Case Study: Boosting retail traffic with Predictive Mobility
Among the many changes we’ve experienced over the last two years, we’re also behind the wheel more than we used to be. In fact, according to Arity data, people drove approximately 10% more in 2021 compared to 2019 before COVID-19 was even a known thing.
At the same time, shopping via mobile devices is also on the rise with mobile commerce sales expected to reach nearly $437B in 2022.
With people on their phones and in their cars more often, retailers can increase traffic by learning from their customers’ driving habits and patterns. With data on where they go, how often, and when they drive – there’s opportunity to not only personalize those moments when they’re shopping, but also predict when they’re likely to shop.
Using data on driving habits of over 40M people across the country, Arity analyzed driving data in a major metropolitan area to evaluate the opportunity this could offer to a major retailer, referred to as Ready Retailer in this scenario.
Arity data of trips taken in one week around the retailer’s locations, noted in yellow. Teal indicates drivers that passed but did not stop at locations.
Our analysis found that in one week, drivers passed Ready Retailer’s 50+ locations about 1.2 million times. Of those 1.2 million trips, we found that 120K of them, approximately 10% of total trips, passed their retail locations regularly at similar times of day.
How could Ready Retailer encourage customers who are already in the area to stop by more often? By predicting when customers will pass by, Ready Retailer could send personalized ads via their mobile app to customers right before they get in their cars. Moreover, with data on when and where they tend to stop, Ready Retailer has even more opportunities to tailor the message and reward the customer based on their specific preferences.
For example, let’s look at this driver that makes the same trip every weekday at 3 PM. Because this is a recurring trip that passes one of Ready Retailer’s stores, Arity pings the retailer with the insight that this driver is a good candidate for a predictive offer.
Now, Ready Retailer has two options to send offers to the driver – before they leave home, and before they make the return trip and pass the store again. By catching drivers before they start their trip, the retailer captures more of the driver’s attention with an offer that’s easy to remember and redeem. “Free cookie with the purchase of any coffee between 3-5 PM” may be just the perfect afternoon snack to bring them in.
Creating more meaningful connections with customers is invaluable for retailers. For Ready Retailer, Arity discovered the opportunity to increase retail traffic by 10% by predicting when customers will be in the area. And, the opportunity scales as consumers continually demand seamless, frictionless, and personalized shopping experiences.
Arity discovered the opportunity to increase retail traffic by 10% by predicting when customers will be in the area
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