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©2020 Morningstar, Inc. All rights reserved.
Behavioral Science for Data Scientists
Steve Wendel
Author of Designing for Behavior Change
Head of Behavioral Science at Morningstar
@sawendel
2
3
1. What is Behavioral Science?
2. How BeSci and Data Science Differ
3. How You Can Combine Them
4. Q&A
Where we’re going
4
1. What is Behavioral Science?
2. How BeSci and Data Science Differ
3. How You Can Combine Them
4. Q&A
What’s Next
5
6
7
8 www.behavioraltechnology.co
9
People often face a gap, between their
intentions and their actions
Procrastinating
Being afraid
to fail
Lacking willpower
Being busy and forgetful
Worrying about
what others might
think
12
We look to other people
for guidance on what to
do.
Even when it’s the wrong
thing to do.
Descriptive Norms (and Herding Behavior)
13
Temporal Myopia (Present Bias)
14
We overestimate our strength and ability,
each believing we’re “above average”
Which is reinforced with selective attention
and processing – confirmation bias
See Moore and Healy (2008)
Overconfidence Bias
Our users face these challenges with our products
An understanding of how people decide & act
What is behavioral science?
Tools to potentially help people do better
Rigorous testing to find results
16
1. What is Behavioral Science?
2. How BeSci and Data Science Differ
3. How You Can Combine Them
4. Q&A
What’s Next
17
Data science generally seeks to understand
and to predict (and many other things)
The main difference:
Behavioral science seeks to
change human behavior
18
identify risk factors
predict likelihood to buy
decrease behavioral risk
increase likelihood to buy
20 obvious effecthidden patterns
1. What is Behavioral Science?
2. How BeSci and Data Science Differ
3. How You Can Combine Them
4. Q&A
What’s Next
21
#1: By looking at the data differently
23
Is what you’re seeing
driven by a conscious
choice? Or a bias?
Descriptive Norms (and Herding Behavior)
Is it a lack of interest, or a gap
between intention and action?
What is driving your client’s behavior?
A behavioral guide to what to look for.
Waiting doesn’t
have to be bad,
with the Labor
Illusion
Make the Future into the Present
From Goldstein et al.
Temptation Bundling:
Combine the good and the good for you
See Milkman et al. 2013
#2: By looking for different data
30
To analyze descriptive norms,
you need a different a
different type of data
(reference groups, social
relationships, and level of
prior experience)
A Behavioral plan
Qual & quant research
that maps out the status
quo & the micro-
behaviors from there
to action.
To understand the
obstacles at each step.
31
32
Condition Current Status? Obstacle?(Y orN)
Cue to think about taking
action
E.g. Relevance of email is unclear. E.g. Yes
Emotional
Reaction E.g. Aiming for positive. E.g. No
Conscious Evaluation of
costs and benefits
E.g. Long, multi-step sign-up process. E.g. Yes
Ability to act
(resources, logistics,
self-efficacy)
E.g. Ensure all users feel capable E.g. No
Timing and urgency
to act E.g. No urgency or commitment E.g. Yes
Prior Experience taking
action
E.g. Varied E.g. Not sure
33
a very clear signal,
when you have the data
#3: By explicitly moving from data analysis
to behavior change
35
If you want to stop herding
behavior, there are existing
techniques that can help.
Specific interventions to
match each problem
37
www.behavioraltechnology.co
#4: By better understanding our
own behavior
Image from Ganna Pogrebna
1. What is Behavioral Science?
2. How BeSci and Data Science Differ
3. How You Can Combine Them
4. Q&A
What’s Next
41
People often face a gap, between their
intentions and their actions
43
identify risk factors
predict likelihood to buy
decrease behavioral risk
increase likelihood to buy
What’s stopping your clients?
A behavioral guide to what to look for
Waiting isn’t
as bad
with the Labor
Illusion
A range of interventions
to address common problems
Image from Ganna Pogrebna
49
www.behavioraltechnology.co

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