CRM System Using Attrition Risk Segmentation for Targeted Retention
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Solution Overview
Problem
Customer relationship management (CRM) systems face challenges in effectively retaining customers, as they lack a systematic approach to identify and address customer attrition risks, leading to inefficient use of retention and cross-sell strategies.
Innovation Solution
A CRM system that computes a customer's attrition risk based on historical data and real-time interactions, using a decision server to determine personalized offers and incentives or promotions, and communicates these to agents to enhance customer retention or cross-selling opportunities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If CRM systems track and analyze customer information to improve retention, then customer retention capability is improved, but system complexity increases
Solution Approach 1:
The patent segments customers into different attrition risk groups (high risk, medium risk, low risk) based on analyzed characteristics, allowing the CRM system to apply different retention strategies to different segments. This segmentation approach improves retention effectiveness while managing system complexity by focusing resources on high-priority customer groups.
Solution Approach 2:
The patent introduces an intermediary analysis layer that processes customer information and generates attrition risk assessments, which then guide retention actions. This intermediary layer acts as a mediator between raw customer data and retention decisions, improving the system's ability to retain customers while organizing complexity through a structured analysis framework.
2Reliability
If personalized offers are provided to high attrition risk customers, then retention effectiveness is improved, but resource consumption increases
Solution Approach 1:
The patent applies local quality by providing personalized retention offers specifically to customers identified as high attrition risk, rather than uniformly to all customers. This targeted approach improves retention effectiveness for vulnerable customers while conserving resources by not deploying the same level of personalized intervention to low-risk customers.
Solution Approach 2:
The patent implements partial action by focusing retention resources on the portion of customers most at risk of attrition. Rather than applying retention efforts universally, the system selectively applies personalized offers to high-risk segments, improving overall retention effectiveness while optimizing resource consumption.
3Measurement precision
If attrition risk analysis is performed for all customers, then retention accuracy is improved, but processing time increases
Solution Approach 1:
The patent segments the customer base and performs detailed attrition risk analysis primarily on high-priority segments rather than uniformly across all customers. This segmentation allows the system to maintain high retention accuracy for critical customers while reducing overall processing time by applying simplified assessments to lower-priority segments.
Solution Approach 2:
The patent applies full attrition risk analysis selectively to customers where it matters most (high-value or high-risk segments) rather than performing exhaustive analysis on every customer. This partial action approach maintains measurement precision where needed while minimizing unnecessary processing time consumption.
Data Source
AI summary
Customer relationship management (CRM) system for enhanced retention of customers of a business entity. In one embodiment, a historical data corresponding to a customer is maintained in the CRM system, with the historical data including information on prior interactions between the customer and the business entity. On receiving a call from the customer, the historical data is identified and an attrition risk (indicating the probability of retaining the customer) is computed based on the identified data. Different offers based on the computed attrition risk are then determined and communicated to the customer during the call.