Research shows that by employing predictive analytics and arming relationship managers with data-driven insights and enablers, financial institutions can reduce total attrition by 20% to 30%—a result that would nearly double most banks’ average revenue growth.²
In the first blog of our Attrition series, Slam the Door on Attrition, we stated that not all attrition models are created equal. Attrition models that dig deep into the Financial Institution’s (FI’s) full universe of customer data – both product utilization and everyday purchase spend – help 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. 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 is the predictive modeling approach FI’s need now.
Customer retention is critical in the ultra-competitive environment we live in³ – an attrition model can help an FI proactively limit the impact of churn. However, to make the predictive model effective, it must include three key concepts: better data daily, on-demand access, and low-friction integration bundled with personalized touchpoints.
How useful is data in driving strategic business decisions when that data is months old? In the fast-paced, high-speed environment we live in where the expectation of consumers is now, outdated data can certainly become a hurdle to gaining profit. Models built on data that is even just 30 days old are out of date – many customers will have already left by the time any action can be taken.
Every FI has access to their customers’ financial journeys through product consumption and everyday purchase transaction history within its core. Customers’ financial behaviors, their patterns of spend and shifts in spend can be identified through their purchase transactions
Leverage this data. Leverage it daily. Use this data to identify behaviors that may be indicative of churn. Held-away payments, decline in debit card usage, or change in recurring payments may be indicators pointing to customers likely to attrit. Continuously feeding this data into your model helps your model stay in sync with your customers’ ever-changing behaviors. Better data – customer product utilization and everyday purchase transaction – is the rich data that should be driving every attrition predictive model.
Traditionally, it can take 90-120 days, or longer, for an institution to deploy a predictive attrition model. By then, it might be too late. Customers in the early consideration phases of leaving their financial institution may have already taken irreversible steps.
A predictive model is most effective when it evaluates new data daily, and provides results through an on-demand approach. Incessant optimization and calibration allows an FI to access these predictions when needed when the FI is ready to take action and reach out to their customers that are likely to attrit.
The good news is that banks have the ability to turn things around. But doing so takes timely insights, and thoughtful and proactive engagement. . .
– How Banks Can Close the Back Door on Attrition, Boston Consulting Group
Integration is a component of an attrition model that brings together all the components of data, operations and people, allowing the process to work seamlessly and efficiently. When you build a repeatable data integration, an FI can automate the flow of data, freeing up resources to focus on the business of banking and outreach to grow customer relationships. Within the FI’s ecosystem, revenue producers and customer-facing staff need access to these insights.
Use this data to personalize messaging to customers and build relationships improving their likelihood of becoming profitable. Not only should customer data be used as inputs to your attrition model, but this data should also be used as a guide to identify customers worth saving based on their behaviors with the Financial Institution.
For example, focus your initial engagement efforts on high-wealth customers likely to leave. Request that their personal banker reach out with a phone call to express their appreciation and offer your concierge services. Or, reach out to your long-tenured clients, thank them for their commitment to the institution and ask them about their recent experience with the institution. A personal touchpoint should never be underestimated in building long-term relationships.
Traditionally, legacy attrition models in the industry rely on slow-moving data that becomes stale very quickly. At last, FI’s can have on-demand visibility to customers at risk of leaving the institution – 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 control.
Segmint’s Attrition Model, through consumer behavior modeling, proactively limits the impact of churn for financial institutions of all sizes. Simply put, transaction data is the key. Through our patented AI-driven analysis of every transaction, Segmint assigns Key Lifestyle Indicators® to customers’ describing spend patterns, held-away payment activity, banking behaviors, and product mix. Changes in activity can be indicators of customers at high-risk of leaving an institution in the near future. The model uses better data, produced multiple times throughout the day – your customer’s data – continuously optimized and integrated resulting in the ability to manage attrition throughout the financial institution. Slam the door on attrition for good.
This article was authored by Marla Sferra-Pieton, VP, Marketing, and Joan Clark, VP, Product for Segmint, Inc, a provider of a data insights platform that cleanses, categorizes, and contextualizes insights from customer financial transaction data. Click here to learn more about its Attrition Model product offering for financial institutions.
