Scott Porter, VP, Methods, and Aliza Pollack, VP Qualitative, led a session at the Planningness 2015 unconference. Scott works in marketing analytics, and regularly uses Artificial Intelligence algorithms to sort through large quantities of data to find plausible causal models for the interrelation of drivers, outcomes, and intermediate or mediating variables. In facilitating discussions between marketers and modelers, Scott has realized that the process of gearing up to work with AI can help us think better. Computer algorithms have the advantage when sorting through large amounts of data, but they have their limitations. With current technology, artificial intelligence has to sort through the data it is given--it generally can’t intuit missing data that would be important. However, as humans, we excel at this sort of intuition. Or, at least we can... we have to overcome our human tendency to stop when we've uncovered the first plausible answer. We shared a structured approach to brainstorming that forces us to push wider to additional context that might be important. These exercises (looking for multiple causes, looking for side effects, looking for missing causes) are the steps we would need to go through in order to select the right data for a computer to have sufficient information to build a quantified model. However, the steps are useful regardless of whether or not we later quantify the model, because the techniques help us push beyond where we would normally stop because we found a single reasonable explanation. After going through an overview of the theory and the process, we put it into practice. We took turns leading small groups in structured hypotheses sessions to systematically unpack potential complexities of real client challenges shared by members of the session, and brainstorm what information we (or algorithms) will need to better understand potential opportunities. Speaker note annotations are available within the deck if you download the pdf (orange boxes at the top left of each slide).

2. an insight agency
focused on Strategic
Marketingthat works.

3. Aliza Pollack
talks to humans
about humans
Scott Porter
talks to computers
about humans

4. Dealing
with Flux
Creating change
Responding to change

5. Success stories
Data
Macro trends
Data mining
Outliers
Human
Had an insight
Had an experience
Intuition

6. Predictive Analytics
Predictivemodel

7. Past evidence
Did not
hurry
hurried
Arrived
on time
Arrived
late

8. Prediction
Predictive
model
Willbelate
Won’tbelate

9. Predictive modelingis not enough
New policy: Expectation:
Late
1 in10
meetings
baseline:
Late
less
often
“Stop hurrying… hurrying
is linked with being late”.

10. Predictive modelingis not enough
New policy: Expectation:
Late
1 in10
meetings
Late
5 in 10
meetings
baseline:
Late
less
often
“Stop hurrying… hurrying
is linked with being late”.
result:

11. What went wrong?

12. Identifyingwhat
might matter
Tireless
Difficultyprocessing
complexity
Tend to single
solution
No context
Don’t recognize
sillyanswers

13. Knowledge representation
vs. prediction.
Usable by both computers
and humans

14. Easing human communications, too
X5
X4
X3 X2
X1
? answer
Blackbox approachrequires
a game of telephone.
Graphicalmodels facilitatediscussion
andcollaboration.

15. What are our roles?
Human:
Brainstorm
potential inputs
Computer:
search
possible models
trust
Attribute 1
favorability
Attribute 2
Other
outcome
trust
Attribute 1
favorability
Attribute 2
Other
outcome

16. What’s in the diagrams?
behavior
perception
outcome
event
intention
Variables (thingswe already
measure or things we think we
should start measuring)
Relationships between variables… arrows
from causes to effects (guesses fine…
don’t have to besure about)
1 2

17. Don’t forget our goal
Identify what is
under your control.
Identify what
could change.
We can easilychoose to
change this.
You can’t control this, but you
might suspect it could change.

18. Which is more actionable?
Why?

19. Hypothesis Generation
Step 1.
Start with the outcome you hope to change.

20. Hypothesis Generation
Step 1.
Start with the outcome you hope to change.
Step 2.
For every hypothesized variable, add at least 2
causes.

21. Hypothesis Generation
Step 1.
Start with the outcome you hope to change.
Step 2.
For every hypothesized variable, add at least 2
causes.

22. Hypothesis Generation
Step 1.
Start with the outcome you hope to change.
Step 2.
For every hypothesized variable, add at least 2
causes.
Step 3.
For every hypothesized variable, add at least 1
side effect.
$

23. Hypothesis Generation
Step 1.
Start with the outcome you hope to change.
Step 2.
For every hypothesized variable, add at least 2
causes.
Step 3.
For every hypothesized variable, add at least 1
side effect.
Step 4.
Find at least 2 items that have a common
cause. Add that cause.
$
repeat

24. Can we draw
what we think is
going on…