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Exponential SA deck


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  • Soup shop hires a full time meterologist Sandwich shop does not Both can observe the weather
  • Huge sales incentive for warranty extension Worked for a time Salespeople began selling cheaper televisions to sell the warranty Cheaper televisions made warranties more expensive to provide Everything fell apart
  • Billboards are like display ads We’re asking drivers to pull over and take a brochure
  • The industry is desperate to move on
  • There are far too few clicks to be representative Click patterns make the situation worse
  • People who click are different
  • Here’s an example of what we mean. Look at the conversion behaviours for this UK business banking client. In this example, the converting behaviours are predominantly executive careers and executive cars. This is the profile of a small business owner. You can almost imagine them in the room. Compare that to the clicker audience for the same client. This looks nothing like the intended target. In this case the choice of measurement metric has skewed the delivery away from the target audience.
  • The grey circle is CPC traffic This pattern repeats as the site chases volume and then resumes
  • Traffickers forgot to optimize to CPC When they did, brand lift fell Brand lift is rolling average
  • Clickers have higher awareness of ads and message – significant but small No other effect
  • What if you are using last view attribution? i.e. the media partner showing the last ad before the user converts gets the full display ad conversion attribution. This is the prevalent model today for display and while we think it is the “least worst” option versus click through rate measurement and click to conversion attribution it does lead to bad practice. In our example here, a user has already visited the site. Now all the media partners in the plan with a retargeting pixel on the site are incentivised to show as many ads as possible to the use on the cheapest inventory they can find whether that ad was visible on the page or not. The infamous “spray and pray technique. In a last view attribution world it forces you to concentrate on emptying the funnel with retargeting and not to fill the funnel with prospecting”.
  • You can’t see all the data different distributions, experiences,… R-squared of 24%
  • With regression: Assume that every event is independent and unrelated Assume that the error is truly random
  • In 2011 IBM released a paper with the oft repeated comment that “ “Everyday, we create 2.5 quintillion bytes of data–so much that 90% of the data in the world today has been created in the last two years alone.” When people engage online they create huge amounts of data. Exponential has the same big data issue as many other businesses. We collect 80 billion events a month across 450 million users worldwide and organise that into 50,000 categories. That’s equivalent to seeing more than 5600 Olympic Stadia of people more than 170 times a month each. Day to day online business dwarfs the data potential of the biggest events. [ “The amount of data is meaningless”] All big data presentations start with stats about the vast scale of data now collected. But we would argue that the amount of data being collected today is now so vast as to be incomprehensible to the human mind. 5 years ago it was about how big your data warehouse was and how fast you could process data. Today, it really isn’t about how big it is but what you do with it.
  • Transcript

    • 1. About 2
    • 2. ABOUT EXPONENTIALCompany ADVERTISING e-X combines world-class data and technology to help brandsPlatform INTELLIGENCE define, model and reach their audiences online. PLATFORM Our audience engagement divisions apply advertising intelligence to deliver high-impact, high-engagementDivisions campaigns across display, video and mobile to more than 450 million unique monthly users worldwide. Advertising Intelligence 3
    • 3. Serving brands in 26 countries Advertising Intelligence 44
    • 4. World’s leading brands choose Exponential Automotive Consumer Technology Retail Travel Telecom Goods Advertising Intelligence 5
    • 5. Have you not heard of us? (In millions) Global monthly unique visitors (2012 average, comScore) Advertising Intelligence 66
    • 6. Where do we fit in? Prospecting High impact formats with a Awareness large reach to find new Buzz customers IntentConsideration High quality engagement Preference to drive purchase intentFavourability and brand favourabilityEngagement Conversion Combine the learning from the initial Response two phases to create a scientifically Performance built custom audience to drive high campaign performance Advertising Intelligence 7
    • 7. Technology that drives smarter brand advertising Advertising Intelligence 88
    • 8. What You Measure Is What You GetBe careful how you define 9
    • 9. Lesson One:Measureable is not 10
    • 10. Case Study: Soup or Sandwich Measurement Just because it’s measurable doesn’t mean it’s relevant. Advertising Intelligence 11 i
    • 11. Case Study: Extended Warranties ‘Big data’ When Measurement you pair measurement with incentives, it will impact the outcome. Advertising Intelligence 12 i
    • 12. Lesson Two:Clickers are 13
    • 13. The Billboard Analogy ‘Big data’ Measurement Clickers are like distracted drivers. Advertising Intelligence 14 i
    • 14. Pleas from the Industry Credit: Collective Media Advertising Intelligence 15
    • 15. Clicks Are Rare • Most clickers are serial clickers ‘Big data’ • 18% of clicks are from the same user on the same ad • Game sites are often harvested for clicks • Click fraud is likely ~20% Advertising Intelligence 16 i
    • 16. Clickers Are Different • They are mostly older and female ‘Big data’ • They have low income and poor credit • They are late adopters • They are economizers Advertising Intelligence 17 i
    • 17. Case Study: An Investment Bank Conversion behaviours Click behaviours Advertising Intelligence 18
    • 18. Case Study: An Online Recruiter CPC campaigns ‘Big data’ drove traffic but not conversions. The CPC visitors seemed lost. More Visits More Resumes More Resumes Advertising Intelligence 19 i
    • 19. Case Study: A Luxury Auto Campaign ‘Big data’ CPM CPC Advertising Intelligence 20
    • 20. A Wonks Slide Analysis of 4,300 campaigns by Ken Mallon and Rick Bruner Advertising Intelligence 21
    • 21. What About Last View?• Ignores all upper funnel brand • Targets consumers about tomarketing convert• Incentive to buy remnant • Easiest to manipulateinventory Advertising Intelligence 22 i
    • 22. Lesson Three:Models are 23
    • 23. Credit: Chris Anderson, WiredAdvertising Intelligence 24
    • 24. Models Are Simplifications Advertising Intelligence 25
    • 25. Models Demand a Controlled Experiment Advertising Intelligence 26
    • 26. Lesson Four:Attribution Is Too 27
    • 27. Computers At War Advertising Intelligence 28 i
    • 28. Welcome to Big Data Advertising Intelligence 29 i
    • 29. Credit: Chris Anderson, WiredAdvertising Intelligence 30
    • 30. Look At Your Customers, Not Your Campaign Advertising Intelligence 31
    • 31. Discovering Your Audience Advertising Intelligence 32
    • 32. Correlation Works Advertising Intelligence 33 i
    • 33. Push The Curve By Understanding Your Audience Advertising Intelligence 34
    • 34. Thank 35