Iterative Advertisement Response Model Training via Merchant Data Feedback
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Solution Overview
Problem
Online service providers face challenges in effectively advertising and optimizing their services for merchants due to a lack of awareness about the merchants' current systems and offerings, leading to suboptimal use of payment processing and shopping experiences.
Innovation Solution
A method involving the analysis of merchant website data to create a dictionary of terms, which helps predict merchant behavior and tailor advertising and payment services, including the use of image processing and analytics to identify relevant terms and their importance on the websites.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If general advertising is used by the payment provider, then brand recognition is improved, but the effectiveness of targeted advertising to merchants is reduced
Solution Approach 1:
The patent segments the advertising approach by dividing merchants into different groups based on their website characteristics, payment processing needs, and behavior patterns. This allows the payment provider to deliver tailored advertising messages to specific merchant segments rather than using a single general advertising approach, thereby maintaining brand recognition while improving targeted advertising effectiveness.
Solution Approach 2:
The patent implements preliminary action by analyzing merchant website data, terms, and behavior patterns before delivering advertising messages. The system pre-processes merchant information to identify characteristics and needs, then uses this pre-analyzed data to customize advertising content in advance, ensuring that merchants receive relevant advertisements that match their specific requirements.
2Loss of energy
If the payment provider lacks knowledge of the merchant's current systems and offerings, then advertising costs are reduced, but the ability to provide optimized payment processing services is worsened
Solution Approach 1:
The patent applies self-service by enabling the system to automatically analyze merchant website data, extract relevant terms and characteristics, and generate customized advertising and service recommendations without requiring manual intervention or extensive prior knowledge from the payment provider. The system autonomously gathers and processes merchant information to provide optimized payment processing services.
Solution Approach 2:
The patent replaces manual analysis and mechanical processes with automated data processing and analytics systems. Instead of relying on human analysts to study merchant websites and provide recommendations, the system uses computational algorithms to automatically extract insights from merchant data, thereby reducing costs while improving the reliability of service optimization.
3Loss of energy
If merchants are not aware of how to change their website or consumer experience, then website modification costs are reduced, but the effectiveness of the website is worsened
Solution Approach 1:
The patent implements feedback by analyzing merchant website data and providing actionable recommendations based on observed patterns and performance metrics. The system continuously monitors website characteristics and consumer behavior, then feeds back customized suggestions to merchants for improving their website effectiveness, enabling cost-effective modifications that enhance productivity.
Solution Approach 2:
The patent applies parameter changes by identifying specific website parameters and characteristics that can be modified to improve effectiveness. The system analyzes merchant websites and recommends changes to various parameters such as website structure, content, payment integration, and user experience elements, allowing merchants to make targeted modifications that maximize impact while minimizing costs.
Data Source
AI summary
There are provided systems and methods for iteratively improving an advertisement response model. A payment provider may perform operations that include training an advertisement response model using a training data set. The operations include determining that a first accuracy value corresponding to the advertisement response model is less than a accuracy value threshold. The operations further include identifying, based on executing the advertisement response model using a target data set that is different from the training data set, one or more units from the target data set for which to run the advertising campaign. The operations also include receiving one or more responses corresponding to a run of the advertising campaign with respect to the identified one or more units from the target data set and updating the training data set based on the one or more responses. The operations further include training an advertisement response model using resulting training data and repeating the operations as long as the accuracy value of the resulting model stays below the threshold or until the increase in the accuracy value with each iteration becomes unprofitable with respect to the costs of acquiring responses from further units from the target dataset.