Virtual Credit Card Issuance via Machine Learning for Purchase Conversion
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Solution Overview
Problem
Users often refrain from purchasing products from merchant systems despite frequent visits, as promotional offers can lower the perceived value of products or brands, and existing methods to incentivize purchases are ineffective.
Innovation Solution
Implementing a system that uses machine-learning models to issue virtual credit cards based on user behavior and purchase criteria, allowing merchants to offer personalized credit amounts that maximize the likelihood of completing a purchase without discounting all users, thereby maintaining brand perception.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If promotional offers such as discounts are provided to entice users to purchase, then user purchase conversion is improved, but the perceived value of products and brand is reduced
Solution Approach 1:
The patent applies local quality by providing personalized credit offers to specific users based on their individual behavior patterns, purchase history, and engagement metrics rather than offering universal discounts. This allows the merchant to incentivize purchases for users who need encouragement while maintaining full price for users who already perceive high value, thus resolving the contradiction between improving conversion and preserving brand perception.
Solution Approach 2:
The system dynamically changes the parameter of credit offer amount based on user characteristics, behavior data, and predicted purchase probability. By adjusting the credit offer parameters individually for each user rather than applying a fixed discount rate, the system improves conversion for targeted users without broadly reducing perceived product value across the entire customer base.
2Ease of manufacture
If no promotional offers are provided to maintain product value perception, then brand perception is improved, but user purchase conversion is reduced
Solution Approach 1:
The system enables self-service by using automated machine learning models and behavioral analysis to identify which users would benefit from credit offers without requiring manual merchant intervention. The system autonomously evaluates user data, predicts purchase probability, and generates personalized credit offers, allowing the merchant to maintain high conversion rates while preserving brand perception through automated, data-driven decision-making.
Solution Approach 2:
The system implements feedback loops by continuously monitoring user responses to credit offers, purchase behavior changes, and engagement metrics. This feedback is used to refine the machine learning models and adjust future credit offer strategies, enabling the system to optimize the balance between maintaining brand perception and improving conversion rates based on real-world outcomes.
3Productivity
If universal discounts are offered to all users, then purchase conversion is improved, but the effectiveness of promotions is reduced due to lack of personalization
Solution Approach 1:
The patent applies segmentation by dividing the user base into distinct groups based on behavioral characteristics, purchase history, engagement levels, and predicted response to credit offers. The machine learning model segments users to identify those most likely to convert with a credit offer versus those who would not be influenced, allowing the merchant to target promotions effectively and avoid wasting resources on users who would purchase regardless.
Solution Approach 2:
By providing locally optimized credit offers tailored to each user segment's specific characteristics and needs, the system maximizes promotion effectiveness. Different user segments receive appropriately calibrated offers based on their individual profiles, ensuring that promotional resources are allocated where they will have the greatest impact on conversion while maintaining personalization and adaptability.
Data Source
AI summary
A merchant system offers various products for sale to users. The merchant system receives a set of application programming interface (API) calls from an online system allowing the merchant system to provide a user with a virtual credit card when the user is purchasing products via the merchant system. The merchant system provides one or more conditions to the online system and provides information describing an order by a user via the merchant system. By applying a machine learned model to characteristics of the users and characteristics of one or more orders by the merchant system, the online system determines an amount for the virtual credit card. When the user's order satisfies the conditions, the online system issues the virtual credit card and transmits information describing the virtual credit card to the merchant system, which the virtual credit card information to the user while the user completes the order.