3. ABOUT EXPONENTIAL
Company
ADVERTISING
e-X combines world-class data and technology to help brands
Platform INTELLIGENCE define, model and reach their audiences online.
PLATFORM
Our audience engagement divisions apply advertising
intelligence to deliver high-impact, high-engagement
Divisions campaigns across display, video and mobile to more than 450
million unique monthly users worldwide.
Advertising Intelligence 3
6. Have you not heard of us?
(In millions)
Global monthly unique visitors
(2012 average, comScore)
Advertising Intelligence 66
7. Where do we fit in?
Prospecting High impact formats with a
Awareness large reach to find new
Buzz customers
Intent
Consideration High quality engagement
Preference to drive purchase intent
Favourability and brand favourability
Engagement
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
11. Case Study: Soup or Sandwich
Measurement
Just because it’s
measurable
doesn’t mean
it’s relevant.
Advertising Intelligence 11 i
12. Case Study: Extended Warranties
‘Big data’ When
Measurement you pair
measurement
with incentives, it
will impact the
outcome.
Advertising Intelligence 12 i
14. The Billboard Analogy
‘Big data’ Measurement
Clickers are
like distracted
drivers.
Advertising Intelligence 14 i
15. Pleas from the Industry
Credit: Collective Media
Advertising Intelligence 15
16. 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
17. 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
18. Case Study: An Investment Bank
Conversion behaviours
Click behaviours
Advertising Intelligence 18
19. 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
20. Case Study: A Luxury Auto Campaign
‘Big data’
CPM CPC
Advertising Intelligence 20
21. A Wonks Slide
Analysis of 4,300 campaigns by Ken Mallon and Rick Bruner
Advertising Intelligence 21
22. What About Last View?
• Ignores all upper funnel brand • Targets consumers about to
marketing convert
• Incentive to buy remnant • Easiest to manipulate
inventory
Advertising Intelligence 22 i
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.