This is a case study of 3 projects with a seemingly trivial task in common: how do we show users meaningful suggestions of what they can buy in a web store without knowing much about them? The first project is about us struggling to design meaningful suggestions using conventional patterns. In the second, we shifted to a conversational UI and saw its mechanics change the dynamics of interaction. In the third project, we had an epiphany where it became apparent just how much designing conversational UIs is tied to understanding language, speech acts and the psychology of human behaviour. We will look at how much conversational UIs differ from traditional UIs, like steppers or forms, and what makes them better - or worse. We'll look at how the conversations can be prototyped and how speech act theory can help us in designing better interfaces. A good part of the session will be spent with Bobby, the chatbot we created to help users find the perfect gift, and with the psychology behind the bot. Apart from the chatbot itself, we we also made a prototyping tool to help us design the conversations.

1. Show me something nice!
Memi Beltrame

2. Memi Beltrame
I make things fun
and easy to use.

3. http://protostrap.ch

4. Show me something nice!
3 studies on making buying suggestions

5. Based on my behaviour and that
of users watching similar things

6. Not all sites have
that kind of data

7. Let’s look at suggestions
on starting pages

9. Are these meaningful &
relevant suggestions?

10. New Seasonal
SeasonalNew
Are these meaningful &
relevant suggestions?
Deals Random
Deals
Brands
Random

11. New Seasonal
SeasonalNew
Maybe in terms of how their
business is structured
Deals Random
Deals
Brands
Random

12. Can we make
meaningful & relevant
suggestions?
If so: How?

13. figuring out Taste

14. Test 1:
Endless Stream of
Random Stuff
Focus on
Discovery

16. User feedback:
Why are you
showing me this?
It’s great to
browse but what
has this to
with me?

17. Favorites
Bought Sold
last items
similar
Other
suggestions
Irrelevance

18. UX trivia #1
Users don’t care about
things they don’t care about

19. Test 2:
User Groups
For All
Mamas & Papas
Geeks
Gourmets
Collectors
Fashionistas
Gardeners
Hunters
Home Improvers
Runners & Hikers
✕ close

20. User Groups
Users often can’t see
themselves in a group
"Where are the normal people?"

21. User Groups
Users don’t want to be reduced
to a group.
"What happens if I choose one?
Will I miss out on the others?"

22. Test 3:
Configuration

24. Configuration
Users welcome it
but will not use it.
"It’s interesting but
I can’t be bothered."

25. How people make
major purchases

26. How do people buy
a car or a house?

27. Phase I Phase II Phase III
Discovering Learning Hunting
Questions
• What do I need?
• What is the offer?
• What matches my needs?
• What is a good offer?
• What do I have to expect?
• Is this a good offer?
Activities
• Research
• First Search
• Discovery
• Comparing
• Collecting
• Test Driving (Does this model
match?)
• Playing Around
• Observing
• Test Driving (Does this
car match?)
• Deciding
Knowledge
Basic Needs • Exclusion Criteria
• Range Spektrum
• Awareness of Criteria
• Knowing exact needs
• Clarity
• Ability to make a
decision to buy
3 Buying Phases

28. Entry points for different phases
What’s your type of car?
Don’t know what car you
should be looking for?
Answer a few questions and
get suggestions to get you
started .
Start wizard
1
2
New entries for your saved searches
Family Car 5 Seats, Station Wagon, Max Price 15’000, Max Milage 20’000km 3

29. Help users to discover
their basic needs
SuggestionsUsagePurposeBudgetStowageSeats
How much stowage space do you need?
Just for groceries For a trip for two We’re a large family I often move stuff

30. Make suggestions to
get the users going
Our suggestions to get you going
Seats Stowage Budget Purpose Usage Suggestions

31. Switching to
conversational ui

32. Mobile
The "chatbots are
better steppers"
assumption

34. Stepper vs Chatbot
• Users engaged with the chatbot
• were quicker in going through
the steps
• Found the interaction natural
• but preferred the stepper over
the chatbot

35. The Power of Decision Trees
Just translating the stepper to a
chatbot leaves out all the
possibilities conversational
interfaces offer.

36. Human vs Bot
Using Photos is
misleading.
Users want to know who
they’re talking to

37. Making the conversation
feel natural
Adding hesitation markers
("fake writing") makes the
conversation more realistic.

38. Making the conversation
feel natural
Following the pattern of
Turn-taking:
- Pick up the thread (response)
- Move along (follow-up)

39. Hesitation marker Response &
Hesitation marker
Follow-up
Response structure

40. Finding out how users tick

41. Cambridge Analytica

42. Article in the Tages-Anzeiger Magazine:
https://www.dasmagazin.ch/2016/12/03/ich-habe-nur-gezeigt-dass-es-die-bombe-gibt/
Article on Nieman Lab:
http://www.niemanlab.org/2017/05/cambridge-analytica-trump-and-the-new-old-fear-of-manipulating-the-masses/

43. Ocean Model
The Big Five
Five broad dimensions
used to describe the human
personality and psyche.

44. Ocean Model
The Big Five
Openness
Conscientiousness
Extraversion
Agreableness
Neuroticism

45. Ocean Model
The Big Five
Personality Trait High Scorers Low Scorers
Openness Imaginative Conventional
Conscientiousness Organized Spontaneous
Extraversion Outgoing Solitary
Agreableness Trusting Competitive
Neuroticism Prone to
stress & worry
Emotionally stable

46. Ocean Model
What’s the point of it?
If you can describe a
personality you can
respond to their impulses,
fears, sense of duty etc.

47. Be responsible and
don’t misuse it.

48. Don’t be like
this Asshole

49. Last Christmas

50. How to find
the perfect present.

53. Of course there
is a button
labelled
Weirder!

56. Personality Trait High Scorers Even Low Scorers
Openness Imaginative Conventional
Conscientiousness Organized Spontaneous
Extraversion Outgoing Solitary
Agreableness Trusting Competitive
Neuroticism Prone to
stress & worry
Emotionally
stable
What personality does
this product reflect?

57. Feedback:
Users liked how well the gift
matched most of the time.
And the weirder button.
Overwhelming

58. Thank you!
I’m @bratwurstkomet on Twitter
Prototypes made with Protostrap
http://protostrap.ch