Predictive Analytics for Targeted Advertising
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Solution Overview
Problem
Existing targeted advertising strategies struggle to effectively deliver the right advertising to the right person at the right time, leading to inefficiencies in resource utilization and revenue maximization for financial entities.
Innovation Solution
A computing system utilizing predictive analytics and data analytics to classify user actions, generate predicted action scores, and simulate user reactions to recommended actions, thereby identifying preferred user actions to be displayed on a digital platform.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional marketing strategies are used, then resource utilization is low and revenue maximization is difficult, but implementing targeted advertising requires complex data analysis and prediction systems
Solution Approach 1:
The system performs predictive analytics in advance to determine predicted action scores for multiple potential user actions before displaying recommendations. This preliminary classification and scoring of user actions enables the system to proactively identify the most likely successful recommendations, improving resource utilization by avoiding wasted display slots on low-probability actions while the complex analysis is performed beforehand rather than in real-time
Solution Approach 2:
The system creates simplified representations of complex user behavior patterns through predicted action scores. Instead of directly implementing complex data analysis whenever a recommendation is needed, the system uses pre-computed scores that copy and represent the outcomes of sophisticated analytics, enabling efficient decision-making without repeatedly executing complex analysis
2Measurement precision
If the system displays recommended actions based on predictive analytics, then user engagement accuracy improves, but the complexity of data processing and analysis increases
Solution Approach 1:
The system segments the complex data processing task into distinct phases: data collection from multiple sources, predictive analytics processing, classification of user actions into categories, scoring of segmented actions, and selection of top recommendations. This segmentation allows each component to be optimized independently and processed in manageable stages, improving prediction accuracy through focused analysis while reducing overall system complexity through modular architecture
Solution Approach 2:
The system introduces predicted action scores as an intermediary metric between raw user data and final recommendation display. This intermediary layer translates complex multi-source data into a simplified scoring system that represents user engagement likelihood, enabling accurate predictions without requiring the display system to directly process the underlying data complexity
3Reliability
If the system analyzes unstructured data to predict user actions, then targeted advertising effectiveness improves, but data processing time and computational resources increase
Solution Approach 1:
The system performs predictive analytics and classifies user actions in advance before recommendations need to be displayed. By pre-processing unstructured data and computing predicted action scores ahead of time, the system reduces real-time processing requirements while maintaining high targeted advertising effectiveness through accurate pre-computed predictions
Solution Approach 2:
The system creates simplified copies of complex user behavior patterns through predicted action scores. These scores represent the outcomes of extensive unstructured data analysis in a compact format that can be quickly retrieved and used for targeted advertising decisions, reducing the time needed to process raw data while preserving the reliability insights gained from comprehensive analysis
Data Source
AI summary
Systems and methods perform predictive analytics using analytical tool(s) on an aggregation of user action history data. A cluster model classifies action(s) previously entered into by a user, including scoring the action(s) previously entered into by the user. A predicted action score predicted to be currently applied to at least one of the action(s) previously entered into by the user is programmatically generated. Predicted user reaction(s) of the user resulting from providing, via a digital platform, the user with recommended user action(s) are programmatically simulated, the simulating identifying a preferred recommended user action capable of being at least partially performed by the user across a network via the digital platform. Program instructions determine that the user is accessing, via a user interface of a user device, the digital platform to perform user action(s), and display, via the user interface, the preferred recommended user action.