Looking at Customer Routines

 An area we studied.   AI was proposed in one case.  Like the direction.

With Predictive Analytics, Companies Can Tap the Ultimate Opportunity: Customers’ Routines

31 MAY 2023| by Rachel Layne   in HBSwk

Armed with more data than ever, many companies know what key customers need. But how many know exactly when they need it? An analysis of 2,000 ridesharing commuters by Eva Ascarza and colleagues shows what's possible for companies that can anticipate a customer's routine.

If knowing what customers need is marketing gold, pinpointing exactly when they need it may just be platinum.

Services that become part of a customer’s routine may deliver advantages beyond repeat business for a company, Harvard Business School Associate Professor Eva Ascarza and colleagues find in a new working paper.

“We find that routine customers have higher value to the organization, even after controlling for their level of consumption,” Ascarza says.

These customers may also tolerate price increases better and even stay loyal longer when things go wrong compared with customers who haven’t made a service part of their routines, the authors find.

These findings come as companies such as Procter & Gamble, Adidas, and McDonald’s are trying to collect more consumer data to hone their marketing messages. With artificial intelligence (AI) opening new possibilities in the noisy world of digital marketing, companies are looking for new ways to gain an edge with fatigued customers. Harnessing customers’ routines may offer a compelling new opportunity.

When services such as ridesharing are part of a routine—even if that routine isn’t obvious to the user—firms may be able to pinpoint a customer’s motive more precisely than for people who use the service casually or merely as a preference. That may help companies carefully tailor both marketing and service for their most valuable customers, the authors find.

Ascarza teamed with Ryan Dew from the University of Pennsylvania’s Wharton School as well as Columbia Business School’s Oded Netzer and Nachum Sicherman to develop the model that identifies routine users and their value.

Not all rides are routines

To track how targeting routines may work, the authors teamed up with a rideshare company in New York City and tracked some 2,000 users, homing in on passenger usage data between January and November in 2018. After a rider had been active for three weeks, the authors tracked how—and, more important, when—customers used the service.   ... '