Major financial institutions such as American Express, Bank of America, Chase and Citi offer credit cards to hundreds of millions of consumers in the U.S. alone. However, the work doesn’t stop once a customer opens up an account. Millennials have an average of 2.5 credit cards each, and Baby Boomers average 3.5 each. With an average of three cards to pick against (Bycer, 2020)i, these institutions need to find ways to incentivize customers to use their card offerings. Rewards programs have become the most attractive mechanism to promote loyalty but how can credit card issuers maximize their share of wallet in this wave of consumerism?
Currently, financial institutions acquire new customers using traditional digital marketing modeling techniques. This can be very effective, but what about the increasing capture of data from customer interactions with all of the stores and brands they come into contact with each day?
Each and every touchpoint in customers’ journey creates a wealth of information including search and purchase patterns, as well as which stores and brands they support. In the past, traditional reporting on these data sources could only provide a look back at what happened. However, the incorporation of analytics will unlock not only what happened, but also why and what to do as a result. These actionable insights can guide savvy financial institutions to strategically create the right incentives and rewards programs for their customers and promote brand loyalty for themselves.
Leveraging multidimensional data sources and experimentation models, financial institutions can make informed decisions about the right channels to best reach their customers and the evaluation of merchant partnerships. The availability of rapid iterations can enable the right offering for each customer much quicker while minimizing the risks posed under conventional annual planning models.
The challenge then becomes how these activities are measured. Today’s consumers are interacting with brands through a multitude of touchpoints, from their websites and mobile apps to physical in-store visits. Despite the increase in digital interactions that consumers have with brands, this is only a part of the equation. Gaining a full understanding of the customer, and their interests, it requires connecting their digital and offline identities. Using machine-learning algorithms and advanced data analytics, financial institutions can connect the disparate data sources to determine which customers are interacting with brands online and in-store.
Furthering the availability to understand the interplay of customers’ digital and offline interactions is the ability to create seamless experiences on mobile apps that merge the two. For example, customers may make an in-app purchase, and then pick-up their order in-store. By keeping customers engaged within the app, brands can see how customers interact with the brand throughout the entire purchasing process.
One of the key goals for any brand is to identify high yield customers. But simply identifying these customers isn’t enough for a brand’s success. Identifying high yield customers, combined with an understanding of where and how often they visit stores will move the needle. This creates a path to optimal marketing campaigns for rewards and loyalty programs through the appropriate delivery channels. These insights, cross-referenced against a brand’s foot traffic and social trends, are what will give businesses the greatest potential to reach their target customers in a way that truly resonates.
A total of eighty-one percent of consumers agree loyalty programs make them more likely to continue doing business with a brand (Autry, 2017)ii. If brands and financial institutions can pinpoint which loyalty programs will meet consumers’ needs, they’ll be gaining not only customers but brand loyalty as well.