big data sessie maurits kaptein
DESCRIPTION
Info.nl organized a knowledge session on Big Data on August 9. In this presentation founder Maurits Kaptein of PersuasionAPI talks on the Big Data challenges.TRANSCRIPT
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
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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
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
Avera
ge
Individuals
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Individuals
Effect of using a strategy
Avera
ge
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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
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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
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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 necessaryAnalytical 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.