1. Erin Hunter, comScore Kim McCarthy, Starcom Greg Rogers, TACODA
Natural Born Clickers
Erin Hunter, comScore
Kim McCarthy, Starcom
Greg Rogers, TACODA

2. Background

3. We knew something was happening when certain audiences continually performed the best on a Click-Through and Click-Conversion basis
Click-Based Performance by TACODA Audience
Click-Through Rates
Click-based Conversion Rates
Source: TACODA database of campaigns, October 2007

4. Sweepstakers skew heavily female and yet even for products that skew male, the sweepstakers will still click at the highest rates
Sports cars
Electric Shavers
Golf Clubs
Fishing Rods
Sports Memorabilia
Electronics
Home Repair
Male Pharma

5. Methodology

6. comScore’s proprietary technology passively tracks all digital activity
Complete URL/clickstream data
No PII is retained
Very accurate knowledge of the user
Location
Demographics
Ad Exposure
Click by Click
Panelist’s PC
Browser
HTTPS Website
HTTP Website
CProxy
Streaming Site
Collection Server

7. How comScore tracks Natural Born Clickers
comScore panelists browse online
All actual creatives load into the browser
Image URL, Server, Creative, Size, Type, Publisher
Includes cached ads
All exposures and clicks are added to the database
“Category” is based on “the page the ad was served from”
Example: Clicking on an auto insurance ad for insweb.com on kbb.com (Kelley Blue Book) is put in the “Automotive Resources” subcategory
This analysis utilized comScore’s Client Focus Dictionary (CFD).
Just as there is overlap in comScore’s CFD (Client Focus Dictionary) categories, a single click can be attributed to more than one category. Example: clicks on a (page) on a channel like Yahoo! Sports will be attributed to Sports, but this click activity would also be attributed to the broad Yahoo! Media title, so the same clicks will also be counted in Portals.
*Adult content display ads were excluded from this analysis

8. How comScore defined clickers
Clickers defined as anyone who clicked on at least one ad within the study time frame, July 2007
Clickers
Non-Clickers
Source: comScore, Inc. custom analysis, Total US Online Population, XPC Persons Panel, July 2007 data period

9. Online clickers follow the 80/20 rule
Divided clickers into the subgroups based on click volume:
Heavy: 4+ Clicks
Moderate: 2-3 Clicks
Light: 1 Click
Despite only accounting for 6% of the total internet population, heavy clickers accounted for 50% of clicks in the month
How were breaks defined…
Source: comScore, Inc. custom analysis, Total US Online Population, XPC Persons Panel, July 2007 data period

10. Something to consider:
When optimizing your online buy to click based conversions, advertisers are letting somewhere between 6-16% of the internet population dictate their online strategy

11. Who are Natural Born Clickers?

12. First let’s start with GENDER – there is little to no difference across sets of clickers
Gender Indices against Total Internet
Non-Clickers
Light Clickers
Heavy Clickers
Index vs. Internet
Source: comScore, Inc. custom analysis, Total US Online Population, XPC Persons Panel, July 2007 data period

13. But with AGE – the more people click, the more they will tend to be in the 25-44 age range
Age Level Indices against Total Internet
Non-Clickers
Light Clickers
Heavy Clickers
Source: comScore, Inc. custom analysis, Total US Online Population, XPC Persons Panel, July 2007 data period

14. And with INCOME – the more people click, the more they will tend to be in the lower income brackets
Income Level Indices against Total Internet
Non-Clickers
Light Clickers
Heavy Clickers
Source: comScore, Inc. custom analysis, Total US Online Population, XPC Persons Panel, July 2007 data period

15. But yet, when we look at BUYING POWER INDEX, heavy clickers spend about 2X what non-clickers do
Note: A BPI value over 100 means that a clicker purchases, on average, more (in dollar terms) than the average Internet user during the reported time period.
Source: comScore, Inc. custom analysis, Total US Online Population, XPC Persons Panel, July 2007 data period

16. The counter intuitive spending pattern may be a result of the extraordinary consumption of the web demonstrated by the Heavy Clickers
Total Minutes per Visitor
Total Pages per Visitor 5X 8X
Source: comScore, Inc. custom analysis, Total US Online Population, XPC Persons Panel, July 2007 data period

