Messaging Inbox Analysis for Contextual Ad Timing
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Solution Overview
Problem
Conventional advertising systems cannot effectively couple with mail or messaging servers to serve contextually and temporally relevant advertisements, leading to suboptimal click-through-rates and revenue generation.
Innovation Solution
The system analyzes a user's inbox to identify purchase receipt messages, determines purchase patterns, and predicts future purchase events to serve relevant advertisements and coupons at opportune times, improving the relevance and timing of advertisements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional advertising systems serve advertisements without coupling to mail servers, then device complexity is reduced, but advertising relevance and click-through-rates deteriorate
Solution Approach 1:
The patent introduces an intermediary component that couples the mail server and ad server through a standardized interface. This intermediary translates and exchanges purchase pattern data between the two systems, enabling relevant advertising without direct complex coupling. The intermediary acts as a mediator that simplifies the integration while maintaining advertising relevance through purchase pattern analysis.
2Productivity
If advertisements are served without temporal prediction, then system complexity is reduced, but click-through-rates and revenue deteriorate
Solution Approach 1:
The system performs preliminary analysis of purchase patterns from email data before advertisements are served. By extracting and storing purchase frequency and timing information in advance, the system can predict future purchase events and serve advertisements at optimal times. This preliminary action improves click-through-rates without requiring complex real-time prediction during ad serving.
3Measurement precision
If purchase pattern analysis is implemented, then advertising timing precision is improved, but information processing time increases
Solution Approach 1:
The system performs purchase pattern analysis in advance by examining historical email data and storing extracted purchase information in a database. This preliminary processing allows the system to quickly retrieve and use pre-analyzed purchase patterns for timing predictions, rather than analyzing raw email data in real-time when serving advertisements.
Solution Approach 2:
The system creates simplified copies of purchase pattern data from complex email content. Instead of re-analyzing original emails, the system uses extracted and stored purchase pattern representations (frequency, timing, item categories) that capture essential information in a compact form, reducing processing time while maintaining prediction accuracy.
Data Source
AI summary
Disclosed are computer systems and methods for contextually targeted advertising using a regular periodicity of information derived from a user's messaging behavior. The disclosed systems and methods enable the prediction of future purchases based on a periodicity analysis of a user's purchase history, whereby advertisements can be targeted to the user based on the user's determined purchase habits. The disclosed systems and methods analyze a user's inbox by mining for purchase receipt messages, and determine a frequency of purchases associated with such receipts. Based on the determined frequency, the disclosed systems and methods can predict when subsequent like or similar purchases are to occur, whereby relevant advertisements or coupons may be served to the user in advance of the projected purchases.