Improving Client Segmentation: Increasing Wallet Share by Identifying Hidden Assets

Improving Client Segmentation: Increasing Wallet Share by Identifying Hidden Assets (July 2019)

Financial institutions and their affiliated broker dealers are investing in the development of omni-channel delivery strategies, adding digital advice platforms and investment call centers to serve less profitable clients with small accounts currently served by the traditional across-the-desk advisor model. But Kehrer Bielan research has found that 13% of banking customers have more than $100,000 in investable assets held outside their primary banking institution, what Kehrer Bielan calls “hidden assets.”

It wouldn’t matter as much if a financial institution assigned someone with hidden assets to a digital or call center delivery model if households with hidden assets were indifferent about how they receive financial advice. But there is evidence that some households care very much, so channeling them to the wrong delivery model risks throwing out the baby with the bathwater.

How can a firm identify which clients have hidden assets, and how they prefer to be served? Demographic databases are expensive and appear to be not very useful in targeting individual households.

In Improving Client Segmentation, Kehrer Bielan and LPL Financial Institutions collaborated on analysis of data from a large national survey of financial decision making to demonstrate that a financial institution can use data it already has on banking and investment product use to dramatically improve its guess about which clients have hidden assets and how they want to be served.

The study examined the likelihood that households who use any one of 35 banking or investment products at their primary financial institution hold more than $100,000 at another financial institution or asset manager. After identifying the products which are the best “markers” identifying the existence of hidden assets, we then examined the likelihood of having hidden assets if the household had various combinations of the markers. The results demonstrate that targeting households according to the banking and investment products they own is a sound approach to identifying households that merit more extensive follow up before they are assigned to a digital or call center advisor.

The study followed a similar approach to identify the combinations of banking and investment product use that indicate which households with hidden assets are open to receiving financial advice remotely, and those that will only work with an advisor face to face.