17. Click-through rates show increased likelihood to click (2x), even after controlling for consumption
Source: comScore, Inc. custom analysis, Total US Online Population, XPC Persons Panel, July 2007 data period

18. When we look at COMPOSITION INDICES by website category, we begin to see an internet of two drastically different lives
Non-Clickers:
68% of the Online Population
0% of all clicks
Like Portals, Search, News, Finance
Heavy Clickers:
6% of the Online Population
50% of all clicks
Like Gambling, Job Searching, Games
Non Clickers
Heavy Clickers
Note: Indices are against each segment’s average UV composition over all categories. For example, compared to their average category consumption, Heavy Clickers are 40% more likely to visit Gambling sites
Source: comScore, Inc. custom analysis, Total US Online Population, XPC Persons Panel, July 2007 data period

19. Something to consider:
Clickers in general are a unique group of people
Consume more online media than non-clickers
Demographics differ from general population
Entire web experience appears to be leisure time
Increased likelihood to click
As marketers we have become enamored with the behavior of Heavy Clickers – but are there potential consequences?
Can we find an environment where typically “normal” clickers enter into the heavy clicker mindset?
Find a category where a user set is spending disproportionate amounts of time
Look at how clicking behavior is effected

20. Social Networks

21. Why Social Networking? Inherently leisurely category for teenagers
P2-17 over index (146) for total minutes spent on Social Networking sites
Overall category demographics differ from that of heavy clickers
Age skews younger
HHI skews higher
Geography skews are negligible
Despite the category differing demographically overall from the heavy clicker profile, a lot of clicking activity is happening here
Heavy clickers make up 9% of Social Networking Visitors (155 Index)
Social Networking Heavy Clickers make up 23% of total Heavy Clickers
Social Network Heavy Clickers defined as those who clicked on an ad within Social Networking content 4+ times in the month
Will heavy clickers look the same in this environment as they do overall?
Are Heavy Clickers the same in all environments?
Or will their demographics more closely mirror those of the site category?

22. Social Networking Heavy Clickers are almost twice are likely as Heavy clickers in general to be years old
Age Level Indices against Total Internet
Social Networking
Heavy Clickers
Heavy Clickers
Source: comScore, Inc. custom analysis, Total US Online Population, XPC Persons Panel, July 2007 data period

23. However the income skew is consistent between Social Networking Heavy Clickers and Heavy Clickers overall
Income Level Indices against Total Internet
Social Networking
Heavy Clickers
Heavy Clickers
Source: comScore, Inc. custom analysis, Total US Online Population, XPC Persons Panel, July 2007 data period

24. Something to consider:
When we find a consumer segment in a leisurely mindset, clicking does increase
P12-17 clicking at a much higher rate than groups spending less time on Social Networking sites
However, income skew stays consistent, despite shifting age skew
Will this hold in other categories?
Is income a key driver in clicking likelihood?

25. Preliminary data suggests no correlation between clicks and brand metrics
Starcom Clutter and Context study examined the relationship between behavioral and attitudinal metrics
Site level data from Dynamic Logic MarketNorms and DART was matched
Sites’ average delta on each of five brand metrics was correlated to average click thru rate
As the below chart shows, no attitudinal metrics were correlated to click through rate
When digital campaigns have a branding objective, optimizing to clicks is not necessarily improving campaign performance

26. Conclusions
Assumption of click based optimization is that everyone that sees your ad is equally likely to click it, but data shows this assumption to be false
If anyone, we would expect those within your target to be the ones more likely to click
Specific group of people click on ads, often not within your target
When you optimize to click-based conversions you are shifting your plan to reach a specific type of internet user
“Casino” mentality
Spending a lot of time online
Spending more money online
Exploring more online
Unless you are targeting this type of person specifically, you cannot rely on CTR alone
Even when click-based conversion is your metric of interest, you are still limiting the reach of your campaign by optimizing based on the behavior of a small group
Return rate? Life time value?
More time = higher likelihood to click
When targeting busy demographics (e.g. c-level executives, moms), would expect lower click thru rates