It takes months to find a customer...seconds to lose one.
- Vince Lombardi
Within the financial institution (FI) space, it is known that the stickiness customers have with their FI can make it a challenging feat to convince them to make a switch. At a cost of more than $300 to acquire a new customer1, and FIs losing 10-15% annually of gross revenues to attrition,2 can FIs afford to not slam the door on attrition? Read on.
Having a pulse on the satisfaction levels of FI customers often brings more questions than answers. For example, why did a customer choose to take out a loan with RocketMortgage? Why is there a decline in debit card swipes? Why did a customer’s recurring payments to TMobile drop off?
Research indicates that attrition affects a whopping 30-50%2 of an FI’s customer base when tracking gradual declines in product usage, individual account closures, and full customer exits. Because of this, FI’s need to employ a data-driven strategy aligned with having an on-demand process in place to identify customers with potential churn risk before it’s too late.
Managing attrition is not a new paradigm hitting the financial industry; however, consumer banking behavioral shifts to fintech disruptors and fluctuations in purchase spend patterns brought on by the pandemic have brought heightened concerns and a greater need for a solution to stem attrition.
The importance of retention and having a modern-day approach to battling attrition is directly tied to the bottom line for a financial institution. Profitability is a function directly tied to lifetime value — the longer your relationship with a consumer, the better chance the consumer will become profitable3. While the thought may be your institution has a grasp on the profitability of your customers and their individual churn risk, contemplate these statistics:
Still thinking your FI has a grasp on customers ready to attrite? Consider this. In order to replace just 10% of customers leaving with new ones, an FI will spend 4x more than they would have with retention efforts for the same audience. How big is your budget?
Not all attrition models are created equal. Some focus on identifying product closures – yet, by that point it might be too late. Others focus on identifying traditionally slow-moving data, such as branch visits to predict churn – introducing a roller coaster of variables, fluctuating often, and providing a false sense of churn indication.
A better approach is to look at the full universe of customer data – both product data as well their everyday purchase spend data – providing a more holistic view and helping an FI identify the early warning signs that a customer is reducing their commitment to the institution. Models that leverage a customer’s spend transactions, held-away payment activity, banking behaviors, and product mix consider the full 360-degree view of the customer in evaluating their likelihood to attrit. It’s the uniqueness of the transaction data – such as identifying customers making micro-deposits into Chime, or a drop off in automatic withdrawals of car insurance payments, or an increase in the number of monthly payments made to competing institutions – combined with product data that is the predictive modeling approach FIs need now.
Traditionally, it can take 90-120 days or longer for an institution to deploy a predictive attrition model. By then, the results are stale. This is far too long in modern banking.
The new norm is on-demand access to the list of customers likely at risk – always updated, and always accurate based on the most recent transactions processed by your customers. Access to this data, when you are ready to act on it, puts you in the control seat.
In our next blog, learn more about Segmint’s predictive attrition model and why it’s different.
