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How to Build an Attribution Solution in 1 Day

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I presented this at the London Measurecamp Conference, in September 2016. This is an overview on how to build an attribution solution with Python and Tableau. This is meant as a starter solution.

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How to Build an Attribution Solution in 1 Day

  1. 1. How to build an attribution solution in a day - well maybe a couple of days ;) Dr. Phillip Law
  2. 2. • By the end of this Talk I want to give you the tools and methods to be able to go away and build your own solution. • I started this on Monday and got something useable in a day, then it took me a couple of days to polish this up and iron out any bugs. • It’s not perfect, but give you an attribution solution with contextual data and importantly the power to slice and dice.
  3. 3. Overview • Blurb About Me and the Company I work For. • Quick Overview of Rules Based attribution. • Discuss the tools I used and why, this is effectively an ETL process using Python and Tableau. • How I extracted the data. • What Transformations I did using Python. • What Transformations I did in Tableau. • Run through the models in Tableau. • Limitations (Scalability) • Next Steps (Improve the models, Allocation of Credit, Bayesian attribution)
  4. 4. About me: Dr. Phillip Law
  5. 5. © 2016 YOUR FAVOURITE STORY ALL RIGHTS RESERVED .
  6. 6. To craft powerful digital experiences to help our clients grow Our Mission
  7. 7. CLIENTS LOGOS
  8. 8. 1. Improving Marketing Performance Improving conversion throughout the customer journey and reducing inefficiency. In a world where customer experience is the brand experience. Digital has great power. 2. Brand Building 3. Future Proofing It is particularly important to work to future web standards and consumer patterns to create lasting solutions.
  9. 9. Reporting & implementation (Adobe and GA) SEO & PPC Data Science (modelling) Data Visualisation (D3, Tableau) Growth Audits Optimisation (A/B Testing, Videos) Analytics
  10. 10. Attribution
  11. 11. Attribution Model Types (Sort of) RULES BASE BAYESIAN
  12. 12. Attribution Model Types? RULES BASE WEIGHTING 2. USE THE SHAPLEY VALUE (GAME THEORY) BAYESIAN MODELS
  13. 13. Types of Models 1.First Touch 2.Last Touch 3.Linear/Evens 4.Starter Player Closer 5.Temporal 6.Spatial
  14. 14. (Raw Data Feed) (Credit to Marketing Channels)
  15. 15. Right Tools for the Job
  16. 16. Transformation Visualise Raw Data Feed
  17. 17. Raw Data Feed Because of file size you’ll probably need to get it delivered to an FTP You can ask for the full data feed, this file is delivered hourly and contains all data, this file is Huge, you can get this delivered to D3 on the amazon cloud, which is nice 
  18. 18. Raw Data Feed Because of file size you’ll probably need to get it delivered to an FTP You can ask for the full data feed, this file is delivered hourly and contains all data, this file is Huge, you can get this delivered to D3 on the amazon cloud, which is nice 
  19. 19. Process this Data File in Python (4 Steps) (Did this whole thing in 140 lines of code) Step 1: Clean file (remove all page views where page views equals zero), flag fist touch point in visit, count page views in visits, create sort key. Step 2: Group by tracking ID, and sort by time (need to sort by the sort key), flag conversion event (Only one conversion Event per Visitor) Step 3: Read in file backwards, create attribution window, count touch points from conversion, write conversion time to the same row as the conversion touchpoint. Step 4: Re-order file and step three reversed the process.
  20. 20. Next Step to Open in Tableau
  21. 21. Next Steps
  22. 22. Fair Allocation of Credit
  23. 23. Traditional rules for assigning credit are arbitrary not and do not reflect the true weighing of a touch point. Weightings are skewed towards channels that retarget There is a method from game theory that has been mathematically proven to allocate credit in a fair way. Shapley Value
  24. 24. PPC Social Email $100 PPC Social $230
  25. 25. Email PPC Social $230 $50 PPC $90 Social $85 Email $100 PPC Social Email PPC Social Email $55 $150 $160 No Touchpoint PPC Social Email PPC Email Social Social PPC Email Social Email PPC Email PPC Social Email Social PPC PPC Social Email 40 10 130 40 60 80 15 35 130 70 35 75 95 80 5 70 105 5 55 54 71 Average
  26. 26. Only Considers People Who Convert
  27. 27. BAYESIAN
  28. 28. 𝑝(𝑃𝑃𝐶) 𝑝(𝑃𝑃𝐶) 𝑝(𝑐𝑜𝑛𝑣𝑒𝑟𝑡) 𝑝(𝑐𝑜𝑛𝑣𝑒𝑟𝑡) 𝑝(𝑐𝑜𝑛𝑣𝑒𝑟𝑡) 𝑝(𝑐𝑜𝑛𝑣𝑒𝑟𝑡) 𝑝(𝑃𝑃𝐶 ∩ 𝑐𝑜𝑛𝑣𝑒𝑟𝑡) 𝑝(𝑃𝑃𝐶 ∩ 𝑐𝑜𝑛𝑣𝑒𝑟𝑡) 𝑝(𝑃𝑃𝐶 ∩ 𝑐𝑜𝑛𝑣𝑒𝑟𝑡) 𝑝(𝑃𝑃𝐶 ∩ 𝑐𝑜𝑛𝑣𝑒𝑟𝑡)
  29. 29. 𝑝 𝑐𝑜𝑛𝑣𝑒𝑟𝑡 | 𝑃𝑃𝐶 𝑝 𝑐𝑜𝑛𝑣𝑒𝑟𝑡 | 𝑃𝑃𝐶 Probability they would have converted anyway Difference in probabilities is the impact that channel has on conversion ∆𝑝 Probability someone converts given that they have seen a PPC ad
  30. 30. Advantages • Using the Shapley value provides a more “true” allocation of the influence of channels. • Bayesian model takes into account user journey that don’t convert. Understand unconverted users that are the best prospect of conversion.
  31. 31. £5000
  32. 32. £750
  33. 33. 31st October
  34. 34. Any Questions Phillip.law@yourfavouritestory.com https://uk.linkedin.com/in/piplaw

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