How Deep Learning Can Bring Innovation to CG Marketing
Deep learning also helps brands differentiate between existing customers and those who need additional exposure to advertising messaging to convert. Traditionally, brands of all types have struggled to make this distinction, resulting in much wasted advertising spend and missed opportunities.
Deep learning’s advanced incremental lift abilities allow brands to avoid spending digital dollars on advertising to customers who are already brand-loyal thanks to television ads and other traditional media campaigns, and instead focus on potential customers who need an extra push on digital channels in order to make the purchase.
Not only that, deep learning algorithms use patterns gleaned from deterministic data, first-party data and consumer behavior data to determine the most effective ways of reaching those consumers who are still on the fence. By using information from existing customers, brands can find new, receptive audience clusters to advertise to while optimizing media buying and targeting strategies for each impression and potential customer in real time.
As CG brands are far too aware, consumer behavior and purchasing patterns have changed drastically over the last seven months. Gaps in the supply chain have forced consumers to seek out new brands, which in turn gives CG companies the opportunity to convert those who may have only shopped with them out of necessity into loyal customers.
Deep learning not only gives brands the ability to optimize their campaigns in real time, it also gives them the opportunity to understand, deliver and measure incrementality for performance marketing campaigns. CG brands who have previously struggled to get access to deterministic data, can now, with the aid of deep learning, know precisely how well their digital campaigns are performing and what action is needed to add another customer to the fold.
Jeremy Fain is CEO and founder of Cognitiv.