Transactional Network Analysis for Influencer Identification
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Solution Overview
Problem
Merchants lack the ability to effectively identify early adopters and potential influencers among customers to optimize their marketing strategies and return on investment from deals offered.
Innovation Solution
The Early Adopters and Potential Influencers (EAPI) system uses transactional data and pattern recognition algorithms to construct transactional networks, compute centrality measures, and score customers based on their influence and early adoption potential, enabling merchants to offer targeted deals to the most influential customers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If merchants offer deals to customers without identification of early adopters and influencers, then all customers can be reached, but the return on investment and marketing effectiveness are suboptimal
Solution Approach 1:
The patent segments the customer base into different categories based on their influence and early adoption potential. By analyzing transactional data and constructing social networks, the system identifies specific customer segments (early adopters, influencers, regular customers) and enables targeted deal offerings to each segment, thereby improving marketing effectiveness while optimizing investment return.
Solution Approach 2:
The system changes the parameter of customer selection from random or broad-based to precisely defined segments based on multiple parameters including transaction frequency, network centrality, and influence scores. This parameter-based segmentation allows merchants to target deals at customers most likely to generate high return on investment.
2Quantity of substance
If merchants use broad customer targeting for deals, then coverage is maximized, but resource efficiency and ROI are reduced
Solution Approach 1:
The patent applies local quality by treating different customer groups with different deal strategies. Instead of uniform treatment, the system assigns different influence scores and early adoption ratings to different customers based on their local characteristics (transaction patterns, network position, behavior properties). This enables optimized resource allocation where marketing resources are concentrated on high-value customers rather than uniformly distributed.
3Measurement precision
If merchants implement customer influence analysis, then deal targeting improves, but system complexity increases
Solution Approach 1:
The patent introduces intermediary computational components including pattern recognition algorithms, social network construction modules, and scoring functions that mediate between raw transactional data and customer influence measurements. These intermediaries automatically process and transform data, reducing the complexity burden on merchants while enabling precise influence measurement through systematic computational approaches.
Data Source
AI summary
Various methods are provided for determining transactional networks using transactional data. One example method may comprise receiving a set of transactional data associated each of a plurality of customers, the set of the transactional data comprising a plurality of ordered lists of elements, each ordered list of elements defining a transaction of a plurality of transactions, the ordered list of elements comprising information identifying a particular customer from the plurality of customers, a merchant, and a timestamp, for each particular customer from the plurality of customers, generating a network, each generated network comprising one or more merchant nodes, a plurality of customer nodes, one or more merchant-customer edges between at least one of the one or more merchant nodes and at least one of the plurality of customer nodes, and one or more customer-customer edges between two or more customer nodes.