Campaign attribution is broken

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Presentation from eMetrics London providing an alternative view of Campaign Attribution and why the current popular approach can never work. All presented using football as an analogy.

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Campaign attribution is broken

  1. 1. Campaign Attribution is BrokenSo how do we maximise ROI on marketing spend?
  2. 2. Who am I? G’day, I’m Peter... I am Australian – with a strong aussie accent Founder of L3 Analytics Also Founder of MeasureCamp Page 2 @peter_oneill 28th Nov, 2012
  3. 3. I intend to cover 4 areas*1. Review visitor behaviour & touch points  How campaign attribution treats them2. My debate with campaign attribution fans3. Always focus on the business questions4. My recommended approaches*** Using an extended version of Matthew Tod’s football (soccer) analogy to make my points** I don’t have a case study to prove these work ****** Yet... Page 3 @peter_oneill 28th Nov, 2012
  4. 4. VISITOR BEHAVIOURPage 4 @peter_oneill 28th Nov, 2012
  5. 5. The Field of PlayRIO (location of the game)
  6. 6. Who gets the credit for a goal (conversion)?Page 6 @peter_oneill 28th Nov, 2012
  7. 7. 1. Last Click Attribution Final touch scores goal & gets all creditRIO (location of the game)
  8. 8. Who gets the credit for a goal (conversion)? The “Goal Scorer”  Last click attribution Page 8 @peter_oneill 28th Nov, 2012
  9. 9. 2. First Click / Weighted Attribution Models Midfield play set up the goal for the striker Multiple players contributed to the goal & may deserve some creditRIO
  10. 10. Who gets the credit? The “Goal Scorer”  Last click attribution Midfield passes  First click / weighted attribution / descending attribution / whatever allows you to pick the winner... Page 10 @peter_oneill 28th Nov, 2012
  11. 11. 3. Ad Tracking Models Ad tracking networks don’t capture all online touch pointsRIO
  12. 12. Who gets the credit? The “Goal Scorer”  Last click attribution Midfield passes  First click / weighted attribution / descending attribution / whatever allows you to pick the winner... Ignoring players  An issue with traditional ad tracking tools Page 12 @peter_oneill 28th Nov, 2012
  13. 13. 4. Multiple Devices Play started on the other side of the pitch & these players deserve credit too Work (or smart Home (computer)RIO phone, tablet, etc)
  14. 14. Who gets the credit? The “Goal Scorer”  Last click attribution Midfield passes  First click / weighted attribution / descending attribution / whatever allows you to pick the winner... Ignoring players  An issue with traditional ad tracking tools Initiators of the passage of play  You simply can’t ignore the issue of multiple devices Page 14 @peter_oneill 28th Nov, 2012
  15. 15. 5. Offline Touch Points Ball forced out by defender & other players provided alternative attacking options – also deserve creditRIO
  16. 16. Who gets the credit? The “Goal Scorer”  Last click attribution Midfield passes  First click / weighted attribution / descending attribution / whatever allows you to pick the winner... Ignoring players  An issue with traditional ad tracking tools Initiators of the passage of play  You simply can’t ignore the issue of multiple devices Defenders caused the error & ran in support  Offline touch points can’t be captured in any tool, however powerful (or big the data is) Page 16 @peter_oneill 28th Nov, 2012
  17. 17. Let’s look at a scenario Kevin Hillstrom (MineThatData) wrote this scenario & asked – How should this purchase be attributed? http://blog.minethatdata.com/2012/10/your-opinion-wanted-attribution.html Nine varied answers, most very precise  Lets have a flick through Page 17 @peter_oneill 28th Nov, 2012
  18. 18. THE DEBATEPage 18 @peter_oneill 28th Nov, 2012
  19. 19. Defining the Debate Campaign attribution tools only capture a part of the visitor journey  All, half, most, a minority of the journey – it depends...  If all visitors login, you will get more (multiple devices) But it is just not possible to get the full visitor journey, however powerful (expensive) the tool is or how big the data is Let’s clarify what I believe the debate should be“Is the data being captured sufficient to provide the intelligence for informed business decisions?” Page 19 @peter_oneill 28th Nov, 2012
