Using Your Data to Reduce Attrition in Banking

Editor's Notes

1. Would tell the background story to this slide with an anecdote story of a customer need for what we were creating
2. Involuntary: financial attrition when a customer no longer has the finances to continue doing business with the bank or can no longer afford the product; customer dies; customer relocates and no longer continues the relationship with the bank Voluntary Churner: commercial churners- when a customer consciously decides to stop doing business with the bank Rotational Churner - two scenarios: Customer drops a product and purchases another product within the same bank to obtain better conditions (e.g. if you become a customer, you receive a promotional offer, take the company up on the offer and close the existing product in order to qualify for the new offer. This is a ‘fake’ new customer by opening the closed account – with the new deal that was offered) Customer constantly (partially) switches between companies depending on where he can get the best conditions. (e.g. bank customer transfers money from savings account to competitor, having an interesting savings account offer.) The most effective churn situations to address are for the customers who churn voluntarily, not involuntary churners nor rotational churners. Need image (graphic designs not photos) illustrating the different churners. Turn into a visual Ability to act on lack of activity People are used to acting on activity Behavior – key differentiator can be activity and lack of activity Customer walking out of the door Don’t include involuntary churn There are 3 types of churners and we’re focusing on voluntary churn for this Identify the different types so you don’t waste $ on the wrong types
3. BOTH CAN BE MONITORED FOR (IN)ACTIVITY AND TREND, AND TO GENERATE ALERTS Predictors – Traditional scoring Banks have sophisticated models from historic data not based on usage (could be account balance checks, web data etc) Banks have been focused on historical models Combining historical data and real time behavioral metrics Product propensity Behavioral and interaction data is important Previously, models build on top of slow moving profiles Only did churn scoring once a quarter bc values didn’t chance often. Now more valuable to measure more often Traditional attrition predictors used by banks have been based on historical data, yielding dated insights from batch processing that represented a snapshot at a single point in time Attrition prediction for voluntary customers: Product level: will the customer drop the product or not renew the contract? Customer level: fully: customer drops all products/services and stops doing business with the bank. Banks address: Who & Why Lily Addresses: Who, why, where and how by using usage and activity based data Customers want a consistent omni channel experience Using behavioral data, and tracking all drivers towards attrition, in real-time, in addition to socio-demographic data will yield the most actionable, real-time insights to proactively address these attrition predictors and enable you to meet customer expectations
4. Prepare data to obtain a complete view on the customer Build a better attrition model Become actionable and learn from feedback
5. BOTH CAN BE MONITORED FOR (IN)ACTIVITY AND TREND, AND TO GENERATE ALERTS Predictors – Traditional scoring Banks have sophisticated models from historic data not based on usage (could be account balance checks, web data etc) Banks have been focused on historical models Combining historical data and real time behavioral metrics Product propensity Behavioral and interaction data is important Previously, models build on top of slow moving profiles Only did churn scoring once a quarter bc values didn’t chance often. Now more valuable to measure more often Traditional attrition predictors used by banks have been based on historical data, yielding dated insights from batch processing that represented a snapshot at a single point in time Attrition prediction for voluntary customers: Product level: will the customer drop the product or not renew the contract? Customer level: fully: customer drops all products/services and stops doing business with the bank. Banks address: Who & Why Lily Addresses: Who, why, where and how by using usage and activity based data Customers want a consistent omni channel experience Using behavioral data, and tracking all drivers towards attrition, in real-time, in addition to socio-demographic data will yield the most actionable, real-time insights to proactively address these attrition predictors and enable you to meet customer expectations
6. BOTH CAN BE MONITORED FOR (IN)ACTIVITY AND TREND, AND TO GENERATE ALERTS Predictors – Traditional scoring Banks have sophisticated models from historic data not based on usage (could be account balance checks, web data etc) Banks have been focused on historical models Combining historical data and real time behavioral metrics Product propensity Behavioral and interaction data is important Previously, models build on top of slow moving profiles Only did churn scoring once a quarter bc values didn’t chance often. Now more valuable to measure more often Traditional attrition predictors used by banks have been based on historical data, yielding dated insights from batch processing that represented a snapshot at a single point in time Attrition prediction for voluntary customers: Product level: will the customer drop the product or not renew the contract? Customer level: fully: customer drops all products/services and stops doing business with the bank. Banks address: Who & Why Lily Addresses: Who, why, where and how by using usage and activity based data Customers want a consistent omni channel experience Using behavioral data, and tracking all drivers towards attrition, in real-time, in addition to socio-demographic data will yield the most actionable, real-time insights to proactively address these attrition predictors and enable you to meet customer expectations
7. BOTH CAN BE MONITORED FOR (IN)ACTIVITY AND TREND, AND TO GENERATE ALERTS Predictors – Traditional scoring Banks have sophisticated models from historic data not based on usage (could be account balance checks, web data etc) Banks have been focused on historical models Combining historical data and real time behavioral metrics Product propensity Behavioral and interaction data is important Previously, models build on top of slow moving profiles Only did churn scoring once a quarter bc values didn’t chance often. Now more valuable to measure more often Traditional attrition predictors used by banks have been based on historical data, yielding dated insights from batch processing that represented a snapshot at a single point in time Attrition prediction for voluntary customers: Product level: will the customer drop the product or not renew the contract? Customer level: fully: customer drops all products/services and stops doing business with the bank. Banks address: Who & Why Lily Addresses: Who, why, where and how by using usage and activity based data Customers want a consistent omni channel experience Using behavioral data, and tracking all drivers towards attrition, in real-time, in addition to socio-demographic data will yield the most actionable, real-time insights to proactively address these attrition predictors and enable you to meet customer expectations
8. VOICEOVER TO BE REDONE Moreover, it is really important to act on churn-related behavior as soon as possible, as the likeliness to win back the customer decreases over time.
9. VOICEOVER TO BE REDONE Moreover, it is really important to act on churn-related behavior as soon as possible, as the likeliness to win back the customer decreases over time.
10. VOICEOVER TO BE REDONE Lily will notify you at the first symptoms of churn, allowing you the appropriate time to take action and retain the customer. Thus allowing you to drive retention programs to maximum effectiveness.
11. First identify high potential churners Banks need to choose a scenario with the most value to the company – high value customers – whose attrition could be easily averted, with proper knowledge and clear implementable actions. Include with the scale image How can you best serve Your -Focus on high value Spend market budget wisely (high value customers) At the right time, channel, context (slide with guy sitting on a bench on banner)