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"Targetable is a store OS. Executing good marketing ideas becomes systematically foolproof."

Lee Peterson
Former SVP
Guest Experience
Buffalo Wild Wings

Former Director, Restaurant Systems, Applebee's. 

Watch & Win

  • Real-time sports data, connected to in-store promotions, creating localized & relevant events in every store - minus the labor or cost. 

  • BWW set up the rules (ex: local football team scores touchdown = Buy1Get1 free beer for 15 minutes.) Targetable executed the rules automatically in any location, any country, and any covered sport, including Saudi Arabian League Soccer!

  • A combination of software and execution at the store level resulted in big wins in consumer behavior. BWW’s own internal analysis found that: revenue, driven by beer sales, went up 3%; dwell, accounting for people waiting to see if events “pay” off saw a 6% lift; and check size, heavily impacted by dwell, skyrocketed 16%.

Draft Board

  • While not as entertaining as Watch & Win, the Draftboard product designed, launched, and operated by Targetable chainwide, delivered substantial, material results through its innovative use of real-time data and machine learning.

  • Targetable first integrated BWW’s massive store-unique beer inventory, then using BWW-created rules assembled promotions displayed in-store closely matched that location’s demographics, tastes, and consumption patterns.

  • Once the above info was stable and solid Targetable designed and launched machine-learning-enhanced food & beverage promotions which used rapid A/B testing to identify the most profitable food/bev bundles and then promoted them to gain profit. 

  • Draftboard fundamentally changed how BWW promotes, bundles, and markets their food and beverage to consumers chain wide, delivering data-driven upside for every store.

Menu A/B Testing

  • As briefly described in Draftboard example, Targetable has a history of using rapid A/B testing to gain insight through data, and help customers make informed decisions.

  • Historically, testing is used to find optimal margins, match local demographics,  and create sales and profitability lifts for customers.

  • Typically delivered to in-store screens and digital marketing endpoints, these machine-learning-informed ads deliver results by finding the data (demographics, sports, traffic patterns, weather) that impact metrics the most at individual stores.