Email Offer Integration Across Multiple Aggregators
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Solution Overview
Problem
Existing marketing email campaigns primarily rely on a single third-party aggregator, limiting growth and visibility of data for analytics, which hampers the integration and personalization of third-party offers.
Innovation Solution
A system and method that integrates third-party offers from multiple aggregators into a single message by using an email content optimizer, recommendation engine, and abstraction layer with machine learning capabilities to personalize and rank offers in real-time, capturing customer engagement data for analytics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a single third-party aggregator is used to service marketing email campaigns, then the system is simple to operate and manage, but the growth potential is limited and data visibility for analytics is reduced
Solution Approach 1:
The patent introduces an email content optimizer as an intermediary component that sits between the email distributor and multiple third-party aggregators. This optimizer manages the complexity of integrating multiple aggregators by providing a unified interface, while enabling the system to leverage offers from multiple sources simultaneously, thus resolving the contradiction between integration capability and system complexity
Solution Approach 2:
The system segments the offer integration function by separating the email distribution function from the offer aggregation function. The email content optimizer independently manages multiple third-party aggregators, allowing the core email distribution system to remain simple while the segmentation enables sophisticated multi-aggregator integration
2Ease of operation
If offers are personalized and ranked in real-time using multiple aggregators, then customer engagement is enhanced, but the processing time and computational resources increase
Solution Approach 1:
The email content optimizer performs preliminary actions by pre-fetching and caching offer data from multiple aggregators before the email is opened by the customer. This allows real-time personalization and ranking to be achieved without adding significant processing time at the moment of email opening, as much of the work has been done in advance
Solution Approach 2:
The system replaces traditional mechanical offer selection methods with machine learning-based recommendation engines that automatically rank offers based on customer preferences and behavior patterns. This substitution enables sophisticated real-time personalization without linearly increasing processing time, as the ML models efficiently evaluate and rank offers
Data Source
AI summary
A method may include: an email distributor generating an email using a template comprising static content and placeholders for dynamic content and sending the email to a customer using a customer email client; in response to the email being opened: a customer electronic device executing the scripts that call an email content optimizer for the dynamic content; the email content optimizer calling an email experience service with the calls for the dynamic content; the email experience service requesting recommendation engine for the dynamic offer content; the recommendation engine ranking the dynamic content; the email experience service receiving the ranked dynamic content, retrieving metadata for the dynamic content from an offer catalog, combining the ranked dynamic content and the metadata, receiving the combined ranked dynamic content and the metadata, and populating the placeholders in the email template with the dynamic content and the metadata.