ML-Based Targeted Message Placement in Software Applications
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Solution Overview
Problem
Existing software applications face inefficiencies in delivering targeted messages to users, as current techniques often result in irrelevant messages being presented, leading to wasted resources such as network bandwidth and power usage.
Innovation Solution
The implementation of machine learning models that analyze effectiveness, distance, and match scores to identify and select appropriate host parties for targeted messages, ensuring relevance and reducing resource wastage by optimizing message placement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If targeted messages are randomly selected by a message placement engine, then messages can be delivered to users, but the messages may not be relevant to the user and resources are wasted
Solution Approach 1:
The system performs preliminary actions by training machine learning models offline using historical data to predict message effectiveness, user qualification, and relevance. These pre-trained models are then deployed to make real-time message placement decisions, eliminating the need for random selection and reducing resource waste during actual message delivery operations.
Solution Approach 2:
The system implements feedback mechanisms by using historical message performance data and user interaction patterns to continuously train and refine machine learning models. The models learn from past outcomes (user engagement, message effectiveness, qualification accuracy) to improve future message placement decisions, ensuring resources are allocated to messages with highest expected value.
2Adaptability or versatility
If targeted messages include audio and/or visual components, then messages can be more engaging, but more overhead is imposed for transmitting the message
Solution Approach 1:
The machine learning models evaluate user-specific factors and message characteristics to determine the optimal message format for each user-context pairing. The system adapts message content and format (text-only vs. multimedia) based on local conditions such as user preferences, device capabilities, network conditions, and predicted engagement metrics, delivering rich media only when it adds value without wasting bandwidth.
3Ease of operation
If targeted messages are delivered to users who are not qualified for the content, then messages can be presented, but resources are wasted that could be used for other productive purposes
Solution Approach 1:
The system performs preliminary qualification assessment by training machine learning models to predict user eligibility and suitability for targeted message content before message delivery. These models analyze historical user data, behavior patterns, and contextual information in advance to pre-determine which users are qualified to receive specific messages, filtering out unqualified recipients beforehand to avoid resource waste.
Data Source
AI summary
Certain aspects of the present disclosure provide techniques for placing targeted messages in communications within a software application using machine learning models. An example method generally includes retrieving, from a repository, a data set of targeted messages. For each respective targeted message in the data set of targeted messages, an effectiveness score for a party associated with the respective targeted message, a distance score between the party associated with the respective targeted message and a host party, and a match score between the party associated with the respective targeted message and the host party. Based on the effectiveness score, the distance score, and the match score for each respective targeted message in the data set of targeted messages, a message is selected to be included in one or more communications by the host party. The one or more communications including the selected message are generated and output for transmission.