1. Low-Friction Integration
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 a repeatable data integration is built, 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.
2. On-Demand Access
Through our patented AI-driven analysis of every transaction, Segmint’s AI Platform is a market differentiator because it was purpose-built to consume Segmint’s proprietary Key Lifestyle Indicators®, or KLIs. KLIs are ideal inputs to predictive models because they solve the most difficult and time consuming part of data science which is cleansing and normalizing data. These KLIs are assigned to account holders describing spend patterns, held-away payment activity, banking behaviors and product mix. Changes in activity can be indicators of account holders at high-risk of leaving an institution in the near future, which are accurately identified by the model. Clients are using the results of the model to execute automated marketing and personal engagement campaigns to prevent the likelihood of account holders attriting.
For community banks and credit unions, this solution provides on-demand access and a reliable path to identify account holders at risk, and visibility into correlated behavior patterns.
3. Daily Data
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 In the fast-paced, high-speed environment we live in, where the expectation of consumers is now, outdated data can certainly become a hurdle to gaining profit. Models built on data that is even just 30 days old are out of date - many customers will have already left by the time any action can be taken.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 account holders’ financial journeys through product consumption and everyday purchase transaction history within its core. Account holders’ financial behaviors, their patterns of spend and shifts in spend can be identified through their purchase transactions.
Leverage this data. Leverage it daily. Continuously feeding this data into your model helps your model stay in sync with your account holders’ ever-changing behaviors. Better data - customer product utilization and everyday purchase transactions - is the rich data that should be driving every attrition predictive model.