User Cohort Qualification Using Email-Derived Offer Eligibility
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Solution Overview
Problem
Existing offer codes in e-commerce are not dynamic and user-specific, failing to provide personalized benefits based on real-time user attributes, leading to inefficiencies in targeted marketing and customer engagement.
Innovation Solution
A system that dynamically generates user-specific offer codes by analyzing user profiles and interactions through a backend service, utilizing email data and analytic events to determine eligibility for merchant-specific campaigns, and providing unique, time-limited codes based on user qualifications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional static offer codes are used, then implementation simplicity is maintained, but user personalization and marketing effectiveness deteriorate
Solution Approach 1:
The offer code system transitions from static to dynamic generation. Offer codes are now created in real-time based on user profile attributes, campaign criteria, and interaction data. The system dynamically evaluates user qualifications against cohort definitions and generates personalized codes, making the offering adaptive rather than fixed.
Solution Approach 2:
The system changes multiple parameters simultaneously: offer code uniqueness (from shared to individual), timing (from pre-defined to real-time), and personalization level (from generic to user-specific). These parameter changes enable sophisticated personalization while managing complexity through modular architecture.
2Measurement precision
If real-time user analysis is performed, then offer code relevance is improved, but processing time increases
Solution Approach 1:
User profiles are pre-populated with attributes from multiple sources (email data, analytic events, demographic information) before offer code generation is needed. Campaign criteria and cohort definitions are pre-configured and stored. When a user interacts with the system, the evaluation process quickly matches pre-existing data against pre-defined criteria, significantly reducing real-time processing requirements.
Solution Approach 2:
The system automatically retrieves and evaluates user data without manual intervention. User profiles self-update with new interaction data, and the offer code generation process autonomously queries user attributes, evaluates cohort membership, and generates codes without requiring external input or complex real-time computation.
3Loss of information
If multiple data sources are integrated, then user profile completeness is improved, but data processing complexity increases
Solution Approach 1:
The user profile structure is designed as a universal container that can hold attributes from multiple diverse sources (email metadata, web analytics, demographic data, transaction history). The cohort definition system provides a unified framework for evaluating users against campaigns regardless of data source. This multi-functional architecture handles heterogeneous data through consistent interfaces, reducing integration complexity.
Solution Approach 2:
The system introduces intermediary layers: a standardized user profile format that mediates between diverse data sources and the evaluation engine, and a cohort definition framework that mediates between campaign objectives and user attributes. These intermediaries translate and harmonize data from different sources, making integration manageable despite the diversity of input data.
4Productivity
If offer codes are made time-limited, then user engagement is improved, but code validity management becomes more complex
Solution Approach 1:
Offer codes transition from static, long-validity identifiers to dynamic, time-bound tokens. Each code is generated with an embedded expiration timestamp and validity conditions. The system dynamically tracks code usage, expiration, and redemption status, automatically managing the lifecycle of each offer code without manual intervention.
Data Source
AI summary
In one embodiment, a method includes storing, in a query database of a backend system, and for each of a plurality of users, a set of user-supplied information in response to a set of one or more queries created by a merchant and accessing, for each of the users, one or more email folders from one or more email accounts of the user. The method further includes determining, for each of a plurality of emails, whether the email corresponds to one or more criteria in a cohort; if so, then storing, in the query database and associated with the user, information in that email that corresponds to the one or more criteria; and determining, from the query database, whether each of a plurality of users of the backend system satisfy the criteria of cohorts.