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Data Driven Attribution: The Future of Intelligent Measurement By Simon Poulton

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From the SMX East Conference in New York City,Oct 24-26, 2017. SESSION: How To Develop Multichannel Attribution Models That Move The Needle. PRESENTATION: Data Driven Attribution: The Future of Intelligent Measurement - Given by Simon Poulton, @spoulton - Wpromote, Director of Digital Intelligence. #SMX #31C

Published in: Marketing

Data Driven Attribution: The Future of Intelligent Measurement By Simon Poulton

  1. 1. #SMX #31C @SPoulton Dissecting The Evolution From Rules Based To Data-Driven Attribution DATA-DRIVEN ATTRIBUTION: THE FUTURE OF INTELLIGENT MEASUREMENT
  2. 2. #SMX #31C @SPoulton Dissecting The Evolution From Rules Based To Data-Driven Attribution DATA-DRIVEN ATTRIBUTION: THE FUTURE OF INTELLIGENT MEASUREMENT
  3. 3. #SMX #31C @SPoulton
  4. 4. #SMX #31C @SPoulton Dan Carter
  5. 5. #SMX #31C @SPoulton Dan Carter – New Zealand All Black 106 1,598 89 § Matches Played § Percent Of GamesWon § Points Scored (AllTime)
  6. 6. #SMX #31C @SPoulton § Matches Played § Percent Of Games Won § Points Scored (All Time) Owen Franks – New Zealand All Black 95 0 88
  7. 7. #SMX #31C @SPoulton § Owen Franks is a view-through conversion on Facebook. § Daniel Carter is an affiliate taking last-click attribution in Google Analytics. § Who should we invest in to keep winning? Rugby At-TRY-bution
  8. 8. #SMX #31C @SPoulton § March 2008: Avinash Kaushik publishes first article explaining basic rules-based models. § April 2009: Adam Goldberg challenges marketers to think about complexity. Multi-Touch Attribution As A Concept
  9. 9. #SMX #31C @SPoulton Standard Position & Rules-Based Models
  10. 10. #SMX #31C @SPoulton § Great article by Aaron Levy: http://searchengineland.com/whats- best-attribution-model-ppc-252374 Rules-Based Models – Further Reading
  11. 11. #SMX #31C @SPoulton § First started appearing in articles & social conversations in late 2012. The Rise Of Data-Driven Attribution
  12. 12. #SMX #31C @SPoulton Google’s Data-Driven Attribution § Google Analytics Premium: Launched on August 20th, 2013 (or 1,529 days ago!) § Additional Roll Outs: – DoubleClick: Feb 2016 – AdWords: May 2016 – Google Attribution: ~Q1 2018
  13. 13. #SMX #31C @SPoulton Methodology & Purpose DATA-DRIVEN ATTRIBUTION: UNDER THE HOOD
  14. 14. #SMX #31C @SPoulton Shapely Values § Created by Lloyd Shapely in 1953 § Solution concept in Cooperative GameTheory § A way to assign credit among a group of “players” who cooperate for a certain end. Lloyd Shapely
  15. 15. #SMX #31C @SPoulton Shapely Values – Glove Example § Example: – 3 “Players”. – Player 1 receives a left-hand glove. – Players 2 & 3 receive a right-hand glove. § Task: – Form a pair. – Credit assigned to each player after forming a pair.
  16. 16. #SMX #31C @SPoulton Brand§ 3 AdWords Campaigns § Minimum 15,000 Clicks & 600 Conversion Actions In Past 30 Days Shapely Values – AdWords Example Shopping Non-Brand
  17. 17. #SMX #31C @SPoulton Problem: 3 AdWords Campaigns had 4 sales of $1. How can we distribute the total credit of $4 to the individuals? Brand Shopping Non-Brand $4
  18. 18. #SMX #31C @SPoulton Step 1: Compute Normalizing Factors (NF), for different sizes of sub-teams. NF Formula** NF Team Permutations Number of Campaigns NF Formula NF Team Permutations 1 NF: (0! * 2!) / 3! = 2/6 = 1/3 33% 2 NF: (1! * 1!) / 3! = 1/6 = 1/6 16% 3 NF: (2! * 0!) / 3! = 2/6 = 1/3 33% Brand Shopping Non-Brand
  19. 19. #SMX #31C @SPoulton Step 2: Performance data-points for individuals. Brand Shopping Non-Brand $2 Sales $1 Sales $0 Sales
  20. 20. #SMX #31C @SPoulton Step 3: Performance data-points for campaigns as part of teams. Brand Shopping $4 § Counterfactual Gain – Brand alone makes $2 in sales. – Shopping alone makes $1 in sales. – The two of them make $4 in sales. § Brand’s counterfactual gain, i.e. what Brand brings as a value add, is therefore the total sales, minus what Shopping would have achieved on its own. § Brand’s counterfactual gain, in a group with Shopping, is $4 - $1 = $3. – Similarly Shopping’s counterfactual is the total sales, minus what Brand would have had on its own. § Shopping’s counterfactual gain, in a team with Brand, is $4 - $2 = $2.
