As you exit the office, your phone alerts you with a notification: a nearby shop is offering a discount on a smartwatch, complete with a 20% off coupon displayed on your app. Intrigued, you decide to take advantage of this timely offer.

This scenario is a classic example of a push promotion: a retailer proactively sends a notification through an app to catch your attention. This strategy has been a staple for online giants like Amazon. However, Jason Duan, an associate professor of marketing at Texas McCombs, believes that physical retailers, ranging from small family-run shops to large chains like Target, can also effectively utilise this method to enhance competitiveness.

Jason Duan and Vijay Mahajan, another marketing professor at McCombs, delve into this topic through their latest research. They explore the intricacies of how, when, and what kind of push notifications are most effective in driving customer visits to physical stores. Their study particularly emphasises the potential of location-based notifications, which are sent when a consumer is near a store, as opposed to behaviour-based notifications that rely on a user’s previous shopping habits. While companies like Amazon depend on behaviour-based strategies, physical stores might benefit more from leveraging location-based notifications.

According to Duan, this approach could provide a significant edge as retailers possess vast data reserves that could help optimise location- and behaviour-based notification deployment.

To assess the effectiveness of these two promotional strategies and their potential synergy, Duan and Mahajan collaborated with Zhuping Liu from Baruch College. They developed a model simulating shopper behaviour, incorporating data from a company that sends coupon notifications through a mobile app. This dataset included detailed logs of when notifications were issued, when and where consumers engaged with the app and its coupons, and their subsequent store visits, all with the consent of the participants. The study tracked 5,000 shoppers in a mid-sized American city over 120 days in 2015.

Their findings revealed distinct advantages for each type of notification. The timing was critical for behaviour-based notifications; if sent before a shopping trip, they significantly increased the likelihood of app engagement and store visits by up to 23% compared to those without notifications. On the other hand, location-based notifications proved most effective when the user was already near or within the store, enhancing the probability of opening the app by 27% and clicking on coupons by 22%.

Moreover, integrating both notification types resulted in even more compelling outcomes. For instance, if a user engaged with the app outside a mall, they were 35% more likely to use it again inside the mall.

Duan suggests a strategic approach where a retailer could send a behaviour-based notification when a customer is planning a shopping trip and a location-based notification at a pivotal moment during the store visit. For example, a notification about a shoe sale might be sent as the customer prepares to leave home, followed by a coupon offering additional discounts once they arrive at the mall.

However, Duan cautions against excessive repetition of notifications, which can become irritating for shoppers. Instead, he advocates personalising notifications as much as possible to enhance their impact. With advancements in AI, wearable technology like smartwatches, and real-time location tracking, businesses can tailor each notification to match customer profiles and preferences.

For stores lacking the capability to collect and analyse such data, partnering with external companies specialising in gathering and dispatching push notifications can be an effective solution. This enables smaller retailers to stay competitive with larger online counterparts, like Amazon, in the rapidly evolving retail landscape.

More information: Jason Duan et al, Push and pull: Modeling mobile app promotions and consumer responses, Quantitative Marketing and Economics. DOI: 10.1007/s11129-024-09289-w

Journal information: Quantitative Marketing and Economics Provided by University of Texas at Austin