Maurits was invited to give a talk on smart ways of using Big Data.

1. Big Data @ PersuasionAPI
Maurits Kaptein
Co-founder / Chief Scientist Science Rockstars
www.persuasionapi.com

2. Big Data?
Big data is not really defined.
“Datasets that are larger than „common‟
machines can handle”

3. What I will and won’t talk about
Yes: What are the challenges that are
associated with big data
Yes: How did we solve them in PersuasionAPI
(high level)
No: Algorithms
No: Infrastructure / Technical details

4. 3 Key Challenges
• Focus on meaningful data
• So much data, but which is useful?
• Move from Analytics to Advice
• No reports in hindsight but direct responses
• Inability to run analysis on all of the data
• Need for summaries / online learning

5. Challenge 1:
What is meaningful?

6. What is meaningful
Depends obviously on what your aim is as a
company.
We help companies increase conversion
(Click-through, sales, etc.)

7. Persuasion plays a big role:

8. 6 Principles of Persuasion
8
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9. Persuasion Online
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10. Should we use all the strategies we
can think off?
At the same time?
For the same product?

11. Comparing many strategies with
single strategies
3000
2000
Density
500 1000
0
0.000 0.002 0.004 0.006 0.008 0.010
Click probability

12. Should we use all the strategies
we can think of?
No, we are better of selecting a
specific one.

13. Should we use the same strategies
for everyone?
Strategies not equally
effective for
everyone?
Large differences
based on personality
traits

14. 2 Scenarios:
Average
Average
Individuals Individuals
- + - +
Effect of using a strategy Effect of using a strategy
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15. Should we use the same
strategies for everyone?
No, people are distinct in their
reactions to different strategies.

16. Challenge 1:
Meaningful data
Identify Persuasive Strategies
Select distinct strategies
Adapt to individuals
Data:
{ userId : “zcvx2312”, strategyId : 4,
implementation: 32, estimatedSucces : 0.23,
certainty : 0.013}

17. Challenge 2:
Moving from analysis to advice

18. Choose not to produce reports after
logging responses…
But rather summarize all the data
to be available for direct
recommendations.

19. Persuasion Profile:
Normal Page:
A1 (Scarcity):
A2 (Authority):
A3 (Consensus):
Effect
•A persuasion profile is a collection of the
estimates of the effect of persuasion principles
for each individual user
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20. We log the success of each attempt
Normal Page:
A1 (Scarcity):
A2 (Authority):
A3 (Consensus):
Effect
• Based on the dynamic image and the link we can monitor the
success of each page served to a user.
• We will keep updates of the average performance of your served
page variations, and of the performance for each client.
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21. We improve the personal profile
Normal Page:
A1 (Scarcity):
A2 (Authority):
A3 (Consensus):
Effect
• Based on the response of each client we will update our advice for that user
• The new advice is a combination of the response of that client, as well as that of
other clients
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22. User navigates, we improve
First page served: Second page served: Third page served:
Normal: Normal: Normal:
A1: A1: A1:
A2: A2: A2:
A3: A3: A3:
Effect Effect Effect
And so on, for each individual client...
Real time analytics is most effective in predicting
behavior
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23. Competing Principles
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24. Example of adjusted page
1: Log Client ID (e.g. via
dynamic image, cookie, etc)
2. Link(s) to log success of
the Sales Strategy
3. Hooks to log non-
responsiveness to a Sales
Strategy
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25. Challenge 2:
We provide “advice” stating which
Strategy to Use for your current
customer.
In between page views…

26. Challenge 3:
How do we deal with all the data?

27. Problem 1: Impossible fitting to all
of the data in memory
Move fully to “online” learning:
Handle datapoint for datapoint
Do not focus on ( theta | data ) but rather on ( theta |
prior(s) )
• Summarize all meaningful info in the priors.
Find out what data you need and don’t need to make
an impact on the bottom line.
• E.g. no demographic data
Use M/R jobs for re-estimating

28. Problem 2: Individual level
estimates are needed fast
Use hierarchical models:
Aggregated level => Input for new users
User level => Start model for known users
Apply shrinkage
Link the two levels
Use user-level model in isolation if necessary
Analytical updates thus very fast.

29. Challenge 3:
How do we deal with all the data:
Use online learning and split
different levels of the model

30. Results
Slide with the
Increase inexample through:
towell email click
(at the 5th reminder)
>100%
Increase in e-commerce revenue: >25%
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31. My Big Data considerations:
Focus on meaningful data: Persuasion at an
individual level.
Move from analytics to real time response:
Provide real-time advice
Inability to analyze all of the data: Use online
learning and hierarchical models.

32. End.
Thanks!
Contact us at:
maurits@sciencerockstars.com
+31 621262211
www.sciencerockstars.com