Businesses like quick service restaurants (QSRs) captured a significant amount of first-party customer data at the start of the COVID-19 pandemic as new digital technologies like contactless payments expedited delivery, drive-thru and pick-up services. Knowing how to stitch that data together to understand customer behaviors and preferences will help brands successfully grow customer loyalty via personalized offers. However, truly personalized offers remain a challenge for many organizations.
Test-and-learn, as a component of marketing automation, is the solution organizations are searching for, but the process often gets overanalyzed. Some businesses spend years trying to connect disparate data to create a single view of the customer – such as with a customer data platform (CDP). But are they wasting resources and failing to target the right customers with the right offers at the right time?
With myriad communication methods available to consumers, companies that serve personalized offers make purchasing decisions easier and help companies better understand the appropriate customer value exchange. According to a survey from PYMTS.com, more than 46% of high spending, high frequency customers said discounts and offers were a top reason they ordered directly through a restaurant versus a third-party aggregator.
Although discounts may be a top motivator to get customers through the door, data has shown that discounting isn’t always the right approach. Companies should be targeting personalized offers to customers who wouldn’t have otherwise made a purchase rather than offering discounts to people who would have made purchases anyway.
Businesses need to adopt a data-driven, test-and-learn automation approach to create personalized customer experiences and avoid the pitfalls of mass-marketing practices like discounting.