Hybrid Forecasting Model for Client Retention Prediction
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Solution Overview
Problem
Businesses face challenges in predicting client loyalty and retention, as acquiring new clients is more difficult and expensive than maintaining existing ones, with unhappy clients potentially leading to high market rejection rates and competitor approaches.
Innovation Solution
A hybrid forecasting model combining convolutional neural networks for capturing temporal patterns and decision trees for providing explainable insights, trained on 12-month histories of leading markers to predict lagging markers, enabling proactive client retention strategies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional analysis methods are used to understand client behavior, then the approach is simple and easy to implement, but the ability to accurately predict client loyalty and retention is insufficient
Solution Approach 1:
The patent segments the prediction task into multiple components: feature extraction from unstructured data, pattern recognition through neural networks, and predictive modeling. This segmentation allows each component to be optimized independently, achieving high prediction accuracy while managing complexity through modular architecture.
Solution Approach 2:
The patent employs a composite modeling approach by integrating multiple AI techniques (neural networks, natural language processing, pattern recognition) into a unified prediction system. This composite structure combines the strengths of different methods to achieve superior prediction accuracy compared to any single approach.
2Measurement precision
If comprehensive data analysis is performed to predict client retention, then prediction accuracy improves, but the time and computational resources required increase
Solution Approach 1:
The patent performs preliminary data processing and feature extraction in advance, preparing client data for analysis before prediction is needed. This preliminary action reduces the computational burden during actual prediction, enabling fast and accurate retention forecasting when required.
Solution Approach 2:
The patent replaces traditional mechanical data analysis methods with AI-based neural networks and automated pattern recognition systems. This substitution dramatically reduces analysis time while improving accuracy, as these systems can process large datasets instantly without manual intervention.
3Adaptability or versatility
If detailed client data is analyzed to understand individual perspectives, then client satisfaction improvement is possible, but data processing complexity increases
Solution Approach 1:
The patent implements self-service through automated natural language processing and pattern recognition systems that automatically analyze client data, extract insights, and generate personalized recommendations without requiring manual data processing. This automation handles complexity internally while providing simple, customized outputs to users.
Solution Approach 2:
The patent transforms unstructured client data into structured parameters and features that can be efficiently processed by predictive models. By changing the representation of client data from raw text to standardized parameters, the system achieves high adaptability to individual clients while reducing processing complexity.
Data Source
AI summary
An embodiment for training a forecasting model is provided. The embodiment may include receiving a first 12-month history of a plurality of leading markers. The embodiment may also include submitting the first plurality of leading markers to a convolutional neural network model. The embodiment may further include submitting a first output of the second convolution layer to the aggregation layer. The embodiment may also include generating one or more feature summaries and one or more first lagging markers. The embodiment may further include training the convolutional neural network model. The embodiment may also include removing the regression layer. The embodiment may further include creating a decision tree model and training the decision tree model to generate one or more updated first lagging markers.