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GAUC 2017 Workshop Attribution with Google Analytics: Peter Falcone (Google)

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Overview of Attribution: Digital Attribution, Online to Offline Attribution and TV Attribution. We’ll cover the real-life examples, best practices, implementation details and results obtained.

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GAUC 2017 Workshop Attribution with Google Analytics: Peter Falcone (Google)

  1. 1. © Google Inc. 2016. All rights reserved. Attribution with Google Analytics Peter Falcone Analytical Lead EMEA April 6th, 2017
  2. 2. © Google Inc. 2016. All rights reserved. ● Digital Attribution ● Online to Store Attribution ● TV Attribution We’ll cover
  3. 3. © Google Inc. 2016. All rights reserved. ● Real life examples ● Results achieved ● How to & implementation details We’ll focus on
  4. 4. 3
  5. 5. 4 Source: https://en.wikipedia.org/wiki/Fritz_Heider
  6. 6. 5 Attribution The purpose of attribution is to quantify the influence each advertising touchpoint has on a consumer’s decision to make a purchase decision, or convert.
  7. 7. 6 ” Aim for better, not for perfect Improving focus by increasing data quality, extending the scope of channel measurement and media budget allocation. It`s a process of technology and service which provides a clearer view on marketing performance and enables value driven optimization.
  8. 8. Digital Attribution
  9. 9. 8
  10. 10. Founded 2011 4 Products (but 4.400 recipes) Countries 9 SEM manager 1 Bid strategy Adwords ROAS 1
  11. 11. 10 FABB ● is a constant process of media optimization ● assigns fractional contribution at granular and actionable level ● exports fractional contribution into bidding systems
  12. 12. Proprietary + Confidential Process, products and features Data driven modeling (DDA + unified channel grouping) X-Channel measurement (auto tagging, utm`s, filters) Automated bidding (ROAS bid Strategy) Data access / export (unsampled report) FABB Import Conversion credits (Offline Conversion Import)
  13. 13. Proprietary + Confidential Demo: Data in GA
  14. 14. Proprietary + Confidential All signals per click are stored here value click IDs) Unique ID used by bid managers to track ads and refer back in the system per ad / user / time / auctionURL?gclid=value
  15. 15. Proprietary + Confidential Signals used in autom. bidding stored in a Click ID +/- XX% Smartphone Noon EST Location BrowserOS Remarketing list Ad creative App Language Actual query Search partner Bid adjustment based on prioritized combinations of signals Click ID Google Stores auction signals/info
  16. 16. Impact on ROAS performance Pre Post ROAS - SEA all (Adwords All campaigns) 145% (proportional increase) 77% (proportional increase) ROAS Top 10 generics (Adwords Top 10 Generic campaigns) Case study: https://goo.gl/r6RHgb
  17. 17. Online to Offline Attribution
  18. 18. 17
  19. 19. Context 153 stores in France 36 days of store data loaded in Google Analytics In-store buyers with loyalty cards A high % of transactions’ volumes are made through the loyalty card program In-store buyers with loyalty cards that log-in on the website Logged-in users represent a high % of online traffic that can be matched with offline transactions made with loyalty cards Online to Offline - Context & Methodology 1 2 3 18
  20. 20. 19 UserID tracking
  21. 21. 20 2 different Users (cookie-based) Cookie (clientID) 123456.429834 Cookie (clientID) 432234.3423424
  22. 22. 21 Login Login 1 User (persistent ID based) › User-Centric Measurement › Works on Web, mWeb & Apps and other devices User ID: 4Q321 Cookie (clientID) 512955.2424231 Cookie (clientID) 123456.429834 Cookie (clientID) 123456.429834 User ID: 4Q321
  23. 23. 22 UserID Tracking in Analytics user login UserID (UID) assigned <UID > <UID > <UID > <UID > User ID User ID
  24. 24. 23 Implementation guide: http://goo.gl/cMkBv7 1 2 4 3 UserID Tracking - Implementation
  25. 25. 24 Implementation guide: http://goo.gl/cMkBv7 UserID Tracking - Session Unification PAGE 1 PAGE 2 PAGE 3 LOGGED INNOT LOGGED IN 1 SESSION Login With Session Unification enabled, all login and pre-login hits in the same session (only) are reported in the User ID View 4
  26. 26. 25 Implementation guide: https://goo.gl/pMB4aT UserID Tracking - Tag Manager 4
  27. 27. 26 Online to Offline Tracking in Analytics Loyalty Card purchase Measurement Protocol user login UserID (UID) assigned <UID > <UID > <UID > <UID > User ID User ID
  28. 28. 27 Measurement Protocol for Online to Offline Measurement Protocol allows you to send data to Google Analytics from anything with an Internet connection. The data is sent via HTTP Requests, a very common way to transfer data online, to: http://www.google-analytics.com/collect http://ssl.google-analytics.com/collect Name Parameter Example Description Protocol Version v v=1 Protocol version - the value should be 1 Tracking ID tid tid=UA-123456-1 Google Analytics Property ID User ID uid uid=123456 Persistent/authenticated user id, unique to a particular user Hit Type t t=event The type of interaction collected for a particular user
  29. 29. 28 Demo time
  30. 30. Return on AdWords spend is multiplied by 6.4 when considering in-store transactions Online return on ad spend (€) Online to in-store return on ad spend (€) x6.4
  31. 31. Proprietary + Confidential More online preparation is done, when the basket value is high Low 28% 33% 39% 46% 58% 57% 66% 73% 87% 86% x3 HighStore average basket value O2S effect1 by basket size (%) 1 In-store buyers who visited the site before making a purchase (the standard lookback window of this study is 7 days
  32. 32. Proprietary + Confidential Key Findings 44% x3 of in-store buyers visited the site before making a purchase x6.4 Is where the O2S effect is maximized Mobile O2S1 effect when average basket value is high AdWords ROAS when in-store sales are considered 1 In-store buyers who visited the site before making a purchase (the standard lookback window of this study is 7 days) Case study: https://goo.gl/sKw1Ii
  33. 33. TV Attribution
  34. 34. How would you like to... Identify TV Spot performance and optimise towards it? TV Attribution helps you identify low performing TV spot activity, and optimise its budget into higher performing activity ENGAGEMENT COSTEFFECTIVENESS
  35. 35. © Google Inc. 2016. All rights reserved. TV Attribution Analysis Logic 6am 8am 10am 12pm 2pm 4pm 6pm 8pm 10pm 12am Digital Activity Baseline TV TV TV TV TV TV TV TV How it Works • Evaluate minute-by-minute and hour-by-hour activity • Machine learning establishes baseline • Model incremental impact of airings
  36. 36. © Google Inc. 2016. All rights reserved. Bear in mind: short-term analysis scope!
  37. 37. © Google Inc. 2016. All rights reserved. TV SPOT DATA ● Impressions ● Creative ● Network ● Day-part ● Spot Combine & analyse data Incremental searches & visits attributed to individual TV spots Bayesian Inference with Gibbs Sampling GOOGLE SEARCH DATA ● Volume ● Brand, Generic ● Tablet, Desktop, Mobile ● Baseline, Ad, Other How Does It Work? High level process GOOGLE ANALYTICS DATA ● Paid visits ● Direct visits ● Organic visits ● Baseline, Ad, Other
  38. 38. 37
  39. 39. © Google Inc. 2016. All rights reserved. Thank you!

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