Big Data sessie Maurits Kaptein
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Big Data sessie Maurits Kaptein

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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.

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.

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  • What is big data?Hype, but we don’t really compyHowever, there are somethingschaning , because we have so much data…
  • Briefly go through each of the six:Consensus (previous example)Liking (Similarity wallet example)Expertise (Milgram example)Commitment (Sign in garden example)Scarcity (Abundantly available example)Reciprocity (Free books example)
  • They are already used online (Scarcity, Concensus, Scarcity)
  • Talk through the two scenarios.
  • So for each user its an estimate of what works. Which then can subsequently be used to select content.

Big Data sessie Maurits Kaptein Presentation Transcript

  • 1. Big Data @ PersuasionAPIMaurits KapteinCo-founder / Chief Scientist Science Rockstarswww.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 8Beta Launch presentations Q2 2012 8
  • 9. Persuasion Online 9Beta Launch presentations Q2 2012 9
  • 10. Should we use all the strategies wecan think off?At the same time?For the same product?
  • 11. Comparing many strategies with single strategies 3000 2000Density 500 1000 0 0.000 0.002 0.004 0.006 0.008 0.010 Click probability
  • 12. Should we use all the strategieswe can think of?No, we are better of selecting aspecific one.
  • 13. Should we use the same strategiesfor 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 14Beta Launch presentations Q2 2012 14
  • 15. Should we use the samestrategies for everyone?No, people are distinct in theirreactions 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 afterlogging responses…But rather summarize all the datato be available for directrecommendations.
  • 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 19 19Beta Launch presentations Q2 2012
  • 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. 20 20Beta Launch presentations Q2 2012
  • 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 21 21Beta Launch presentations Q2 2012
  • 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 22 22Beta Launch presentations Q2 2012
  • 23. Competing Principles 23Beta Launch presentations Q2 2012 23
  • 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 24 24Beta Launch presentations Q2 2012
  • 25. Challenge 2:We provide “advice” stating whichStrategy to Use for your currentcustomer.In between page views…
  • 26. Challenge 3:How do we deal with all the data?
  • 27. Problem 1: Impossible fitting to allof 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 levelestimates 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 splitdifferent 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% 30Beta Launch presentations Q2 2012 30
  • 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