Driving Client Loyalty with Modern Data Methodology

Credit card rewards can help financial services companies provide incentives to customers for repeat business as part of their loyalty program. According to a survey from U.S. News & World Report, 17.7 percent of customers choose a credit card based on the rate of earning rewards. An additional 14 percent of respondents report that they choose a rewards program with a specific brand. In fact, factors tied to loyalty and rewards programs are more important considerations than annual percentage rate and annual fee when choosing a card. However, not all rewards are beneficial to the customers, and not all customers are beneficial to the issuing company. Insights derived from analytics of incentives and rewards data can help companies inform the design of their loyalty programs.

In many loyalty programs, customer engagement analysis is a tedious process and is usually the job of one person manually building monthly reports based on member experience, rewards processing, redemption, and administrative effort (billing reports).

In our recent work with a loyalty card client, there were 30 distinct reports hosted on the age-old tool Hyperion. These reports were run every month by one person and sent out to customers manually. We helped migrate and automate this process by collaborating with the client product owners to design NoSQL-friendly data models that support the product requirements and the strategic vision to modernize data architecture. This new architecture enabled the client to marry diverse data points that had previously been inaccessible. The enhanced visualizations connected data across functional teams and anchored analytics around customer lifetime value.

The innovation did not stop with the data analysis. Our team redesigned the client’s reporting with consolidated and custom dashboards using Tableau. In this portion of the project, we identified four critical categories for the analysis:

  1. Spend: Total and average spend allowing for a drill-down of the same by different dimensions such as Merchant Category, Merchant Segment, and Description.
  2. Redemptions: Analysis of customer point redemption, cost per point, and an overview of the key metrics such as total redemption cost and total points redeemed.
  3. Rewards: Trends over time on how the account is performing in terms of outstanding rewards liability metrics like beginning balance, ending balance, total rewards earned, and total rewards deducted.
  4. Account: Status of total number of accounts such as active, spending, and redeemed.

There are countless benefits associated with taking a modern data approach to analytics, including greater efficiency with time commitment, resource allocation and spend. Data visualization provides consolidation that enables enhanced trending models, improved decision making with fewer reporting points, and greater visibility and access to key, high-level metrics. These new features can be easily filtered by time range and other more granular details of customer groups.

Organizations have an opportunity to invest in the power of data and analytics, migrating away from the manual, tedious job of building reports and building modern data architecture and visualization into organization strategy. This ultimately enables increased visibility into patterns that inform the design of enhanced reward programs based on customer needs and spend trends—while simultaneously maximizing monetary profit value.