficulty,
and add a lot of interaction points for the user.
Few Hard Decisions
Many Automatic Decisions

57. This is TINDER.
Few Hard Decisions
Many Automatic Decisions

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. 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. Smartly happens. A beautiful app that teaches courses in management and
economy, enforcing interaction every 5-7 seconds.
Many Hard Decisions

61. To generate creative ideas using the model,
ask yourself how you can add or reduce complexity to the
decision making process

62. And how to add or reduce to the interaction frequency.

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. 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. 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. Here’s another important thing regarding the model.
Most products should have presence in at least two
quadrant.

67. Tinder’s core experience is swiping left/right.
Many Automatic Decisions
Few Hard Decisions
Few Automatic Decisions
Many Hard Decisions

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. Off course, Tinder offers help to reduce difficulty for struggling users
...
...

70. We see this trend in many classic upper left quadrant
products like emails, that utilize bottom right features.

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. 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. One of the reasons that these products mentioned above
are so successful is that they are compatible to their users’
state of mind.

74. At the end of a long day, or during tasks at work,
many people are in a “few automatic decisions” mindset.

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. 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. Trying to add features of heavy decisions (skill)
to a slot-machine crowd,

78. Or trying to teach skills people who think they are up to it,
but actually prefer staying in the automatic thinking mode.

79. When using the model, imagine the context of the people
using it. Office? Toilet? End of day?

80. What is their state of mind? Are they in an automatic mode?
Or they have time to dig in deep?

81. Think about yourself. What is your state of mind when you
use a social app, or play a certain game?

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. Remember. the decisions’ difficulty and the
frequency of interaction define user
experience.

84. Thank you for reading!
Find more www.doriadar.com