A Guide to Attribution Modeling
We're a long way from the early days of search and display. The tools used to serve ads via both channels have evolved a great deal, as have the tools we have access to that support our marketing intelligence.
Not so long ago, the internet evolved to a space with technology to support display advertising, such as ad servers. Display became a niche, specialty focus for marketers. There were many different jobs in this advertising microcosm that included media planners and the like. And like any microcosm, it was its own separate entity. Those who worked in display didn't consider any other concerns with regards to marketing strategy.
Meanwhile, search advertising was growing in its own independent column. It's always been about procuring sponsored listings in paid search results and then, more recently, on websites through content networks. These jobs were often occupied by the sorts of people who were slightly more analytical than media planners. They got into the weeds of how the channel performed, and played with multiple variables to try to get the best results possible.
With these two channels operating independently of each other within the same business, companies ended up with two different systems taking credit for the same sale.
For example, a consumer might be on a website where an ad for a specific product was advertised, but didn't click on the ad. Later on that shopper searches for the product, clicks a paid search ad and converts to a sale.
The team working on display assumes that because there was an impression and conversion, it's their sale. The search team assumes that because there was a search, click and conversion, that it's their sale.
The display team takes 100 percent of the credit, as does the paid search team. The truth is that the ad may have influenced the sale, but it didn't trigger the sale. Now you have two conversions for only one sale. There's obviously a problem here.
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