Intelligent Case Management Platform for Customer Retention
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Solution Overview
Problem
Existing customer relationship management tools lack actionable insights for customer retention, leading to inefficient customer service and random assignment of agents, resulting in poor customer experience and increased likelihood of customers leaving the business.
Innovation Solution
An intelligent case management platform that analyzes customer data to predict loyalty scores and sentiment, automating case resolution, agent assignment, and targeted offer generation, ensuring that customers are matched with suitable agents and receive personalized offers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If existing customer relationship management tools are used to manage and track sales and customer relations, then basic customer data can be maintained, but actionable insights for customer retention are not provided
Solution Approach 1:
The system performs preliminary analysis of customer data to predict future loyalty scores and identify at-risk customers before they actually leave. By calculating predicted loyalty scores based on recency, frequency, and monetary value of purchases, the system proactively identifies customers who need intervention, enabling preventive retention strategies rather than reactive responses.
Solution Approach 2:
The system continuously monitors customer behavior patterns and provides feedback through predicted loyalty scores that update in real-time. This feedback mechanism allows the system to detect changes in customer behavior and trigger appropriate retention actions, creating a closed-loop system that adapts to changing customer needs and improves service efficiency.
2Ease of operation
If random assignment of agents is used for customer queries, then agent allocation is simple, but customer experience deteriorates and customers are more likely to leave
Solution Approach 1:
The system segments customers into different risk categories based on their predicted loyalty scores. By dividing the customer base into segments such as high-value at-risk customers, medium-value customers, and low-value customers, the system can apply differentiated agent assignment strategies that match customer needs with appropriate agent expertise, improving retention without requiring complex manual assignment processes.
Solution Approach 2:
The system automatically assigns agents to customer queries based on predicted loyalty scores and customer characteristics, eliminating the need for manual intervention in the assignment process. This self-service approach maintains simplicity while significantly improving customer experience by ensuring that high-value customers receive specialized attention automatically.
3Productivity
If manual customer service processes are used, then flexibility in customer interactions is maintained, but network and computing resources are not conserved through automation
Solution Approach 1:
The system performs automated analysis of customer data, calculation of predicted loyalty scores, and generation of retention actions without requiring manual intervention. By implementing self-service automation, the system significantly improves case management efficiency while reducing the computational and network resources that would otherwise be consumed by manual analysis and decision-making processes.
Solution Approach 2:
The system replaces manual mechanical processes with automated computational algorithms. Instead of manually analyzing customer purchase patterns and determining retention strategies, the system uses computational models that automatically process data, calculate scores, and generate actions, thereby improving efficiency and reducing resource consumption associated with manual operations.
Data Source
AI summary
A device may obtain customer data, associated with a customer identifier, that includes an indication of a recency of a past purchase, a frequency of past purchases, and/or a monetary value associated with past purchases by a customer associated with the customer identifier. The device may determine, based on comparing the customer data and aggregate customer data, a first score that predicts a current measure of loyalty associated with the customer, and may predict, based on the first score, a predicted frequency of future purchases by the customer and a predicted monetary value associated with the future purchases, to determine a second score that predicts a future measure of loyalty associated with the customer. The device may compare the first score and the second score to determine a risk level associated with the customer, and may cause an action to be performed based on determining the risk level.