Propelled by the need for speed, the desire to enhance customer experiences and mandates to reduce operating expenses, artificial intelligence (AI) is benefitting companies around the world. Financial institutions are no exception. Community banks and credit unions that embrace AI can increase market share and drive more cross selling of products and services within their existing customer bases.

Artificial Intelligence and Loyalty

Customer loyalty data represents a rich data source available to feed AI algorithms. Combing through reams of customer touchpoints, AI portrays a data story of one’s personal shopping behavior to provide more targeted communications and relevant offers and services. Members benefit from more personalized loyalty programs with more control over how rewards are earned and spent. As a result, online and in-store customer journeys improve and cross-sell opportunities increase.

HSBC’s card program, using Maritz Motivation Solutions, recently ran a promotional campaign to 75,000 loyalty rewards customers. The goal was to predict the percentages of program members who would redeem their points in four different redemption categories based on AI recommendations. Each cardholder was sent an email with one recommendation. Results were impressive – an open rate of 40 percent for the emails, and 70 percent redemption in the recommended category among those receiving the targeted messages and redeeming.

It’s Still the Wild, Wild West

Companies are racing to embrace it, but we’re still very much in the early days of AI. Although the outlook for AI is bullish, it will take time to determine how best to leverage this powerful tool in the contexts of their businesses. In the wake of record numbers of data breaches, companies also must put consumers in charge of what personal data they are willing to share and how it’s used. The question of how much and what kind of data consumers are willing to trade for more relevant interaction with a brand remains to be answered.

For community banks and credit unions, start with small projects upon which early successes can be built. Learn through iterations. Assess what works. Hone the algorithms and make necessary course corrections. And remember that human behavior will never be 100 percent predictable.

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