Machine Learning Pricing Prediction From Unsegmented Transaction Data
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Solution Overview
Problem
Conventional pricing systems face issues with data sparsity and inaccurate predictions due to data segmentation, which limits the capture of broader market trends and customer behaviors, and are inefficient in processing large datasets, leading to suboptimal pricing recommendations.
Innovation Solution
A machine learning model, such as an artificial neural network, is trained on unsegmented historical transaction data to generate pricing recommendations, utilizing distributed computing for real-time processing and capturing broader market influences, including seasonality and long-term trends, without the limitations of hardware constraints.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If historical transaction data is segmented into smaller sets (by customer segments or product segments), then analysis of each segment becomes more manageable and focused, but data sparsity occurs leading to inaccurate predictions and poor pricing recommendations
Solution Approach 1:
The patent merges multiple data segments (different customer segments, product segments, geographic regions) into a unified training dataset. Instead of analyzing segmented data separately, the system combines all historical transaction data to train a single pricing prediction model, thereby eliminating data sparsity while maintaining the ability to capture segment-specific patterns through feature engineering.
2Loss of information
If conventional pricing systems process large datasets, then comprehensive market trends can be captured, but processing efficiency decreases and hardware constraints limit real-time capabilities
Solution Approach 1:
The patent replaces conventional mechanical data processing systems with a machine learning-based predictive model. Instead of using traditional statistical methods that require extensive computational resources for large datasets, the system trains a neural network model once on historical data and then uses the trained model for rapid real-time predictions, substituting heavy mechanical processing with intelligent algorithmic inference.
3Measurement precision
If data is segmented to focus on specific customer or product groups, then targeted analysis is improved, but broader market influences such as seasonality and long-term trends are missed
Solution Approach 1:
The patent applies local quality by incorporating segment-specific features (customer characteristics, product attributes, geographic information) into the unified model training process. The model learns both global market patterns and local segment characteristics simultaneously, allowing it to capture broad market trends while maintaining sensitivity to specific customer and product group behaviors through feature engineering rather than data segmentation.
Data Source
AI summary
Techniques for training a price prediction model are disclosed. An example method includes receiving historical transaction data comprising a plurality of transactions, each transaction comprising a plurality of attributes and a transaction price. The method also includes processing the historical transaction data to generate training data comprising features extracted from the plurality of attributes and price indices generated from the transaction price. The method also includes training, by a processing device, a price prediction model using the training data, wherein training the price prediction model comprises training a neural network to generate a mapping between the features and the price indices, and wherein the features used to train the neural network are not segmented and correspond with a plurality of products, product types, geographies, and customer sizes.