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%.
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.