  20. 20. Current Majority Opinion Invest in the right tools & throw enough resources at the problem – and it will can be solved!! Image from SmartInsights blogThis is Digital Analytics – wework with the data we have Image from Adobe SiteCatalyst blog In sporting terms “Point to the Scoreboard” Quotes from Tagman case study Page 20 @peter_oneill 28th Nov, 2012
  21. 21. My Viewpoint - No This is not a sampling size issue  e.g. We don’t have 100% accuracy but we can use the trends It is incomplete data Scale of the problem is unknown Data available can be misleading Risk of wrong decisions is too large Paul Postance: “it could be done better” is not an insult. It’s a mindset to make things better. Change the conversation from which model is most accurate to how to optimise spend Page 21 @peter_oneill 28th Nov, 2012
  22. 22. BUSINESS QUESTIONSPage 22 @peter_oneill 28th Nov, 2012
  23. 23. What do we really need to know? Do we really need campaign attribution models?  No – they are simply the most obvious solution to the business problem Let’s focus on the business problems, not the technology There are three business intelligence requirements... 1. What do I report to the business? 2. How much do I pay agencies for the last period? 3. How do I optimise future marketing spend? Why hunt for a single solution – use the best approach to answer each requirement Page 23 @peter_oneill 28th Nov, 2012
  24. 24. A 4th Question – from the CEO Did we win?RIO ROI (only location they care about)
  25. 25. RECOMMENDED APPROACHESPage 25 @peter_oneill 28th Nov, 2012
  26. 26. Business Performance reporting Pick a single approach & stick to it  I don’t care which (last click is simplest) What is important is that  The business understands the approach  A change in performance can be easily identified  and reacted to!! Football analogy – who scored the goals Page 26 @peter_oneill 28th Nov, 2012
  27. 27. Evaluate Agency Performance Define business objective for each campaign  Is it awareness, research, conversion, etc Define what success looks like  KPIs & targets Agree payment on this basis Football analogy – performance based payments  Goals, tackles, minutes played, crosses, accurate crosses Business  Impressions, TVRs, visits, non bounce visits, visits that create a basket, leads, sales, increase in NPS Page 27 @peter_oneill 28th Nov, 2012
  28. 28. Don’t say this can’t be done... Not saying it would be easy But wouldn’t a campaign equivalent of this be incredibly useful/actionable? Page 28 @peter_oneill 28th Nov, 2012
  29. 29. Optimise Marketing Spend Remind me – how do we optimise again?  Objectives | Evaluation | Hypothesis | Test Football analogy – simultaneous games Page 29 @peter_oneill 28th Nov, 2012
  30. 30. Optimise Marketing Spend Business equivalent – test campaigns How to test campaigns  Different campaigns in different geographical regions  Hold out tests  Switch off/on keywords  Pick similar trending products & promote half  Measure impact from offline on online & vice versa Learn what impact of campaign really is Optimise spend using these learnings Page 30 @peter_oneill 28th Nov, 2012
  31. 31. HAVE I CONVINCED ANYONE???Page 31 @peter_oneill 28th Nov, 2012
  32. 32. Summary Campaign attribution is broken  Simply can’t capture all touch points Instead of wasting money on impossible, invest resources in the difficult Focus on the real business intelligence requirements Be consistent in internal reporting Evaluate performance against predefined objectives & targets specific to campaign Test to discover what works best Adjust spend to maximise profitability Page 32 @peter_oneill 28th Nov, 2012
  33. 33. THANK YOU I can be found at • peteroneill@l3analytics.com • @peter_oneill • +44 7843 617 347 • www.linkedin.com/in/peteroneillPage 33 @peter_oneill 28th Nov, 2012

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