The digital ad firm AdTheorent launched machine learning features for dining brands, restaurants, and marketers.
The solutions are targeted at fast-casual restaurants and fast-food marketers to drive outcomes that they can measure and design campaign goals that increase foot traffic and sales.
Research by Statista found that 53% of diners use phones to find where a restaurant is located. 49% use phones to browse menus, and 37%, research restaurants there.
One of the solutions is CPIV (cost per incremental visit), which helps the restaurants to attract new clients. CPIV is based on machine learning, which identifies customers who are nearby. It makes use of predictive targeting and reaches people more likely to dine somewhere.
Pricing is based on the increase in visits – that is, if a customer who would not have visited the location does. A third party then verifies pricing.
AdTheorent also introduced a study measurement for visitation that allows companies to analyze the impact of campaigns on restaurant visits. They can monitor how frequent purchases are and the changes in them.
The company further added that it is partnering with electronic payments networks to match audiences to transactions and to analyze the impact of campaigns on online orders, in-app orders, and in-store sales.
AdTheorent also revealed a transaction based audiences feature that uses past purchasing data from card providers to identify audiences.
Additionally, the group revealed a competitive conquesting solution to serve ads to customers of other restaurants. The conquesting feature allows marketers to serve geo-targeted ads to defined audiences based on the apps those consumers use.
‘We have succeeded using machine learning to increase visits to sub-locations in the United States,’ said the director of field marketing at Firehouse Subs, Marisa Burton. ‘AdTheorent’s CPIV pricing model is an attractive option because we pay for visits that have resulted from exposure.’
In 2018, the company organized dining campaigns that did 8x better than the industry benchmark and produced an average rich media engagement rate 33% more than the industry averages.