What’s my share of wallet?
Few weeks back, for a bank piloting Algo360, we were reviewing how the bank’s Share of Wallet, presumably high, was not consistently high across the high value segment. That is, for a large percentage of their high value customers, in true customer net worth terms, Bank X was not the preferred bank. (1)
For instance, customer A had an aggregated MAB (monthly average balance) of ~Rs. 15 Lakh (across 4 banking CASA relationships). Her relationship with Bank X was at an “net relationship value (NRV) of 3L per month, and she was classified as “High Net Worth”, and not Ultra/Super High Net Worth. Almost all her other financial relationships, from credit cards, to deposits, trading and other investments, etc. were held with a competitor (Bank Y). Recently, she had started using the wallet app of the Bank Y as well. It made us ask Bank X – “has the RM met this customer even once to influence their choices? How do you modify your accounts knowing that her true customer networth is north of ~Rs. 15L?” The answer wasn’t surprising at all. Bank X did not know, before this discussion, that their Share of Wallet was only 20%! Most institutions don’t. They don’t have this data.
The discussions continued to another interesting user – who had silently churned away from the bank. Banks usually know about this segment. In some scenarios, the blame goes to a shift in the salary account, as a customer presumably switches to a different employer. Long back, for a global major, we had looked at the impact of adverse customer service moments as a switch point. However, the insights about the drivers of churn are limited by a lack of information about change in income levels, change in spend behaviour, and any inputs on drivers of reactivation. How about the case where the customer has moved their relationship not just away from their bank, but also from the new salary account bank to a third bank!
There is a need to empower the customer strategists, relationship managers with More Data. More Insights. Businesses need to arm them with these insights On Demand!
Easier said than done, right? We have always been a huge believer of “segmentation” and “personalization”. However, the journey between segments and highly personalized insights is long, expensive, frustrating and confusing. Even today, the cost of an additional “information point” on a customer is significant.
We should review information availability, insight availability and tactical customer action as three priorities that need to be addressed together. Mobile as a channel and existing “alternate data” can address the information availability constraint in real-time. Deep analytics capability in-house or solutions such as Algo360 address insight availability on demand. And these insights need to be readily available for existing CRM implementations need to help drive corresponding tactical customer strategy and action.