Attribution, the ability to associate advertising impressions to the outcomes they drive, is a topic that we’ve covered before, and one that we’ll remain focused on in the future. Why? Because the marketers of today (and perhaps more importantly, of tomorrow) realize the powerful implications of being able to measure the impact that each touch point has in making a conversion happen. And when conversions and sales are synonymous, it’s crucial that marketers are able to make informed decisions as to which marketing channel is performing best against their goals.
In a retail campaign, each dollar shifted from one channel to another can directly impact the bottom line. That’s because the path to purchase for today’s consumer is anything but straightforward: It’s a tangled track of searches, emails, display ads, mobile messages and social interactions. That’s why we recommend that marketers stray away from last-click attribution – it simply doesn’t take into account all of the actions the consumer took prior to converting.
When complex attribution models are activated in the RTB environment marketers have the upper hand. Take, as an example, a certain in-store and online gift retailer that uses TerminalOne as its marketing operating system of record. This particular retailer used a broad set of digital marketing tactics to achieve its goals, but found closed-loop attribution to have the greatest impact on its overall success across channels. By feeding custom attribution models (created by MediaMath OPEN partner Convertro) into TerminalOne, the retailer was able to access more sophisticated bidding strategies. Know that because of TerminalOne’s extensibility, clients can work with any advanced attribution partner, including their current partner.
Ultimately, this client was able to leverage that advanced attribution data to inform each and every bid decision and spend more efficiently. The retailer was also better equipped to evaluate available inventory, and focus on buying media more efficiently; that is, the company could bid more for impressions that were more likely to lead to sales, and less for impressions that a seemed less likely to convert.
That strategy of backing every bid decision with advanced attribution data resulted in a 21 percent increase in the retailer’s return on advertising spend.
Learn how you can improve your media buying efficiency and contribute to a greater return on ad spend. Contact your MediaMath team to learn more.