Performance-Based Solutions Designed to Achieve Measurable Business Outcomes
AdTheorent, Inc., a digital advertising leader using advanced machine learning technology and solutions to deliver real-world value for advertisers and marketers, announced a suite of machine learning (ML) solutions for Dining and Restaurant brands and marketers. The solutions enable Quick-Service Restaurant (QSR) and Fast-Casual Restaurant (FCR) marketers to drive measurable business outcomes and are designed for specific campaign goals from increasing foot traffic and visitation, to acquiring new customers and increasing sales.
Consumers increasingly Rely on Digital Channels for Dining Decisions
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: 53% of diners use smartphones to find a restaurant location, 49% use smartphones to browse menus and 37% use smartphones to research new eateries.1 As the role of digital expands, so does digital ad spend – total ad spend for QSRs has grown 23% year-over-year.2
AdTheorent’s Restaurant and Dining Success
AdTheorent has a proven track record of delivering value for QSR/FCR brands. In 2018 AdTheorent successfully executed dining campaigns delivering an average visitation lift which outperformed industry benchmark by 8X and yielded an average rich media engagement rate 33% higher than industry average. Some of the brands that AdTheorent has executed successful campaigns for include Church’s Chicken and Firehouse Subs. Based on the success of these campaigns, AdTheorent has created a suite of ML-based solutions tailored specifically to dining brands’ goals and objectives.
“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, 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.”
“AdTheorent’s machine learning-powered QSR and FCR products drive real-world value for our dining and restaurant clients,” said James Lawson, CEO of AdTheorent. “We are excited to make digital advertising valuable for advertisers as measured by their business goals – including delivering new customers and driving new sales – and we believe performance-based pricing is a big part of that.”
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AdTheorent’s Restaurant and Dining Solutions
Advertiser Goal: Acquire New Customers, Drive Incremental Visitation and Sales Lift
- Cost Per Incremental Visit (CPIV): AdTheorent’s guaranteed CPIV solution drives new customers to restaurants. AdTheorent’s visitation models use ML to learn when and where to reach news customers most likely to visit a restaurant location. This 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.
- Visitation Measurement Study: AdTheorent uses a Visitation Measurement Study to analyze a campaign’s impact on driving visits to a restaurant. Predictive location targeting allows AdTheorent 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.
- Sales Lift: AdTheorent purchase models reach consumers who are most likely to purchase. AdTheorent’s partnership with the world’s largest electronic payments network enables matching of exposed audiences to actual transactions to measure and analyze campaign impact on in-store sales, online orders and in-app orders.
Advertiser Goal: Identify and Reach Core Audiences
- Transaction-Based Audiences: AdTheorent utilizes 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.
- Custom Predictive Audiences: AdTheorent’s custom Audience Builder enables brands to find specific audiences through a live poll unit. AdTheorent identifies commonalities within the data profiles of consumers who respond, enhancing ML models with deterministic data from engaged hand-raisers.
Advertiser Goal: Competitive Conquesting
– AdTheorent helps brands tap into competitors’ customers through:
- Geo-Retargeting: Serve ads to audience members who have frequented competitor restaurants.
- App Detection: 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.
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Advertiser Goal: Start a Conversation
- AdTheorent enables brands to harness the power of word-of-mouth recommendations through Relationship Targeting. AdTheorent analyzes signals to connect and categorize the real-world relationships of a brand’s customers, amplifying the campaign message and leveraging the power of group influence.
Advertiser Goal: Deliver Optimal Creative and Messaging
- Machine Learning Creative: Studio A\T uses high-impact and custom creative embedded with ML technology to ensure that brands deliver the right message. ML 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.
- Advanced Predictive Creative: ML designs creative in real-time based on all approved creative elements to construct a unique creative unit in real time for each consumer.
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