Behavior-Based Messaging System for User Segmentation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing messaging protocols struggle to effectively engage users, as they often result in messages being ignored due to their non-targeted nature, leading to low interaction rates.
Innovation Solution
A system that collects historical user data, converts it into feature vectors, calculates and normalizes user propensity scores to segment users, and facilitates targeted message delivery based on these segments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the number of messages sent is increased, then the coverage and potential interaction opportunities are improved, but the annoyance to recipients increases and message open rates decrease
Solution Approach 1:
The patent segments the user base into different propensity groups (high, medium, low) based on historical behavior data. This segmentation allows the system to send targeted numbers of messages to each group - high propensity users receive more messages while low propensity users receive fewer messages, thereby maintaining high interaction rates while minimizing recipient annoyance.
Solution Approach 2:
The system dynamically changes the parameter of message quantity sent to each user based on their calculated propensity score. Instead of sending a fixed number of messages to all users, the system adjusts the message count parameter according to individual user characteristics, achieving optimal interaction rates without causing widespread annoyance.
2Productivity
If traditional non-targeted messaging protocols are used, then the simplicity of implementation is maintained, but the message interaction rates remain low
Solution Approach 1:
The system performs preliminary actions by collecting historical user data and calculating propensity scores before sending messages. This pre-processing of user information allows the system to identify high-value targets in advance, enabling targeted messaging that achieves high interaction rates without requiring complex real-time decision-making during message delivery.
Solution Approach 2:
The patent introduces an intermediary component - the propensity scoring system - that sits between the message sender and recipients. This intermediary processes historical data to generate user scores, which then guide message distribution decisions. This intermediary layer adds functionality for targeted messaging while maintaining relative simplicity through automated scoring algorithms.
Data Source
AI summary
A system including one or more processors and one or more non-transitory computer-readable media storing computing instructions that, when executed on the one or more processors, cause the one or more processors to perform: calculating a first user propensity score to take first actions and a second user propensity score to take second actions based on at least one feature vector of historical data of a user; using the first user propensity score to place the user into a first segment; using the second user propensity score to place the user into a second segment different than the first segment; and facilitating a display of one or more selectable elements of a GUI for the user based on the first segment and the second segment. Other embodiments are disclosed.