Customer Segmentation for Retail Pricing Optimization
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Solution Overview
Problem
Current pricing and promotion systems in retail settings are reactive to historical data, lack granularity in consumer behavior analysis, and struggle with modeling new products and hypothetical product assortments, limiting their effectiveness in driving specific purchasing behaviors and maximizing profitability.
Innovation Solution
A system and method that segments customers based on transaction history to optimize prices and generate customer-specific promotional activities, using decision trees to model consumer purchasing decisions and statistical demand modeling to create business plans including product assortment, pricing, and promotional strategies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If current pricing systems use historical transaction data modeling, then pricing decisions can be made based on past patterns, but the system remains reactive and cannot effectively drive specific purchasing behaviors
Solution Approach 1:
The system performs preliminary actions by segmenting customers and pre-calculating optimal prices and promotions for different segments before sales occur. This allows the system to proactively drive purchasing behaviors rather than reactively responding to historical patterns, as the pricing strategy is predetermined based on customer segment characteristics and predicted responses.
2Measurement precision
If the system treats consumers as an aggregate entity, then analysis is simpler and faster, but granularity and specificity of business activities are limited
Solution Approach 1:
The system divides the aggregate consumer entity into distinct customer segments based on shared characteristics and purchasing behaviors. This segmentation enables granular analysis of specific consumer groups while managing complexity through systematic classification criteria, allowing targeted business activities for each segment rather than treating all consumers uniformly.
Solution Approach 2:
The system applies different pricing strategies, promotions, and analysis methods to different customer segments based on their specific characteristics and behaviors. Each segment receives localized treatment tailored to its unique properties, improving measurement precision for each group while maintaining overall system manageability through the segmentation framework.
3Adaptability or versatility
If current systems model only products with existing historical transaction data, then pricing accuracy is maintained, but new products and hypothetical assortments cannot be effectively modeled
Solution Approach 1:
The system creates proxy models for new products by copying and adapting patterns from similar existing products within the same customer segments. Instead of requiring historical data for each new product, the system replicates pricing and promotion strategies from comparable products, maintaining pricing accuracy through proven segment responses while enabling modeling of products without direct historical data.
Solution Approach 2:
The system develops universal pricing and promotion models that can be applied across both existing and new products within each customer segment. These multi-functional models capture segment-level purchasing patterns that transcend individual product histories, allowing the same analytical framework to accurately price established products and predict responses to new products or hypothetical assortments.
4Productivity
If the system implements highly targeted promotions for specific customer segments, then profitability increases through reduced costs and increased revenue, but the complexity of generating and managing segmented decisions increases
Solution Approach 1:
The system segments the customer base into distinct groups and generates targeted pricing and promotion decisions for each segment independently. This segmentation enables profitability improvements by matching specific strategies to segment characteristics while managing complexity through systematic classification, avoiding the need to create unique decisions for each individual customer.
Solution Approach 2:
The system varies key parameters such as price discounts, promotion intensity, and product recommendations across different customer segments based on their predicted responses. By changing these parameters systematically according to segment characteristics rather than individual attributes, the system achieves higher profitability through targeted strategies while keeping the decision-generation process manageable through parameterized models.
Data Source
AI summary
The present invention relates to a system and method for generating business decisions. Embodiments of this system and method receive customer transaction data and additional information (cumulatively referred to as ‘modeling data’). This data is utilized to generate a product decision tree which models consumer purchasing decisions as a tree structure. The product decision tree may be utilized by the system to analyze demand for a given leaf (product) in association with other related products. In some embodiments, customers are segmented into groupings of customers who have similar attributes, including similar shopping behaviors. Customer insights are generated for the customer segments. The customer insights and the product decision tree are used to generate business plans, which may then be provided to a store for implementation. These plans may include a product assortment plan, an everyday pricing plan, a promotional plan, and a markdown plan.