Case Studies: Chapter 6 – Financial Services Institution

A Financial Services Institution in the Netherlands Pioneers Behavior-Based Omnichannel 1:1 Marketing

One of the most mature next-best-action programs ever has been implemented at a Dutch financial services institution during the heyday of Enterprise Marketing Management. The system integrated information about customer behavior from all their channels, online and offline..

Additionally, the system listened to customer responses carefully to avoid annoying customers by repeatedly presenting offers they weren’t interested in. For example, if the customer repeatedly saw an offer for wealth management services on the website but never clicked on it, the system would not prompt call center agents to present this offer again. Instead, it would learn from response attribution and adjust the customer’s propensity score on this offer. That would then surface a different next-next best offer to present instead.

This ensured relevancy for the bank’s customers and increased bottom-line revenue from their customer marketing efforts.   

Before enriching with Experience Analytics data

Customer-centric marketers have already favored behavioral data as input into their predictive models for over a decade. In the past 5 – 10 years, this behavioral data increasingly included not just transactions but website interactions, e.g. product pageviews and carts. However, there are gaps of insight:

  • Are the customers interested in the products? Where do they stand in their consideration lifecycle? 

  • Did anything happen to interrupt their journey if the customer did not transact? Why did they abandon it?

  • In many businesses, a lot of time goes by between purchases. So, past purchase history may not predict what a customer is in the market for “in the moment”

After enriching with Experience Analytics data

By watching customers’ digital body language and their experience, next-best-action engines have access to a much more humanized understanding of their customers. Going back to a financial service example:

  • Is the customer actually in the market for opening a wealth management account in the near future,  e.g., studying the various types of accounts even if they didn’t click anywhere on the page? Or were they just briefly glancing at the offering?  

  • Was the customer even trying to open the account but ran into a struggle with the account forms?

  • Was there even a website error or performance issue that killed their attempt at completing the account opening? 

These insights will better inform what the customer would expect and appreciate hearing next from the brand. Again, it’s similar to what we’d expect from a more human relationship.