As the unanticipated impact of the pandemic magnified the need for online to offline (O2O) solutions and order ahead for convenience stores, so too will the pending cookie apocalypse by Google drive the business case for autonomous check-in and checkout.
Verifiable O2O attribution from known users commands the highest trade promotion, placement and slotting fees, which for most retailers and convenience stores exceeds EBITA. Of the $254 billion spent annually on advertising and promotions, 95% is derived from consumer packaged goods (CPGs), brands, product makers and service providers vs. 5% from merchants themselves.
The highest trade promotion, placement and slotting fees go to those that can provide a proven SKU-level audit trail of net new sales from registered customers that opt in to select offers in advance or anonymously aggregated through cookies using O2O tracking to measure purchase intent. Grubhub, Instacart, Amazon, PayPal Honey, Venmo, petroleum-brands and other order-ahead mobile apps possess this data and are using it now to increase their proportion of the O2O promotional spend by those stocking the shelves of convenience stores.
An early commitment to autonomous check-in and checkout will give convenience stores a chance to not only preserve their trade promotion, placement and slotting fees but increase them by brokering the attribution data to advertisers. To be fairly compensated for the O2O infrastructure they enable, convenience stores will need a check-in strategy centered on preserving and negotiating their data rights from the platform providers installing autonomous check-in and checkout platforms. This is especially important for c-stores that lack a customer data platform (CDP) integrated with their own branded, proprietary mobile app and end up relying on the check-in and checkout credentials of third parties.
If the c-store is not the system of record (SoR) for the customer data, either opt in at the individual app user level or O2O through anonymized cohorts using cookies, (or a likely successor, Google’s Federated Learning of Cohorts, FLoC), they are not in the pole position for negotiating promotional rates based on attribution. FloC will raise the advertiser preference for and promotional premiums paid for opt-in, known user profiles (vs. anonymized FLoC cohorts) derived from a retailer’s own branded apps.
All of Google’s primary services (e.g., search, maps, email, etc.) are provided at no cost to consumers and are funded by attribution derived from “in-context search” O2O tracking of customer purchase intent with cookies. AI-superpowers, with or without c-stores and other retailers, will aggregate O2O proprietary and third-party data sources with the information gained from autonomous check-in and checkout such as door swings, footfalls, journey tracking, eye-gaze, dwell time, shopping lists, SKU-level basket data using predictive analytics to continuously enhance the customer experience based on intent, as do Amazon, Google, et. al. when you log in to their sites.
You get what you measure, and most c-stores don’t possess the infrastructure or the technical talent to measure these things today. So committing blindly to autonomous check-in through disintermediating third-party or mobile wallets may seem like the only solution, but it’s not without the data risk.
Look no further than the restaurant industry, which savvy convenience stores consider a fierce competitor, which was largely forced to commit to third parties for their order-ahead platforms such as Grubhub, Uber Eats, etc., which ultimately cost them up to 30% of every order.