Real-Time Machine Learning Appeasement Offers for Customer Retention
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Solution Overview
Problem
Current methods for customer retention in retail are time-consuming and costly, often requiring individual review of each customer's situation and may not provide personalized appeasements effectively, leading to inefficiencies and increased budgets.
Innovation Solution
A system utilizing machine learning models to analyze historical customer data and order data in real-time, generating personalized appeasements based on customer preferences and experiences, and transmitting these offers through various communication channels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If rule-based approaches are used for appeasement offers, then personalization can be achieved, but time consumption and operational costs increase significantly
Solution Approach 1:
The system enables self-service by using machine learning models to automatically generate personalized appeasement offers without requiring manual review. The ML models analyze customer data and order information to autonomously determine appropriate appeasement strategies, eliminating the time-consuming manual assessment process while maintaining high personalization standards.
Solution Approach 2:
The patent replaces the mechanical rule-based system with an intelligent machine learning-based system. Instead of relying on predefined rules that require manual interpretation and adjustment, the system uses trained ML models that automatically process customer data and generate personalized offers, significantly reducing operational time and costs.
2Measurement precision
If individual review of each customer situation is conducted, then accurate personalized appeasements can be provided, but operational costs and complexity increase
Solution Approach 1:
The system creates a virtual copy of the customer assessment process through machine learning models. These models are trained on historical customer data and appeasement outcomes, creating a digital twin of expert decision-making that can evaluate customer situations accurately without requiring actual human reviewers, thereby reducing operational complexity and costs.
Solution Approach 2:
The patent transforms the assessment process by changing from manual parameter evaluation to automated ML-based parameter analysis. The system processes multiple customer parameters simultaneously through trained models, achieving accurate assessment without the linear scaling of complexity that would occur with individual human review of each parameter.
3Speed
If real-time processing of order data is implemented, then customer satisfaction can be improved, but computational resources and processing power increase
Solution Approach 1:
The system performs preliminary action by pre-training machine learning models on extensive historical customer data and order information before real-time operation. This preprocessing creates ready-to-use predictive models that can quickly generate appeasement offers in real-time without requiring intensive computational resources during actual customer interactions, thus achieving fast response with optimized resource usage.
Data Source
AI summary
System and methods for controlling customer retention are disclosed. In some embodiments, a disclosed method includes: storing historical customer data and order data associated with a customer of a retailer within a database, receiving the order data within a time period, the order data including a plurality of orders, parsing the order data to determine an order status for each of the plurality of orders, identifying a negative order from the order data based on the order status, the negative order associated with the customer, generating, in real-time, appeasement data for the negative order, the appeasement data being associated based on the historical customer data, and transmitting, in real-time, the appeasement data to an electronic device having a user interface for display of an appeasement offer associated with the appeasement data to the customer.