A tough attribution problem: Do Marketing Affiliates deserve all the credit they get?
Recently, I received a tough question from one of the well known web analysts in our industry.
“Your book did a great job in detailing how to measure the impact of display advertising.”
Thank you
“However, I was trying to think through how to measure the impact of Affiliate advertising. Do you know of a way to measure if there is incremental lift due to advertising with an affiliate?”
Huh? Don’t you just check the reports in the affiliate marketing network, say LinkShare or Commission Junction? Or isn’t that a simple referral report in your web analytics?
“We have a pesky problem of having lots of customers that would have found their way to our site without the additional advertising.”
Oh boy!
So how could either question be researched? We can think through this methodically.
Is a controlled experiment with Affiliates / Distributors possible?
We have to look to either uncontrolled testing and/or panels, to find a solution
Comscore (for online panels) and Nielsen (for online or offline panels) suggest that the following type of analysis would be possible. Namely, one could split their panel population in two buckets:
- Used major affiliate’s sites (or distributors’ stores)
- Didn’t use major affiliate’s web site. (or distributors’ stores)
Watch for bias!
In contrast, group 2 contains people who may or may not be in the market for the manufacturer’s product category.
So in order to correct for this we would have to do more. We’d need to first filter the panel to the subset that makes any purchase in the related product category at all, regardless of whether that is this manufacturer’s product or one of its competitors. Then among this group we’d apply the analysis stated above.
That should get us a little closer to the truth, I believe.
Crazy, how much thought and effort it takes though!
Multichannel metrics (& web analytics) are both easy and hard. This one is an example of how they are rather hard and how they require much more than just (web) analytics software.
P.S.: Some manufacturers will have it easier with this type of analysis, namely those where everybody in the population needs their product. Say, shampoo or groceries or tax help.
P.P.S: In the offline world there is the benefit of geographic testing. So the offline marketer could compare purchase behavior in regions where there are not any stores by the manufacturer. Or they can also look at behavior based on the driving distance of individuals to the closest store.
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