Successfully reported this slideshow.
We use your LinkedIn profile and activity data to personalize ads and to show you more relevant ads. You can change your ad preferences anytime.

Measuring the benefit effect for customers with Bayesian predictive modeling

Strata 2015 London presentation
http://conferences.oreilly.com/strata/big-data-conference-uk-2015/public/schedule/detail/39592
https://github.com/cojette/CompareImpact

  • Login to see the comments

Measuring the benefit effect for customers with Bayesian predictive modeling

  1. 1. Measuring benefit effect for customers with bayesian prediction modeling Kwon, JeongMin (cojette@gmail.com) Strata + Hadoop World 2015, London
  2. 2. Offering
  3. 3. Offering • Popular way to promote products
  4. 4. Offering Process
  5. 5. Step 1: Customer Data Management Monitor customers’ actions Keeping track of customer data and information Defining goals
  6. 6. Step 2: Targeting Customer segmentation and selection with goals at step 1 Based on demographic information and log collections Statistical methods and data mining algorithms
  7. 7. Step 3: Offering Benefit (Campaign) • Delivering proper benefits to targeted customer groups • Methods: Promotion, Event, Advertisement and others • Measurement and prediction of campaign effects
  8. 8. How to measure and predict
  9. 9. How to compare offering effects
  10. 10. Multivariate Testing • Technique for testing a hypothesis with multiple variables • Issues for offering • Lack of long-term prediction • data, benefit limitations (Image from https://www.ownedit.com/features)
  11. 11. Bayesian Interpretation • Diachronic Interpretation • Probability of the hypotheses changes over time • Prior and posterior based on background information • Good for simulation, decision and prediction (Image from http://en.wikipedia.org/wiki/Bayes%27_theorem)
  12. 12. CausalImpact • Based on the paper [Inferring causal impact using Bayesian structural time-series models], Google, 2014 • CausalImpact Package in R • https://github.com/google/CausalImpact (Image from the paper)
  13. 13. CompareImpacts • Integration of bayesian time series prediction model with multivariate tests • For simple comparison of causal effects
  14. 14. Use Cases • Same offerings in various groups
  15. 15. Use Cases • Various offerings in a group
  16. 16. Basic Model • CausalImpact results
  17. 17. • Same offerings in three groups Use Case Results
  18. 18. • Offerings in a group with time differences Use Case Results
  19. 19. Office Hour: 15:25~16:05, Table A E-mail : cojette@gmail.com New Tool for Offering Comparison: Multivariate Test + Bayesian Time-Series Analysis

×