Big Data sessie Maurits Kaptein


Published on 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

    1. 1. Big Data @ PersuasionAPIMaurits KapteinCo-founder / Chief Scientist Science
    2. 2. Big Data? Big data is not really defined. “Datasets that are larger than „common‟ machines can handle”
    3. 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. 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. 5. Challenge 1:What is meaningful?
    6. 6. What is meaningful Depends obviously on what your aim is as a company. We help companies increase conversion (Click-through, sales, etc.)
    7. 7. Persuasion plays a big role:
    8. 8. 6 Principles of Persuasion 8 8Beta Launch presentations Q2 2012 8
    9. 9. Persuasion Online 9Beta Launch presentations Q2 2012 9
    10. 10. Should we use all the strategies wecan think off?At the same time?For the same product?
    11. 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. 12. Should we use all the strategieswe can think of?No, we are better of selecting aspecific one.
    13. 13. Should we use the same strategiesfor everyone? Strategies not equally effective for everyone? Large differences based on personality traits
    14. 14. 2 Scenarios: Average Average Individuals Individuals - + - + Effect of using a strategy Effect of using a strategy 14Beta Launch presentations Q2 2012 14
    15. 15. Should we use the samestrategies for everyone?No, people are distinct in theirreactions to different strategies.
    16. 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. 17. Challenge 2:Moving from analysis to advice
    18. 18. Choose not to produce reports afterlogging responses…But rather summarize all the datato be available for directrecommendations.
    19. 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. 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. 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. 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. 23. Competing Principles 23Beta Launch presentations Q2 2012 23
    24. 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. 25. Challenge 2:We provide “advice” stating whichStrategy to Use for your currentcustomer.In between page views…
    26. 26. Challenge 3:How do we deal with all the data?
    27. 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. 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. 29. Challenge 3:How do we deal with all the data:Use online learning and splitdifferent levels of the model
    30. 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. 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. 32. End. Thanks! Contact us at: +31 621262211