Customer Preference Model for Merchant Matching

Resolve Bottlenecks,
Find Innovative SolutionsGenerate Solutions

Solution Overview

Problem

Customers face difficulties in finding suitable merchants for their needs, especially when traveling or in unfamiliar areas, due to the vast number of options and lack of reliable online reviews, which can lead to inappropriate choices and time-consuming searches.

Innovation Solution

A computer system constructs customer preference models using transactional data to match customers with merchants that align with their preferences, including historical spending patterns, price sensitivity, and brand loyalty, enabling targeted marketing and recommendations.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If customers search online using search engines to find merchants, then they can access a wide range of options, but the task becomes tedious and time-consuming

Engineering Contradiction:

Improverange of merchant optionsVSAvoidtime to select merchants

Core Design Contradiction:

Adaptability or versatilityVSLoss of time

Solution Approach 1:

The system performs preliminary actions by constructing customer preference models in advance using transactional data from payment cards. These models capture spending patterns, price sensitivity, and brand loyalty before the customer needs to search, enabling the system to pre-filter and rank merchants according to individual customer preferences, thus eliminating the need for time-consuming manual searches

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The patent introduces an intermediary computer system that acts as a mediator between customers and merchants. This intermediary automatically processes the matching by comparing customer preference models with merchant profiles, filtering out inappropriate merchants, and presenting only suitable options to customers, thereby resolving the contradiction between providing wide options and reducing search time

Inventive Principle:

Principle #24Intermediary (Mediator)

2Reliability

If customers rely on online reviews to select merchants, then they can get insights from previous purchasers, but reviews may not be reliable or may reflect views of individuals very different from the searching customer

Engineering Contradiction:

Improvereliability of merchant informationVSAvoidapplicability to individual customer preferences

Core Design Contradiction:

ReliabilityVSAdaptability or versatility

Solution Approach 1:

The system applies local quality by creating personalized preference models for each customer based on their unique transactional data. Instead of relying on generic online reviews that may not apply to the specific customer, the system tailors the merchant matching to each customer's local characteristics such as their spending patterns, price sensitivity, and brand loyalty, ensuring the recommendations are reliably applicable to that individual

Inventive Principle:

Principle #3Local quality

Solution Approach 2:

The system uses feedback from historical transactional data to continuously refine and update customer preference models. By analyzing past purchasing behavior, the system learns what merchants and products each customer prefers, creating a reliable feedback loop that improves the accuracy of merchant recommendations over time, making them more reliable than static online reviews

Inventive Principle:

Principle #23Feedback

3Adaptability or versatility

If merchants do not publish tariffs online or have complex special offers systems, then they can maintain flexible pricing, but customers cannot easily determine if merchants are appropriate for their preferences

Engineering Contradiction:

Improvepricing flexibilityVSAvoidprice information availability

Core Design Contradiction:

Adaptability or versatilityVSLoss of information

Solution Approach 1:

The system performs preliminary action by obtaining and storing price information and special offer details from merchants in advance. This data is integrated into merchant profiles before customers need to search, allowing the system to pre-evaluate whether merchants match customer price preferences even when tariffs are not publicly published or when complex special offers are involved

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The intermediary computer system resolves the information asymmetry by acting as a mediator that obtains price and offer information from merchants through automated queries. The system translates complex pricing structures into comparable formats and uses this information to filter and rank merchants according to each customer's price sensitivity and preferences, making flexible pricing transparent to customers

Inventive Principle:

Principle #24Intermediary (Mediator)

Data Source

PatentUS10387855B2Computerized methods and computer systems for matching customers with merchants

Publication Date: 2019.08.20 MASTERCARD INT INC

AI summary

For each of multiple customers, a computer system constructs a customer preference model using transactional level data relating to purchases the customers have made using respective payment cards. The computer system has access to a database with merchant data, including profiles of merchants. Upon the customer contacting the computer system in respect of a specific product, the computer system (or an application running on a communication device of the customer which is able to access the customer's preference model and the database of merchant data) uses the customer preference model to match the customer to merchants who offer the product and who have profile matching the customer's preference model. The customer may provide details of his/her payment card identify the customer to the computer system.