Dynamic Checkout Page Optimization via Machine Learning
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional payment processors lack sufficient data to analyze customer experience effectively, particularly at the checkout page of a purchase transaction funnel, leading to inefficiencies and increased costs due to frustrated customers, higher return rates, and loss of business.
Innovation Solution
An improved payment processor uses machine-learned models to dynamically optimize the checkout page by extracting and analyzing data signals from the checkout page, predicting customer behavior and sentiment, and adjusting the user interface to enhance transaction success and reduce dissatisfaction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If data is only sourced from the checkout page in conventional payment processor arrangements, then the payment processor can maintain its transactional presence as the last step in the purchase transaction funnel, but the analysis of customer experience is insufficient due to limited data availability
Solution Approach 1:
The patent segments the data collection process by identifying and extracting specific data signals from different stages of the purchase transaction funnel, not just the checkout page. This includes segmenting user interactions, device information, and transaction data into distinct categories that can be analyzed separately to gain comprehensive customer experience insights.
Solution Approach 2:
The patent introduces an intermediary analysis layer that processes data signals from the checkout page and other sources. This intermediary system uses machine learning models to transform raw data into actionable insights about customer experience, bridging the gap between limited data availability and comprehensive analysis needs.
2Reliability
If conventional payment processors rely only on checkout page data, then system implementation is simpler, but customer sentiment detection is insufficient leading to frustrated customers, higher returns, and chargebacks
Solution Approach 1:
The patent applies preliminary action by detecting customer sentiment and predicting negative outcomes (such as chargebacks or returns) before they occur. The machine learning model analyzes data signals during the checkout process to identify customers at risk of dissatisfaction, allowing the system to take preventive measures before the transaction is completed or reversed.
Solution Approach 2:
The patent implements feedback mechanisms where the analysis results from data signals are fed back into the transaction process. This feedback loop allows the system to continuously learn from customer behavior patterns and improve its ability to predict and prevent negative outcomes, thereby increasing both transaction success rates and operational efficiency.
3Ease of operation
If the payment processor implements comprehensive data analysis from checkout page signals, then customer satisfaction can be improved by predicting and preventing negative actions, but the computational resources and processing time increase
Solution Approach 1:
The patent applies partial action by selectively analyzing specific data signals that are most indicative of customer sentiment and negative outcomes. Rather than processing all possible data equally, the machine learning model focuses on key features such as user interaction patterns, device information, and transaction characteristics that provide the highest predictive value, thereby reducing computational overhead while maintaining accuracy.
Data Source
AI summary
In an example embodiment, a method for processing payments made via an electronic payment processing system is provided. An example method includes obtaining training data from a data source. The training data relates to prior purchases made via the electronic payment processing system, wherein the data source includes, in some examples, only a checkout page in a purchase transaction funnel. Features associated with a negative user action in relation to prior purchases are identified. A machine learning algorithm produces a dynamic transactional behavior score indicative of a probability that a purchase will invoke a negative user action.