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Maurits was invited to give a talk on smart ways of using Big Data.
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Big Data @ PersuasionAPI
Maurits Kaptein
Co-founder / Chief Scientist Science Rockstars
www.persuasionapi.com
Big Data?
Big data is not really defined.
“Datasets that are larger than „common‟
machines can handle”
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
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
Challenge 1:
What is meaningful?
What is meaningful
Depends obviously on what your aim is as a
company.
We help companies increase conversion
(Click-through, sales, etc.)
Persuasion plays a big role:
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6 Principles of Persuasion
8
8
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Persuasion Online
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Should we use all the strategies we
can think off?
At the same time?
For the same product?
Comparing many strategies with
single strategies
0.000 0.002 0.004 0.006 0.008 0.010
05
00
100
020
00
300
0
Click probability
Density
Should we use all the strategies
we can think of?
No, we are better of selecting a
specific one.
Should we use the same strategies
for everyone?
Strategies not equally
effective for
everyone?
Large differences
based on personality
traits
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2 Scenarios:
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Effect of using a strategy
Ave
rag
e
Individuals
+-
Individuals
Effect of using a strategy
Ave
rag
e
+-
Should we use the same
strategies for everyone?
No, people are distinct in their
reactions to different strategies.
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}
Challenge 2:
Moving from analysis to advice
Choose not to produce reports after
logging responses…
But rather summarize all the data
to be available for direct
recommendations.
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Persuasion Profile:
•A persuasion profile is a collection of the
estimates of the effect of persuasion principles
for each individual user
Normal Page:
A1 (Scarcity):
A2 (Authority):
A3 (Consensus):
Effect
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Normal Page:
A1 (Scarcity):
A2 (Authority):
A3 (Consensus):
Effect
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We log the success of each attempt
• 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.
Normal Page:
A1 (Scarcity):
A2 (Authority):
A3 (Consensus):
Effect
20
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We improve the personal profile
• 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
Normal Page:
A1 (Scarcity):
A2 (Authority):
A3 (Consensus):
Effect
21
Normal Page:
A1 (Scarcity):
A2 (Authority):
A3 (Consensus):
Effect
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User navigates, we improve
And so on, for each individual client...
Real time analytics is most effective in predicting
behavior
Normal:
A1:
A2:
A3:
Effect
First page served:
Normal:
A1:
A2:
A3:
Effect
Second page served:
Normal:
A1:
A2:
A3:
Effect
Third page served:
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Competing Principles
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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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Challenge 2:
We provide “advice” stating which
Strategy to Use for your current
customer.
In between page views…
Challenge 3:
How do we deal with all the data?
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
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.
Challenge 3:
How do we deal with all the data:
Use online learning and split
different levels of the model
Slide with the towell example
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Results
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Increase in email click through: >100%(at the 5th reminder)
Increase in e-commerce revenue: >25%
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.
End.
Thanks!
Contact us at:
+31 621262211
www.sciencerockstars.com
