Predictive Offer Targeting via User Data Analysis
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Solution Overview
Problem
Existing software applications often present irrelevant or unqualified advertisements to users, wasting network resources and failing to optimize user experience.
Innovation Solution
A predictive modeling system that uses user account data and clickstream data to generate a predictive score, determining whether to present targeted offers based on a user's likelihood of interaction, thereby optimizing offer placement and reducing resource wastage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If advertisements are randomly selected and presented to users, then advertisement placement is simple and quick, but the advertisements are often irrelevant to users and waste network resources
Solution Approach 1:
The system performs preliminary actions by collecting user account data and clickstream data beforehand, reducing it to n-tuples, and training a predictive model in advance. This allows the system to quickly generate predictive scores when making advertisement placement decisions, maintaining high productivity while avoiding random selection and its associated resource waste.
Solution Approach 2:
The system changes the parameters of advertisement selection from random selection to predictive scoring based on multiple user attributes. By transforming user data into n-tuples with specific predictive attributes and using these to generate predictive scores, the system identifies relevant advertisements that match user characteristics, thereby reducing network bandwidth waste from irrelevant advertisements while maintaining efficient placement through automated scoring.
2Device complexity
If advertisements are presented without user qualification checks, then the advertisement placement process is simple, but resources are wasted on users who will not interact with the offers
Solution Approach 1:
The system performs preliminary qualification checks by training a predictive model in advance using user account data and clickstream data. The model learns to identify user characteristics that correlate with offer interaction, enabling quick predictive score generation that automatically qualifies users without adding complex real-time evaluation steps to the advertisement placement process.
Solution Approach 2:
The system replaces manual or simple random advertisement placement mechanisms with an automated predictive modeling system. The predictive model automatically qualifies users by generating scores based on their characteristics, substituting complex manual qualification processes with an automated statistical system that reduces network bandwidth waste while maintaining manageable system complexity.
3Measurement precision
If a predictive model is trained using detailed user account data and clickstream data, then advertisement targeting accuracy is improved, but the system complexity increases
Solution Approach 1:
The system segments user data into structured n-tuples with specific predictive attributes, separating relevant information from unnecessary data. This segmentation allows the predictive model to focus on key user characteristics that correlate with offer interaction, improving predictive score accuracy while managing system complexity through organized data structures and targeted feature selection.
4Productivity
If irrelevant advertisements are presented to users, then advertisement coverage is maximized, but user experience deteriorates and network resources are wasted
Solution Approach 1:
The system changes the parameter of advertisement selection from random or broad-based selection to predictive scoring based on user characteristics. By using trained predictive models that analyze user account data and clickstream data, the system delivers advertisements targeted to users likely to interact with them, maintaining high advertisement delivery volume while improving user experience through relevant content and reducing network waste.
Data Source
AI summary
Aspects of the present disclosure provide techniques for intelligently presenting targeted offers to a user of a software application. Embodiments include receiving received request from a user of the software application to access a portion of the software application. Using a predictive model, a predictive score is generated for the user. The predictive model generally may be a model trained using user account data reduced into n-tuples of predictive attributes representative of a plurality of users, correlated with clickstream data associated with the plurality of users indicating whether a user interacted with a targeted offer. The predictive score generally represents a likelihood that the user will interact with the targeted offer based on data stored by the software application for the user. The targeted offer is presented to the user based, at least in part, on a determination that the predictive score for the user exceeds a threshold score.