Machine Learning Customer Decision Tree Generation
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Solution Overview
Problem
Conventional methods for generating Customer Decision Trees (CDTs) in retail scenarios lack accuracy and scalability, as they do not effectively capture attribute-level demand transfer and do not consider all sales drivers such as demographics, competition, and promotions, leading to limited predictive capabilities and reliance on historical data.
Innovation Solution
A method and system using machine learning to generate CDTs by aggregating data at multiple levels, creating a multivariate multi-dependent data matrix, and optimizing a prediction model to estimate attribute value sales, incorporating demographic data, inventory availability, and inventory stock, allowing for the identification of optimal aggregation levels and fine-tuning the model for improved accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional statistical analysis methods are used to generate CDTs, then the process is simpler and more straightforward, but the accuracy and predictive capability are limited
Solution Approach 1:
The patent replaces conventional statistical analysis methods with machine learning models to generate CDTs. The machine learning approach uses algorithms that can automatically learn patterns from historical data, capturing complex customer decision-making behaviors that traditional statistical methods miss. This substitution significantly improves prediction accuracy while the automated nature of ML reduces manual intervention complexity.
Solution Approach 2:
The patent transforms the analytical approach by changing from static statistical correlations to dynamic machine learning parameter optimization. The system uses training data to optimize model parameters, enabling the CDT to adapt to changing customer preferences and market conditions. This parameter optimization capability allows the system to achieve high accuracy without requiring manual adjustment of complex statistical parameters.
2Adaptability or versatility
If comprehensive data including demographics, competition, and promotions is incorporated, then the predictive capability improves, but the data processing complexity increases
Solution Approach 1:
The patent creates a universal machine learning framework that can handle multiple types of data (demographics, competition, promotions, inventory) through a single integrated model. The system uses feature engineering to transform diverse data sources into standardized inputs, and the ML model automatically learns the relationships between different data types. This multi-functional approach enables comprehensive predictive capability while the automated feature processing reduces manual data preparation complexity.
Solution Approach 2:
The patent introduces feature engineering as an intermediary layer between raw data and the machine learning model. This intermediary transforms complex multi-source data into standardized features that the model can process efficiently. The feature engineering step handles data cleaning, normalization, and transformation, acting as a mediator that simplifies the input requirements for the ML algorithm while preserving all relevant information from diverse data sources.
3Measurement precision
If machine learning models are used to capture attribute-level demand transfer, then the accuracy of demand prediction improves, but the computational requirements increase
Solution Approach 1:
The patent segments the customer decision-making process into distinct attributes and attribute values, creating a hierarchical CDT structure. The machine learning model predicts demand at each node of the decision tree rather than attempting to model all possible customer paths simultaneously. This segmentation reduces the computational complexity by breaking down the prediction task into smaller, manageable sub-tasks while maintaining high accuracy through the cumulative effect of predictions across multiple attribute levels.
Solution Approach 2:
The patent performs preliminary data processing and feature engineering before feeding data into the machine learning model. Historical data is pre-processed to extract relevant features and patterns, and the training data is prepared in advance to optimize model training efficiency. This preliminary action reduces the computational burden during actual prediction by ensuring data is in the optimal format, thereby improving demand prediction accuracy without proportionally increasing real-time computational requirements.
Data Source
AI summary
A method and system for generating Customer Decision Tree (CDT) for an entity in accordance with an attribute value (AV) based demand transfer estimation for a product category using machine learning, is disclosed. The method includes aggregating very high volume of data associated with a plurality of AVs of a product category at a plurality of aggregation levels. Further, generating a data matrix, which represents data is a structured format for machine learning, at a predefined aggregation level for the product category and generating a prediction model with the data matrix to determine predicted AV sales for each AV at the predefined aggregation level. Further, optimizing the trained prediction model. Thereafter, generate the CDT utilizing the optimized prediction model, a Demand Transfer (DT) estimator, a scenario generator and a hierarchy generator. Machine learning based DT is more accurate, effectively generating more accurate CDT tree.