The Performance Attribution Of 'Programmatic'
Navigating Click-Through and View-Through Windows
We know that a consumer’s journey from ad exposure to conversion isn’t always a simple and linear event. Click-through and view-through windows have long been topics of debate in ad tech circles, as the length of the window between ad exposure and response can mean different results for attribution.
Consider the scenario when a travel credit card company programmatically runs an ad campaign at $5 CPM with 10 different vendors to directly lead to credit card approvals. The consumer is first exposed to the offer on a major magazine site. A few days later the consumer sees the same ad on a small, long-tail site and eventually acts on the offer. If multiple vendors deliver the ad, who should be credited for the conversion?
The issue becomes further complicated when the consumer is exposed to the same digital ad on a site after performing a Google search. If the conversion takes place here, should Google get the attribution as the last vehicle that delivered the ad to the customer?
Fortunately, sophisticated technology solutions provide insight into the full ad lifecycle to mitigate attribution errors. However, the debate continues over methodologies for assigning percentages of attribution credit accordingly.
Outsmarting the Attribution Model Gamers
Performance buying is another way that companies structure programmatic ad deals. Here, the same travel credit card advertiser pays a flat fee to meet a specific performance metric such as $300 for every new card issued. If the vendor negotiates a 25% split on a seven-day view-through or look-back window, then it can buy the cheapest travel-related inventory possible — often found in RTB — and get tens of millions of impressions for pennies, with the payoff of 25% every time a person fills out the application. The quality of inventory used to reach the set metric is not prioritized.
This is a game often played by marketers -- and with advertisers not concerned with which publishers are running their ads, the frequency, placement or many other details. Receiving the highest number of completed applications is the intended result. Without proper audience targeting, the quality of applicants ends up being forsaken for quantity, and the advertiser ultimately loses valuable engagement opportunities with qualified consumers.
Compounding this is the extraordinary level of sophistication that bots have achieved, including the ability to click on ads and fill out applications just as the human consumer would. Vendors that rely on performance models often use resources to write bot programs, including necessary data to properly fill out online forms and may even hire real people to complete the portions of forms that safeguard against bots. They go to such lengths because they are rewarded for their gaming efforts.
The power to stop this practice lies in the hands of brand marketers that have a vested interest in protecting their brands and generating quality results for their digital ad spend. Ultimately, the key for our industry is to rise above these issues and fight fraudulent attribution. Insisting on transparency and meaningful attribution practices will enable brand marketers and agencies to accurately evaluate their digital ad programs’ results while protecting their brands.