CRM Decision-Maker Identification via Participation Data Analysis
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Solution Overview
Problem
Enterprises face challenges in identifying the decision-maker within a client company for sales opportunities due to varying titles and unclear communication patterns, leading to inefficiencies in CRM systems.
Innovation Solution
A computer-implemented method that retrieves and compares participation data from CRM systems to identify potential decision-makers by analyzing communication activities, position titles, and IDs, using historic data and neural networks to rank contacts and predict likelihoods.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If sales personnel manually analyze communication patterns and participant data to identify decision-makers, then identification accuracy can be improved, but time consumption and operational complexity increase significantly
Solution Approach 1:
The system enables automated self-service identification of decision-makers by having the CRM system automatically retrieve participation data, compare it with historic patterns, and compute potential decision-makers without requiring sales personnel to manually analyze communication logs. The system serves itself by autonomously performing data retrieval, comparison, and analysis tasks.
Solution Approach 2:
The patent replaces the mechanical manual analysis process with an automated computational system. Instead of sales personnel manually examining communication patterns and participant data, the system uses automated data retrieval, comparison algorithms, and computational models to identify decision-makers, substituting human mechanical analysis with automated electronic processing.
2Reliability
If sales personnel manually review communication activities and participant information to determine decision-makers, then identification reliability can be improved, but productivity and output per unit time decrease
Solution Approach 1:
The system enables continuous automated processing of sales opportunities by automatically retrieving participation data, comparing it with historic patterns, and computing potential decision-makers without interruption. Multiple sales opportunities can be processed in parallel continuously, maintaining steady throughput while ensuring reliable identification through consistent application of the analysis methodology.
Solution Approach 2:
The CRM system performs self-service by automatically executing the entire decision-maker identification process including data retrieval, comparison with historic patterns, and computation of results. This eliminates the need for sales personnel to manually intervene in each identification case, thereby maintaining reliability through standardized automated processes while maximizing productivity.
3Measurement precision
If the system stores and processes detailed participation data for all contacts across multiple opportunities, then identification accuracy improves, but data storage requirements and computational resource consumption increase
Solution Approach 1:
The system extracts only the essential and relevant features from participation data for storage and comparison. Instead of storing and processing all raw communication data in detail, the system extracts key participation patterns, contact roles, and interaction metrics that are most predictive of decision-maker behavior, thereby reducing storage requirements while maintaining identification accuracy.
Solution Approach 2:
Instead of storing all detailed participation data and filtering it during analysis, the system inverts the approach by pre-processing and storing only the extracted essential features and patterns. This inversion reduces the quantity of stored data while preserving the information necessary for accurate decision-maker identification through comparison with historic patterns.
4Ease of operation
If sales personnel attempt to identify decision-makers without automated assistance, then manual control and judgment can be maintained, but the complexity of the process and difficulty of operation increase
Solution Approach 1:
The system performs self-service by automatically executing the complex data retrieval, comparison, and analysis tasks without requiring sales personnel to manually navigate complex processes. The CRM system autonomously handles the identification workflow, presenting simplified results to users and thereby easing operation while managing the inherent system complexity internally through automated processes.
Data Source
AI summary
A computer implemented method and system, comprising: retrieving, from an electronic data storage, participation data for each of a plurality of contacts that are associated with an opportunity, the participation data including information indicating types of communication activities the contacts have participated in in respect of the opportunity and times of the participation; comparing the participation data for the plurality of contacts with historic participation data for each of a plurality of decision maker contacts for a plurality of historic opportunities, the historic participation data including information indicating types of communication activities the decision maker contacts have participated in in respect of the historic opportunities and times of the participation; and computing, based on the comparing, which of the plurality of contacts associated with the opportunity is a potential decision maker and storing information indicating the identity of the potential decision maker.