Attribution Modeling and Big Data, Google
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The document discusses the evolution of attribution modeling in marketing, comparing digital marketing today to financial practices in the 1980s. It emphasizes the challenges of measuring return on investment for various marketing channels and the importance of data in creating accurate attribution models. The need for detailed user interaction data and competitive environmental data to derive meaningful insights is also highlighted.
Attribution Modeling and Big Data, Google
- 1. Google Confidential and Proprietary 1Google Confidential and Proprietary 1 Attribution Modeling and Big Data Andrew Covato June 21, 2013
- 2. Google Confidential and Proprietary 2Google Confidential and Proprietary 2 What’s on the menu for today? Who am I? Marketing 101: The Basics Marketing 201: Digital Marketing Whet your appetite for attribution Conclusion: Digital marketing today = finance in the 1980s
- 3. Google Confidential and Proprietary 3Google Confidential and Proprietary 3 Who am I? (i.e. am I at least somewhat credible?)
- 4. Google Confidential and Proprietary 4Google Confidential and Proprietary 4 Ready to go? Let the fun begin!
- 5. Google Confidential and Proprietary 5Google Confidential and Proprietary 5 The Basics Marketing 101
- 6. Google Confidential and Proprietary 6Google Confidential and Proprietary 6 “Half the money I spend on advertising is wasted; the trouble is I don’t know which half.” --John Wanamaker: Retailer, business man, innovator, “advertising genius”
- 7. Google Confidential and Proprietary 7Google Confidential and Proprietary 7 The question marketing execs are seeking to answer is…
- 8. Google Confidential and Proprietary 8Google Confidential and Proprietary 8 “What is the dollar value of sales generated by each of my marketing initiatives?”
- 9. Google Confidential and Proprietary 9Google Confidential and Proprietary 9 “Traditional” offline marketing and advertising is impossible to track at an un-aggregate level…
- 10. Google Confidential and Proprietary 10Google Confidential and Proprietary 10 MARKETING MIX MODELING …but in the 1960s, big CPG firms developed a framework for measuring marketing ROI
- 11. Google Confidential and Proprietary 11Google Confidential and Proprietary 11 The Basic Principles of Traditional Marketing Mix Modeling (MMM) Inputs Controls Response Examples… often other metrics or dimensions included Incremental Sales Volume
- 12. Google Confidential and Proprietary 12Google Confidential and Proprietary 12 MMM is great for describing macro behaviours, but sales attribution to specific channels is dubious at best—especially with new advertising technology
- 13. Google Confidential and Proprietary 13Google Confidential and Proprietary 13 Digital Marketing Marketing 201
- 14. Google Confidential and Proprietary 14Google Confidential and Proprietary 14 What is this funnel you speak of?
- 15. Google Confidential and Proprietary 15Google Confidential and Proprietary 15
- 16. Google Confidential and Proprietary 16Google Confidential and Proprietary 16 Digital marketing allows advertisements to align with a specific person’s instantaneous intent This makes ads hyper- contextual
- 17. Google Confidential and Proprietary 17Google Confidential and Proprietary 17
- 18. Google Confidential and Proprietary 18Google Confidential and Proprietary 18 There are many more dimensions by which to accurately target an audience at various stages in the funnel
- 19. Google Confidential and Proprietary 19Google Confidential and Proprietary 19 Ads (and advertising dollars) can be tailored across a variety of such dimensions
- 20. Google Confidential and Proprietary 20Google Confidential and Proprietary 20 Taking bite-sized nibbles from a buffet of complexity Whet your appetite for attribution
- 21. Google Confidential and Proprietary 21Google Confidential and Proprietary 21 Remember what we are trying to quantify (ROI by marketing channel)…now let’s set up the problem
- 22. Google Confidential and Proprietary 22Google Confidential and Proprietary 22
- 23. Google Confidential and Proprietary 23Google Confidential and Proprietary 23 Today, many marketers employ “last-click”, “first- click”, or other weighting schemes to value their channels
- 24. Google Confidential and Proprietary 24Google Confidential and Proprietary 24 But assigning weights will not show you the true value of each channel…the weights need to be derived using the data!
- 25. Google Confidential and Proprietary 25Google Confidential and Proprietary 25 Some things can complicate this
- 26. Google Confidential and Proprietary 26Google Confidential and Proprietary 26 Probability of conversion is path-dependent People are in various stages of awareness when they interact with an ad Lots of exogenous variables (e.g. competitors’ response to marketing)
- 27. Google Confidential and Proprietary 27Google Confidential and Proprietary 27 Technology is a further complication—you need very detailed data to create a proper attribution model!
- 28. Google Confidential and Proprietary 28Google Confidential and Proprietary 28 For each user, we need a time- stamped record of each interaction with each advertisement…
- 29. Google Confidential and Proprietary 29Google Confidential and Proprietary 29 We also need environmental data (some sort of competitive index)
- 30. Google Confidential and Proprietary 30Google Confidential and Proprietary 30 Oh, and we need to develop a model to project the users’ instantaneous awareness state
- 31. Google Confidential and Proprietary 31Google Confidential and Proprietary 31 If you have all this, however… …you wield great power (and responsibility)
- 32. Google Confidential and Proprietary 32Google Confidential and Proprietary 32 While respecting privacy, you can: Calculate, in real-time, each user’s instantaneous probability of converting Determine the best creative to show a user at 20% conversion probability to bump her up to 80% Understand how “upper-funnel” ads impact sales farther down the funnel Ultimately calculate a dead- accurate ROAS for each digital channel!
- 33. Google Confidential and Proprietary 33Google Confidential and Proprietary 33 How? Let’s get technical… We can use a high-order Markov model to calculate conversion probabilities at each path Or a first-order MM could be used to move visitors between states (Abhishek, Fader, Hosanagar) Alternatively (and possibly preferably), some sort of additive model with a time-decay for touch- points
- 34. Google Confidential and Proprietary 34Google Confidential and Proprietary 34 Who will develop the magic model? Digital Marketing today is like Finance in the1980s
- 35. Google Confidential and Proprietary 35Google Confidential and Proprietary 35 Questions?