Digital advertising firm AdTheorent has announced a suite of machine learning (ML) solutions for dining and restaurant brands and marketers. The solutions aim to enable Fast Food/Quick-Service Restaurant (QSR) and Fast-Casual Restaurant (FCR) marketers to drive measurable business outcomes and are designed for specific campaignm goals from increasing foot traffic and visitation, to acquiring new customers and increasing sales.
The first is a Guaranteed Cost Per Incremental Visit (CPIV) solution to drive new customers to restaurants. AdTheorent’s visitation models use machine learning to learn when and where to reach new customers most likely to visit a restaurant location.
The pricing model guarantees that brands only pay for incremental visits, verified by a third-party measurement provider. An incremental visit is a consumer that would not have visited a dining location if AdTheorent had not served the ad.
AdTheorent uses a Visitation Measurement Study to analyze a campaign’s impact on driving visits to a restaurant. Predictive location targeting enables the company to identify and reach consumers in key locations who have the highest probability of visiting a dining location.
AdTheorent identifies unique individuals who were served an impression and later visited a destination, verified by a third-party measurement provider. The study provides brands with visitor insights based on campaign performance and visitation activity, such as change in frequency and change in purchases.
AdTheorent also has a partnership with the world’s largest electronic payments network, which enables matching of exposed audiences to actual transactions to measure and analyze campaign impact on in-store sales, online orders and in-app orders.
The second solution is Transaction-based Audiences, where AdTheorent uses past purchase data from major credit card providers to identify audiences. These audiences are tailored to the brand’s goals – for example, to reach frequent dining spenders, online order delivery spenders, weekday spenders and many more categories.
AdTheorent’s custom Audience Builder also enables brands to find specific audiences through a live poll unit. AdTheorent identifies commonalities within the data profiles of consumers who respond, enhancing machine learning models with deterministic data from engaged hand-raisers.
AdTheorent’s Competitive Conquesting solution serves ads to audience members who have frequented competitor restaurants. It can also define a brand’s audience based on the apps consumers use; the ML service identifies the consumers within that audience who are most likely to convert.
On the creative front, machine learning creative selection assigns the likelihood of engagement to each available creative option to determine the optimal message to serve. Elements within the creative unit change dynamically based on real-time data. The machine learning platform designs creative in real-time based on all approved creative elements to construct a unique creative unit in real time for each consumer.
AdTheorent notes that the opportunity for QSR/FCR brands to reach consumers via digital channels continues to surge as consumers are increasingly relying on mobile devices to make dining decisions.
According to figures from Statista, 53 per cent of diners use smartphones to find a restaurant location, 49 per cent use smartphones to browse menus and 37 per cent use smartphones to research new eateries. As the role of digital expands, so does digital ad spend – total ad spend for QSRs has grown 23 per cent year-over-year, according to eMarketer.
AdTheorent works widely in the QSR/FCR space, for brands including Church’s Chicken and Firehouse Subs. “We have seen great success working with AdTheorent to use machine learning to drive incremental visits to Firehouse Subs locations across the United States,” said Marisa Burton, director of field marketing at Firehouse Subs. “AdTheorent’s Cost Per Incremental Visit pricing model is such an attractive option since we only pay for those incremental visits that resulted from ad exposure.”
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