Today, loyalty programs are often siloed and limited to the interactions between two axes: the customer and spending. In the best of these programs, a brand knows exactly what the customer is spending and how frequently. On the other hand, while brands have spending data across their own locations, they lack knowledge of what kind of business the customer is giving competitors.
If location-based services began collecting the size and frequency of purchases across all locations and mining the data of check-ins (including likes and dislikes), they could begin to build the next generation of loyalty rewards programs comprised of customer, spending, location, and sentiment. Such a program would benefit location-based service providers, brands, and customers alike.
Take this example: if every day a consumer purchases a latte from Starbucks and then walks across the street to Dunkin’ Donuts to pick up a turkey sausage flatbread, both companies could benefit from that information. If many customers display similar habits, Starbucks could add a similar breakfast sandwich to their menu or even discontinue their current breakfast fare at that location.
That level of data provides a more holistic view of consumer behavior, and could ultimately help brands become more relevant and timely. In the example above, in addition to knowing consumers’ breakfast sandwich habits, Starbucks could also learn whether individuals go to Starbucks all or most of the time for coffee. The company could then use that market insight to offer coffee-consumers individual promotions to try their food items, instead of promotions for coffee which the consumer already gladly purchases at full price.