Here are five things bank marketers should consider when contemplating a marketing AI platform.
- Familiarity with financial services data. Platforms that are tailored to the world of financial services will have clearly-defined data models that lighten the lift for overstretched data engineering resources and speed up time to deployment. Even better is a familiarity with core data systems and implementation partners who can pull the necessary data directly.
- A robust financial services metrics library. Algorithms that clean and enrich customer data — in effect generating new and useful attributes that drive differential marketing interaction decisioning — are some of the most important determinants of quality. The extent to which your marketing provider has a robust library of battle-tested metrics for financial services data will dramatically speed up your launch dates – and your race to ROI.
- Orientation to relevant customer applications. Marketing technology that is built for financial services will have out-of-the-box configuration (product definitions, eligibility rules, optimization metrics) so that marketers can be productive immediately without needing to expand their team, agency or consultant rosters.
- Optimization against downstream KPIs. Unlike general retail, the way in which a customer ends up using a financial product has a direct impact on profitability. That’s why it’s important for marketers in this vertical to be able to move beyond clicks and opens to optimize interactions on the true drivers of business value (primacy scores, trailing deposits, trailing loan utilization).
- Strong model guardrails, ability to audit and compliance controls. Ensuring no bias becomes embedded in AI software is absolutely essential to avoid the possibility that certain groups are unfairly rewarded over others. Your AI marketing engine cannot be a black box. It must be easily configured on the front end to ensure decisioning is never driven by potentially bias-reinforcing data variables (e.g., gender). In addition, the platform should be fully auditable on the back end – with clear visibility into the decision logic behind the delivery of a particular marketing experience.
Today’s consumers expect the companies they deal with to be “learning” about them as they move around digital environments, adjusting their product and marketing interactions to better meet their needs on the fly. The hard reality is that financial institutions have a long way to go to make this kind of predictive personalization a reality. Marketing technology holds significant promise, but the market is awash in solutions claiming personalization. Knowing how to evaluate the sea of options and identify the right provider is critical.