Targeted Online Ad Incentive System for Brick-and-Mortar Merchants
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Solution Overview
Problem
Local merchants face challenges in encouraging customers who primarily conduct in-person, brick-and-mortar transactions to engage with their online presence, as there is a lack of effective systems and methods to incentivize these customers to view online advertisements or make online transactions.
Innovation Solution
A marketing system that collects transaction data, analyzes customer behavior, and sends personalized incentives to customers who have not previously made online transactions with the merchant, using a data mining tool to identify likely customers and provide unique links to online advertisements or promotions, thereby encouraging them to explore the merchant's online offerings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If merchants send generic online advertisement promotions to all customers, then online engagement may increase slightly, but customer response rate remains low and marketing resources are wasted
Solution Approach 1:
The system performs preliminary analysis of customer transaction data before sending promotions. The data mining tool pre-identifies customers likely to respond to online ads by analyzing their transaction history, enabling targeted outreach before resources are committed to broad marketing campaigns.
Solution Approach 2:
The system uses feedback from transaction data to continuously improve targeting accuracy. By monitoring which customers respond to online advertisements and their subsequent transaction behavior, the data mining model is refined to better predict responsiveness, reducing wasted marketing spend over time.
2Measurement precision
If merchants implement a comprehensive data analysis system to identify target customers, then marketing precision improves, but system complexity and implementation cost increase
Solution Approach 1:
The marketing system performs multiple functions through a unified platform: collecting transaction data, analyzing customer behavior patterns, identifying target customers, and distributing personalized promotions. This multi-functional approach avoids the complexity of separate specialized systems while achieving high targeting precision.
Solution Approach 2:
The data mining tool automatically analyzes transaction data and identifies responsive customers without requiring manual intervention. The system self-configures targeting criteria based on historical data patterns, reducing the operational complexity of implementing precise customer segmentation.
Data Source
AI summary
After a first transaction but before any subsequent transaction, a merchant communicates an incentive containing a URL to a customer to make a donation to a charity in exchange for a future transaction. The customer uses the URL to accesses and use the incentive. Data may be collected about all customers, either expressly, or from offline or online transactions between the customers and the merchants, and the data may be stored in a data storage area. All data in the data storage area may be utilized by logic tool, which may provide information, such as details of consumer behavior and analytic reporting. Matches between transactions between merchants and customers, and corresponding online activities of the customers that pertain to the merchants may be identified and used to determine the accuracy of a level of certainty of each such match to assess the efficacy of the incentive.