Unified Customer Data Feature Generation for Predictive Modeling
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Solution Overview
Problem
Current predictive modeling techniques for customer lifetime value (CLV) and churn rates rely exclusively on historical transaction data, limiting the data used for predictions and ignoring other relevant customer information, leading to inaccurate or less than ideal predictions.
Innovation Solution
An improved feature generation process that utilizes rich customer profiles from Customer Data Platforms (CDPs) to generate feature vectors, which are then used to train logistic or linear regression models for predicting CLV and churn rates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional predictive modeling techniques are used that rely exclusively on historical transaction data, then the modeling process is simple and computationally efficient, but the prediction accuracy is reduced due to limited data scope
Solution Approach 1:
The patent combines multiple data sources including transactional data, customer profile data, and behavioral data into a unified feature set for predictive modeling. This merging of diverse data types expands the scope of information used for predictions, thereby improving prediction accuracy without being limited to simple historical transaction data alone
Solution Approach 2:
The patent segments the overall data processing into distinct modules: data collection from multiple sources, data cleaning and transformation, feature engineering, model training, and prediction. This segmentation manages the complexity of processing diverse data types by breaking it into manageable steps, making the complex process systematic and controllable
2Productivity
If AI or machine learning is implemented to generate predictive models based on a smaller subset of data, then processing time and computational resources are reduced, but prediction accuracy deteriorates due to insufficient data utilization
Solution Approach 1:
The patent extracts and utilizes the entire available dataset including all transactional records, customer profiles, and behavioral data, rather than relying on a smaller subset. This comprehensive data extraction ensures that maximum information is available for training the machine learning models, improving prediction accuracy while maintaining efficient processing through automated pipelines
Solution Approach 2:
The patent changes the parameters of data utilization by incorporating multiple data dimensions (transactional, demographic, behavioral) and adjusting feature engineering parameters to optimize both processing efficiency and prediction accuracy. This allows the system to process comprehensive data effectively without being constrained by traditional subset limitations
3Measurement precision
If comprehensive customer data from multiple sources is integrated, then prediction accuracy is improved, but data unification complexity and processing difficulty increase
Solution Approach 1:
The patent introduces data integration layers and standardized schemas as intermediaries between diverse data sources and the predictive modeling system. These intermediaries normalize and harmonize data from different sources (transactional systems, customer profiles, behavioral tracking), making the unification process manageable and improving prediction accuracy through comprehensive data integration
Solution Approach 2:
The patent creates a universal data framework that can handle multiple data types and sources through a common structure. This multi-functional approach allows the system to process transactional data, demographic data, and behavioral data uniformly, reducing the complexity of data unification while maximizing prediction accuracy through comprehensive data utilization
Data Source
AI summary
Disclosed are techniques for generating features to train a predictive model to predict a customer lifetime value or churn rate. In one embodiment, a method is disclosed comprising receiving a record that includes a plurality of fields and selecting a value associated with a selected field in the plurality of fields. The method then queries a lookup table comprising a mapping of values to aggregated statistics using the value and receives an aggregated statistic based on the querying. Next, the method generates a feature vector by annotating the record with the aggregated statistic. The method uses this feature vector as an input to a predictive model.