Predictive Sales Intelligence System for B2B Opportunity Identification
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Business-to-business companies face challenges in transforming voluminous business transaction data into actionable insights for customer retention and wallet share expansion due to the lack of statistical and predictive rigor in traditional analytical methods, making it difficult to identify opportunities for cross-selling and customer retention.
Innovation Solution
A computer-implemented predictive sales intelligence system using affinity propagation clustering algorithms to group customers with similar purchase behavior, creating purchase pattern profiles that enable the identification of reliable sales opportunities, including cross-sell and lost sales opportunities, without requiring prior knowledge of the number of clusters and handling non-symmetric similarity matrices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional analytical and reporting methods are used to process business transaction data, then the system is simple and easy to operate, but the statistical and predictive rigor is insufficient to produce high quality customer retention and wallet share expansion insights
Solution Approach 1:
The patent introduces an affinity propagation clustering algorithm as an intermediary between raw business transaction data and traditional analytical methods. This algorithm automatically identifies customer clusters with similar purchase behaviors, transforming unstructured data into structured patterns that traditional analysis can effectively process, thereby enhancing statistical rigor without requiring complex manual configuration
Solution Approach 2:
The system employs self-service mechanisms where the affinity propagation algorithm automatically determines the optimal number of customer clusters without requiring prior specification. The algorithm autonomously processes voluminous transaction data, identifies purchase patterns, and generates actionable insights, reducing the need for complex human intervention while maintaining high analytical rigor
2Reliability
If advanced statistical techniques are used to identify purchase patterns and sales opportunities, then the reliability of insights is improved, but the processing time and computational resources increase
Solution Approach 1:
The patent applies preliminary action by pre-processing business transaction data into standardized formats and pre-computing customer purchase histories before applying affinity propagation clustering. This preparation work is done in advance, allowing the main analytical algorithm to process data more efficiently and reduce overall computation time while maintaining reliable insights
Solution Approach 2:
The system segments the large volume of business transaction data into manageable customer-level datasets, each representing individual customer purchase patterns. This segmentation allows the affinity propagation algorithm to process data in smaller, more efficient units while maintaining the ability to identify reliable cross-sell and wallet share expansion opportunities across the entire customer base
3Quantity of substance
If the system analyzes voluminous business transaction data to identify wallet share expansion opportunities, then the quantity of actionable insights is improved, but the complexity of data processing increases
Solution Approach 1:
The patent extracts only the essential and relevant features from voluminous business transaction data, such as customer purchase histories, product categories, and spending patterns. By extracting only these critical elements and discarding redundant information, the system generates a substantial quantity of actionable insights while keeping the processing system relatively simple and efficient
4Adaptability or versatility
If the system uses clustering algorithms to group customers by purchase behavior, then the ability to identify cross-sell opportunities is improved, but the requirement for computational algorithms and processing increases
Solution Approach 1:
The affinity propagation clustering algorithm operates autonomously to group customers by purchase behavior without requiring manual configuration or intervention. It automatically determines the optimal number of clusters and assigns customers to appropriate groups based on their purchase patterns, thereby enhancing the system's ability to identify cross-sell opportunities while managing automation complexity through a self-configuring approach
Data Source
AI summary
The present invention relates to a system and method for efficiently identifying the sales opportunities in a business-to-business market environment. It provides a computer-implemented predictive sales intelligence system and method for identifying sales opportunities. The present invention is a computer implemented system and method for efficiently identifying reliable purchase pattern profiles through scientific analysis of customer data. It includes a system and method for calculating a customer's purchase profile, clustering customers based on similarity of their purchase profile, and efficiently providing a reliable set of opportunities including lost sales (retention) and cross-selling (wallet share expansion) opportunities. It uses this reliable estimate of sales opportunities to retain and expand wallet share for customers.