  21. 21. #SMX #31C @SPoulton Step 3: Performance data-points for individuals as part of teams. Brand Shopping Non- Brand Shopping + Non-Brand Non-Brand + Brand Shopping + Brand Brand + Shopping + Non-Brand Sales $2 $1 $0 $2 $1 $4 $4 Brand $2 - - - $1 $3 $3 Shopping - $1 - $2 - $2 $2 Non-Brand - - $0 $0 $0 - $0 CounterfactualGain
  22. 22. #SMX #31C @SPoulton Step 4: Computing payoff for individuals from counterfactual gains, using Normalizing Factors (NFs). Group of 1 Group of 2 Group of 3 Attributed Payout NF 33% 16% 33% 100% Brand $2 $2+$3=$5 $3 33%*$2 + 16%*$5 + 33%*$3 = $2.5 Shopping $1 $1+$2=$3 $2 33%*$1 + 16%*$3 + 33%*$2 = $1.5 Non-Brand $0 $0 $0 $0
  23. 23. #SMX #31C @SPoulton Case Studies & Examples DATA-DRIVEN ATTRIBUTION: ADWORDS
  24. 24. #SMX #31C @SPoulton Conversion Shifts To Non-Brand Non-Brand Brand Conversions 244 +64% Cost / Conv $190 +12% Click Conv. Rate 0.7% -13% Conversions 69 -63% Cost / Conv $111 +58% Click Conv. Rate 0.7% -24% § Client Type: Auto-Parts Client with a complex path to purchase. § What Happened? Attribution weight shifted from remarketing and brand to non-brand upper funnel terms, allowing for a focus on non-brand to drive growth. *Date Range: 30 days Pre & Post Attribution Model Change.
  25. 25. #SMX #31C @SPoulton Conversion Shifts To Brand Non-Brand Brand Conversions 1,246 -7.1% Cost / Conv $31 -12.1% Click Conv. Rate 2% +13.4% Conversions 1,450 +8.4% Cost / Conv $1 +0.2% Click Conv. Rate 12% +4.8% § Client Type: Wedding Personalization Company with a strong focus on brand search. § What Happened? Brand campaign has started to see more credit. May be an indicator of over-reliance on lower funnel activity. *Date Range: 30 days Pre & Post Attribution Model Change.
  26. 26. #SMX #31C @SPoulton Conversion Shifts To Mobile Mobile Desktop/Tablet Conversions 551 +10.4% Cost / Conv $86 -32.4% Click Conv. Rate 1% +52.2% Conversions 522 -13.4% Cost / Conv $60 -6.1% Click Conv. Rate 3% +6.7% § Client Type: Furniture store with a long consumer research phase pre-purchase. § What Happened? Heavier weighting of earlier touch points (on mobile devices) drove a number of mobile bid optimizations & increases. *Date Range: 30 days Pre & Post Attribution Model Change.
  27. 27. #SMX #31C @SPoulton Conversion Shifts To Search From Shopping Search Shopping Conversions 827 +2.6% Cost / Conv $74 -19.6% Click Conv. Rate 2% +25.6% Conversions 218 -19.8% Cost / Conv $442 +8.5% Click Conv. Rate <1% -9.4% § Client Type: Fast-Fashion Brand with short consideration phase. § What Happened? Client was overly reliant on shopping campaigns as they were close to bottom of funnel. *Date Range: 30 days Pre & Post Attribution Model Change.
  28. 28. #SMX #31C @SPoulton Things to Consider CRITICISM
  29. 29. #SMX #31C @SPoulton § Google Only – While the methodology is exciting, the model is still limited by the inputs provided. Specifically, accounting for 3rd party ad networks (inc. Facebook) is non- existent. § Black Box – Without the ability to view Transaction IDs associated with AdWords conversions, we are still trusting Google that they are fairly claiming credit across campaigns. Criticism
  30. 30. #SMX #31C @SPoulton TLDR KEY TAKEAWAYS
  31. 31. #SMX #31C @SPoulton § Attribution Modeling is about looking forward to determine how to grow, not about looking back. § Standard Position & Rules-Based Attribution Models: Still very useful, but inherently contain bias & limit actionable insights. § Data-Driven Attribution – Uses ShapelyValues & Counterfactual Gains to constantly adjust based on new information available. – Available to all AdWords accounts with at least 15,000 clicks, and 600 conversion events in past 30 days. – Limitations: Still only ingests data from Google platforms, with no insights provided into Display or Facebook interactions. Key Takeaways
  32. 32. #SMX #31C @SPoulton § Director of Digital Intelligence @ Wpromote § Let’s Connect: – Twitter: @SPoulton – LinkedIn: /smpoulton/ – Email: simon@wpromote.com Speaker: Simon Poulton
  33. 33. #SMX #31C @SPoulton LEARN MORE: UPCOMING @SMX EVENTS THANK YOU! SEE YOU AT THE NEXT #SMX

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