A few months ago I read an article in Ad Age written by Jag Duggal, vice president of product management at Quantcast. I agree with most of what Mr. Duggal covers. In fact, I wrote a column for ClickZ that supports his second claim that CTR is a poor optimization metric.
It is Mr. Duggal’s first claim that caused me to pause:
“Cookies are far shorter lived than thought. The RTB industry structure — separating data from optimization, and lower-purchase-funnel retargeting from upper-purchase-funnel prospecting — assumes that cookies are reasonably long-lived. In reality, the half-life of an average third-party cookie is about three days, and one-third of all cookies last less than an hour. Buyers can err by either focusing on only a population subset with stable cookies or buying lists of cookies that likely won’t be found again. Stale cookie lists degrade effectiveness and efficiency; real-time bidding requires real-time data.”
At face value this statement is potentially disastrous to any cookie-buying campaign. I felt it necessary to dig deeper into the claim, sensing that the implications could make a huge impact on how the industry approaches audience-buying tactics and measurement.
My company took a sample of ~100 million RTB cookie IDs on Day 0 and then scanned a discrete day at seven-day intervals for four weeks. The further we got away from Day 0, the less likely we were to see the cookies again.
(Author Note: Please do not interpret this data as a true cookie decay rate. We measured discrete days. It is very likely that cookies in the 100 million at Day 0 may have appeared on days that we did not measure. Therefore this estimate is directional and we do not present it as an actual decay rate.)
We categorized the data into multiple segments to see if we could find any significant skews. It turns out that Brazilians and Chrome and Mac users tend to hold on to their cookies a bit longer, but not enough to justify a deeper investigation.
We also studied a variable we call surfing behavior. It is how many times we see groups of cookies within a given time frame. These numbers showed a significant skew.
Chart 2 shows that we saw 20 percent of the users only once, 61 percent were observed at a low frequency, and 19 percent were observed at a high frequency.
Chart 3 establishes that there is a significant difference in cookie decay rates when classified by surfing behavior. In this case, the 0,1 surfing behavior category decays at ~2x the rate of the high surfing behavior category.
This data suggests that there is a small subset of browsers that are classified into 0,1 to low frequency buckets that churn their cookies at a much higher rate than the average browser.
The observation is supported by the 2007 comScore study on cookie deletion. In the study, comScore concludes that 7 percent of the browsers are responsible for 35 percent of the cookies over a 30-day period.
This is an important point. The impact of a small subset of what comScore calls “Serial Cookie Deleters” could be very significant. Here is an example to help illustrate:
Assumptions in example for Chart 4:
- At Day 0 there are 100 browsers and each browser has 100 cookies.
- Ten browsers remove their cookies every session.
- Ten browsers remove their cookies every day.
- There is a constant of 10 sessions per browser per day.
- Whenever a cookie is removed from a browser a new one is set.
Chart 4 demonstrates that even if a small percent of the total number of browsers remove their cookies in a systematic manner, the total number of cookies available can be much higher than the actual number of browsers.
If you think through this a bit more, after three days there are 500 dead cookies (600 cookies – 100 browsers). This is an example to illustrate that small subsets and high distribution can make a huge impact on actual counts. This example is not real data, but the comScore data is likely much closer to current reality.
The real potential reach of the Chart 4 example is 100 browsers. Eighty of them are stable throughout the example. Any cookie-based targeting and tracking model will naturally skew toward stable browsers, as the cookie IDs of unstable browsers are quickly removed from the tracking and targeting pool.
What Does This Mean for Digital Marketers?
If the majority of cookie decay is the result of a minority of browsers that remove their cookies frequently, then the majority of browsers have stable cookies even though a significant minority of cookies may have a limited life.
According to the comScore study, any upper-funnel targeting that is >30 days would effectively eliminate 7 percent of the browser target audience over a 30-day period. Mr. Duggal’s statement concludes that upper-funnel segmentation models will be skewed toward the 93 percent of browsers that do not frequently remove their cookies. I feel this fulfills the promise of upper-funnel cookie targeting.
It also means that the current state of cookie-based attribution slightly under-represents the top of the funnel in favor of the lower funnel, but again, not enough to entirely throw away the practice. It needs to be understood by the media buyer that upper-funnel results are undervalued and lower-funnel results are overvalued.
The most tangible impact is on unique reach and frequency calculations. Real reach is vastly overstated, while real average frequency is widely understated.
An educated media buyer should understand these results and their implications on what they buy and how they measure. I think the information in Mr. Duggal’s first statement is interesting and directionally correct but not significant enough to entirely abandon upper-funnel cookie targeting and attribution models.
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