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.

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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  • 1. Campaign Attribution is BrokenSo how do we maximise ROI on marketing spend?
  • 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. 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. VISITOR BEHAVIOURPage 4 @peter_oneill 28th Nov, 2012
  • 5. The Field of PlayRIO (location of the game)
  • 6. Who gets the credit for a goal (conversion)?Page 6 @peter_oneill 28th Nov, 2012
  • 7. 1. Last Click Attribution Final touch scores goal & gets all creditRIO (location of the game)
  • 8. Who gets the credit for a goal (conversion)? The “Goal Scorer”  Last click attribution Page 8 @peter_oneill 28th Nov, 2012
  • 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. 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. 3. Ad Tracking Models Ad tracking networks don’t capture all online touch pointsRIO
  • 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. 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. 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. 5. Offline Touch Points Ball forced out by defender & other players provided alternative attacking options – also deserve creditRIO
  • 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. 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. THE DEBATEPage 18 @peter_oneill 28th Nov, 2012
  • 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. 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. 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. BUSINESS QUESTIONSPage 22 @peter_oneill 28th Nov, 2012
  • 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. A 4th Question – from the CEO Did we win?RIO ROI (only location they care about)
  • 25. RECOMMENDED APPROACHESPage 25 @peter_oneill 28th Nov, 2012
  • 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. 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. 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. 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. 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. HAVE I CONVINCED ANYONE???Page 31 @peter_oneill 28th Nov, 2012
  • 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. 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