Attribution modeling uses statistics on individual purchases to determine the credit that each marketing channel should receive for each sale. Giving proper credit where it is due leads to insights about which forms of advertising are more effective, which leads to better results for marketers. This leads to $100 million for Convertro.
Here’s an example. Suppose you are on espn.com’s golf page and you see an ad for Contoso golf clubs. A link on a posting takes you to a blog where a banner ad is shown at the top of the page. The article makes some ridiculous assertions about Steve Stricker that cannot go unchallenged, so you post the link on Facebook. As you scroll through your news feed, a promoted ad for Contoso shows up based on your obvious interest in golf clubs. The next day on your long commute to work, you pass a Contoso billboard, and the sports radio talk show host drops in three (paid) product placements for Contoso. Looking out your window, the verdant fairways of Royal Oaks golf course beckon. At work, you hop onto a golf equipment reviews site and see that Contoso clubs are rated highly. Finally, that night when you get home you see a link to a hilarious Contoso video starring Steve Stricker. You click on the link and laugh like crazy. That seals it: you hop onto Contoso’s website and purchase new irons for $350.
All told, you received seven advertising impressions prior to purchase:
- ESPN.com ad
- Blog banner ad
- Paid Facebook news feed ad
- Outdoor advertising
- Golf reviews site (unpaid)
- Twitter feed (unpaid)
- Video (viral)
How much credit from the $350 sale go to each of the impressions? This is a question of attribution. In the old days, a survey could have asked you, “How did you hear about us?” And you would have said ESPN.com, and perhaps those conducting the survey would give them all of the credit. This is called first touch attribution. If the operator of Contoso’s website looked at the referrer to their site, they’d see that the video brought you there. If you give the video all the credit, that’s called last touch attribution. Finally, you may decide that all seven impressions played a role in your purchase decision, and that the fair thing to do is give each source credit for one seventh: fifty bucks. That’s called linear, multi-touch, or equal weight attribution.
Attribution modeling attempts to provide weights for each impression using statistics. Here is how:
- Collect information on every online impression seen by as many people as possible, including the anonymized identity of the viewer, the exact time the impression was delivered, and the device it was delivered to.
- Create an analytics model that predicts the probability of purchase based on the entire history of advertising impressions for that individual, called user paths. (Details here.)
- Turn these purchase probabilities into attributions by comparing cases where a particular type of advertising (say Facebook) did and did not appear in user paths. For example, if it turned out that purchase probabilities are the same whether or not Facebook appears in a user path, then Facebook should not get any credit.
Attribution models look at data at a very granular level – down to the individual level as opposed to a metropolitan area (as in Google AdWords) or at the store level (as in a marketing mix model). This means there is the potential for greater accuracy and more targeted conclusions. In a world where marketing messages are becoming more and more individualized, attribution modeling is attractive to advertisers and advertising platforms alike. This potential is what AOL is paying $100 million for.
On the other hand, like any model, attribution models need to adequately represent reality to be of much use. This isn’t easy. If you think about it, there are a few potential pitfalls of trying to apply attribution modeling:
- Not all impressions are delivered online. In fact, many are not: TV, radio, billboards, word of mouth, and so on. Attribution modelers are aware of this of course, but they need to account for this by an “out of band” process that may be prone to error, for example by running another model for traditional media (such as a marketing mix model), or entering projected (“fake” as my previous boss used to say) information about offline media. This amounts to modeling with previously modeled data, which leads to doubt about the confidence intervals for the results.
- Not all sales are captured online. This is highly dependent on category: music is primarily purchased online, soup is not. Attribution modeling started in online-only categories, but as its popularity grows this issue can no longer be avoided.
- Not everyone purchases. You have to reckon not only with cases where a sale occurs, but cases where a sale does not occur. More specifically, cases where a category sale occurs even though the target product was not selected. In other words, Contoso impressions may have led to a Fabrikam sale.
- What about synergy? Even if all of the above considerations are accounted for, there is still the question of whether the combined effect of impressions from multiple channels exceeds their individual impact.
Even if all of these traps are avoided, there is still the issues of expense and complexity. Marshaling all of the detailed information required to carry out an attribution modeling analysis is hard. Making sure it is right is even harder. Will AOL be able to make attribution modeling cost effective and trustworthy enough to put it the reach of all of its advertising customers? We shall see.