Optimizing On Cookie Crumbs

by Sharon Bernstein, Jun 30, 2010, 11:00 AM

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In all my years in online analytics, I have been waiting to see an online media placement that is so strong, the consumer converts directly from one exposure (either click or view), without a path of influence. I think most people reading this would agree that it's nearly impossible (because if we all knew the secret formula, wouldn't we all be using it?!). But why then is the last click/view attribution the standard model?

I strongly believe that if we ignore consumer behavior (you might want to read my last post on landing page testing), we are doing ourselves and our clients a disservice. It's very possible that each exposure is necessary in driving the major influence for the conversion, yet we typically optimize based on last click attribution anyway. In the case of online media, where we can measure the influence, why aren't we? In contrast to offline media, I don't think any client is willing to risk turning TV off, even if we can't directly quantify and attribute the results to a specific commercial or time slot.

To test the online multi-click attribution theory, we developed a custom report based on campaign exposure conversion data (i.e., the exposures or clicks the consumer had leading up to the conversion), to determine if conversions occur after the first initial ad exposure, or if the results are being influenced by the synergy of multiple sites. Think of it as counting the crumbs (or impressions) you leave behind you when you take the cookie from the jar.

Looking at a recent one-month campaign in May 2010 for one of our finance clients, almost 60% of conversions occurred after the exposure to one ad, and the remaining were the results of 2+ exposures. As expected, search engines played an important role in driving direct conversions. Based on the last click/view attribution model, both Google and Yahoo generated almost 55% of the conversions. Individually, the display sites on the buy don't look like strong conversion drivers.

Taking a closer look at the campaign results using all of the impressions and clicks prior to the conversion, the display buy influenced 70% of all campaign conversions. AOL generated close to 15% of direct conversions, but almost 20% of all the people who converted were exposed to an ad on AOL before converting. Casale also proved to be a site that was efficient in generating awareness (third highest percentage of total exposures prior to converting).

While this is only one example on a relatively small data set, multiclick attribution provides a more complete picture than last-click or last-impression attribution. If a consumer was influenced by a series of online ads prior his conversion, each site is a necessary and influential player in the process and we shouldn't be optimizing out of them if they don't provide a direct conversion. Adding consumer value (or size of the cookie) to each of the conversions to calculate ROI tells a whole different story (but that's for another time).

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0 comments on "Optimizing On Cookie Crumbs".

1. Michael Mcmahon from ROI Factory / Quick Ops
   

   commented on: June 30, 2010 at 12:02 p.m.

   I think there may be a flaw in your analysis. "almost 20% of all the people who converted were exposed to an ad on AOL before converting". I don't think you have established a causal relationship between those AOL impressions and the final purchase. Wouldn't you have to suppress the AOL ads for a control group and determine the impact on purchase rate?

   Otherwise, display ads in places with high concentrations of your target audience simply serve as hand-stamps, not necessarily generating behavioral changes just by virtue of the fact they were in the right place to get the stamp. Or am I mistaken?

   Thank you.

2. Sean Gelles
   

   commented on: June 30, 2010 at 12:14 p.m.

   You definitely should write an article on the bread-crumb trail, targeting and cookie-size or customer-value. I can tell you from my own experience with clients that strategically targeting the bread-crumb trail may not get you more conversions but it does get you higher value conversions -- 800 times higher than the industry average in one campaign on which I worked.

   Michael has a good point.

   The other thing you could do to build a case for causality would be to compare the paths of the non-converting cookies to the ones that converted (i.e. use your non-converting cookies as your control). This way you could isolate those parts of your campaign that distinguished conversion from non-conversion.

3. Vincent Chittilappilly from Visual IQ Inc.
   

   commented on: June 30, 2010 at 1:07 p.m.

   Each marketing organization should ask themselves how well the campaigns, channels, business units help each other for their conversions. Multi-click attribution metrics are the ones take you to the answer. Once you can identify the synergy among attributes then you are simply cutting down the wasted advertising dollars.

4. Rob Griffin from Media Contacts
   

   commented on: July 1, 2010 at 5:51 a.m.

   Michael, one always must be careful with post impression conversions or digital display can take undue credit. My two cents is keep tracking and report it then decide the value that exposure had before converting as not all exposures are equal. At least you know you are fishing where the fish are.

5. Sharon Bernstein from Media Contacts
   

   commented on: July 1, 2010 at 12:38 p.m.

   Michael, just to clarify, I am not saying that there was in increase in conversion of 20% of those exposed vs those not exposed to AOL. Analyzing the cookie level data, of those that converted, I know 20% were exposed to a display ad on AOL before converting. You are right that I don't know the impact of removing AOL from the buy, but from the data, I would say AOL is potentially making an impact.

6. Sam Diener
   

   commented on: July 21, 2010 at 11:43 a.m.

   Sharon/Sean/Mike --- in terms of causal relationships, were you looking at time differences? And are we talking last conversion?

   Sharon - I have a huge set of data that would interest you.

   Sam

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