Customer Network Value Index Calculation via Social Graph Analysis
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current marketing campaigns lack an efficient and systematic method to measure and utilize Customer Network Value (CNV) from direct interactions, which is crucial for targeting and improving marketing efficiency, especially in large customer populations where complete information is not available.
Innovation Solution
A method and system that involves storing static and historical network behavior data, building a customer network map, computing social network parameters, and calculating a Customer Network Value Index (NVI) by combining relevant social network parameters and static information, allowing for the characterization of markets and adaptation of strategies over time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If direct observation of word-of-mouth interactions is implemented to compute Customer Network Value, then measurement precision of customer influence is improved, but device complexity and data collection requirements increase
Solution Approach 1:
The patent introduces an intermediary system comprising detection modules, data collection modules, and computation modules that mediate between raw interaction data and the final Customer Network Value metric. This intermediary layer systematically captures word-of-mouth interactions through multiple modules working in sequence, transforming complex observational data into structured network value computations without requiring direct complex analysis of all customer interactions.
Solution Approach 2:
The system segments the complex task of measuring Customer Network Value into distinct functional modules: detection modules for identifying interactions, data collection modules for gathering interaction data, and computation modules for calculating network value. This segmentation allows each module to handle specific aspects of the measurement process independently, reducing overall system complexity while maintaining measurement precision.
2Measurement precision
If complete social network information is collected for all customers, then measurement precision of network value is improved, but loss of time and resources for data collection increases
Solution Approach 1:
The patent applies partial action by collecting and processing only the necessary subset of network interaction data required to compute Customer Network Value, rather than attempting to gather complete social network information for all customers. The system focuses on detecting relevant word-of-mouth interactions and gathering sufficient data to produce meaningful network value metrics, accepting that complete information is neither necessary nor practical.
Solution Approach 2:
The system performs preliminary data collection and processing steps by implementing detection modules that continuously monitor and capture interactions, and data collection modules that accumulate interaction data over time before computation. This preliminary action allows the computation modules to work with pre-processed, organized data, reducing the time required for actual network value calculation while maintaining measurement precision.
3Productivity
If systematic collection of word-of-mouth behavior data is implemented, then productivity of marketing campaigns is improved, but device complexity increases
Solution Approach 1:
The patent creates a universal evaluation system that can be applied across multiple marketing campaigns and different types of word-of-mouth interactions. The detection modules, data collection modules, and computation modules form a multi-functional system capable of handling various interaction types (referrals, recommendations, social media mentions) and serving different marketing objectives, thereby improving productivity without proportionally increasing complexity for each specific application.
Solution Approach 2:
The system implements feedback mechanisms where the computed Customer Network Value indices are fed back into marketing campaign decision-making processes. The evaluation system continuously monitors campaign performance, updates network value measurements based on observed interactions, and provides feedback that informs targeting strategies and resource allocation, thereby systematically improving marketing productivity through data-driven adjustments.
Data Source
AI summary
A method, system and computer program product are disclosed for evaluating a customer network value of a set of customers for a given marketing campaign, said method comprising the steps of storing for each customer static information, collecting for each customer, historical and time cumulative network behavior data, building a customer network map from network behavior data said map describing network relation between nodes, one node representing one customer, computing the social network parameters for the nodes of the network map, selecting the most relevant social network parameters according to the objectives of the given marketing campaign, and, computing for each customer a Customer Network Value Index (NVI) by combining the most relevant social network parameters and static information. When a static information profile is defined for an extended customer population, applying data mining techniques on this population allows estimating a probabilistic NVI for each customer in the extended customer population for which no NVI has been computed.