Automating test-and-learn helps businesses optimize personalized offers to improve customer experience and maximize effectiveness of marketing campaigns.
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.
What is test-and-learn marketing automation?
Legacy test-and-learn software has allowed creatives to experiment with messaging, but it typically requires a considerable amount of time and effort to set up, launch and analyze. However, modern test-and-learn automation generates audience-level data with short, high frequency experiments. Marketers learn whether an offer-centric campaign will be profitable in a matter of days or weeks. It’s a low-risk strategy to quickly determine the effectiveness of certain campaigns for both modeling audiences and driving campaign success at scale.
Test-and-learn automation accelerates an organization’s personalization efforts with meaningful data models such as product relevancy, purchase propensity and reengagement of customers who may have churned out of the system. This approach provides specific data and related attributes, allowing companies to delve into audience segments and set up targeted campaigns. The related data models get smarter over time through machine learning and artificial intelligence (AI) while providing marketers with real-time data visualization to help drive insights and future campaign ideation.
The future of marketing will combine AI with strategies such as test-and-learn, which allows marketers to scrutinize the effectiveness of their personalization strategy quickly and accurately. Today’s technology removes the “gut check” and provides assurance that marketers are balancing consumer expectations with business value. For example, test-and-learn can help determine which offer will resonate with a given audience, driving higher average order value or profitability.