Most senior management teams believe their bank has an attrition problem.
Their assumption, however, generally is based on conventional wisdom. They see statistics showing the average attrition rate for banks in the United States at 14.6 percent. They hear a branch manager mention it seems like the bank has closed more accounts this month than they’ve opened. They get a report back from a team member attending a conference saying that other banks are starting to focus on customer retention.
The reality is that a majority of management teams can’t quantify their own bank’s retention problem. Why? Many can’t access their data in a timely manner and don’t have the analytic capabilities to accurately calculate their attrition rate.
As a result, management is led to believe customer relationships and retention can’t be effectively measured. Therefore, even though they intuitively suspect they have an attrition problem, they’re hesitant to implement a retention program.
That’s why it can be difficult for bank marketers to get management to include retention in their strategic plan. So marketers are tasked with demonstrating that the bank has an attrition problem, providing a strategy supported by data, and proving the strategy can generate measureable results—a positive ROI.
To build a strong case for retention, marketers need access to effective measurement tools like analytics, dashboards and scorecards.
Analytics can help management teams understand the impact attrition has on their bank by assisting them in measuring attrition. Data analysis can not only help calculate a bank’s retention rate, but can also show:
- How many customers are leaving
- Why customers are leaving
- Which customers have closed accounts
- What part of attrition is controllable and what part is uncontrollable
- What customers are at risk of leaving
- The value of winning back lost customers
- The impact of attrition on the bottom line
Analytics should be a key part of any strategy to stop the revolving door of customers leaving the bank. For example:
- The data-mining process can help stem the outflow of new customers by assisting the bank in acquiring customers who value long-term relationships.
- Data analysis can help identify the percentage of single-account households and aid in developing an effective strategy to cross-sell more products to these “at-risk” customers.
- Predictive modeling and event-triggered analysis can help anticipate the next-best product for customers, thereby increasing the number of accounts per household.
Dashboards and scorecards can help marketers prove to management that the key retention indicators are moving in the right direction—and that their strategy is working. The analytics behind the scorecard allow the team to measure important metrics such as attrition rates, cross-sell ratios, campaign results and ROI.
The scorecard also helps the bank communicate and measure strategic objectives that drive behavior at the branch level to strengthen relationships and increase the lifetime value of its customers.
Ultimately, it takes more than intuition alone to get a retention program implemented. You need to be able to demonstrate to management that the bank has an attrition problem, demonstrate that your strategy is supported by data, and prove that your strategy can generate measureable results. Using analytical tools not only can help you build your case, but also help your bank gain a competitive advantage over banks slow to adopt this technology.