Prediction Model for Marketing Strategy Optimization
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Solution Overview
Problem
Traditional digital marketing models are myopic, focusing on short-term objectives and neglecting inter-state dependencies and long-term goals, which limits their ability to maximize revenue and user engagement.
Innovation Solution
A system identification framework is used to develop a prediction model that considers dependencies between variables and successive states, allowing for the evaluation of marketing strategies to optimize customer lifetime value (LTV) through Reinforcement Learning and Markov Decision Processes, simulating user interactions and predicting long-term responses to marketing offers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If traditional myopic marketing models are used to make determinations based on current conditions and short-term objectives, then the simplicity and ease of operation are maintained, but the ability to maximize long-term objectives such as revenue and customer satisfaction deteriorates
Solution Approach 1:
The system performs preliminary actions by collecting and analyzing historical marketing data offline to build predictive models before actual marketing decisions are made. These pre-computed models and insights are then applied during real-time marketing operations, allowing complex long-term optimization without increasing operational complexity during execution.
Solution Approach 2:
The patent introduces an intermediary predictive analytics system that bridges traditional simple marketing models and complex long-term optimization goals. This intermediary layer processes historical data and provides enhanced decision support, enabling improved long-term revenue maximization while maintaining the simplicity of existing marketing workflows.
2Device complexity
If traditional models consider only the next action/offer state in isolation, then the computational complexity and data processing requirements are reduced, but the accuracy of predicting user response and optimizing long-term objectives deteriorates
Solution Approach 1:
The patent segments the marketing decision-making process into distinct components: historical data collection, predictive model building, and real-time decision application. By dividing the complex task of long-term optimization into manageable segments, the system achieves high prediction accuracy without requiring complex models to run during actual marketing operations.
Solution Approach 2:
Complex predictive modeling and analysis are performed in advance as preliminary actions during offline data processing. This pre-computation of user response predictions and optimization insights eliminates the need for complex real-time calculations, maintaining low operational complexity while achieving high prediction accuracy.
3Ease of manufacture
If traditional marketing schemes neglect inter-state dependencies and long-term objectives, then the ease of implementation and operational simplicity are maintained, but the customer lifetime value and long-term revenue optimization deteriorates
Solution Approach 1:
The system implements self-service by automatically collecting historical marketing data, building predictive models, and generating optimization recommendations without requiring manual intervention. This automated approach maintains ease of implementation while significantly improving customer lifetime value optimization through data-driven insights.
Solution Approach 2:
An intermediary predictive analytics platform is introduced that automatically processes historical marketing data and provides long-term optimization recommendations. This intermediary system bridges the gap between simple existing marketing implementations and advanced LTV optimization, improving productivity without complicating the ease of implementation.
Data Source
AI summary
Optimizing customer lifetime value (LTV) techniques are described. In one or more implementations, a simulator is configured to derive a prediction model based on data indicative of user interaction online with marketing offers. The prediction model may be produced by automatically classifying variables according to feature types and matching each feature type to a response function that defines how the variable responds to input actions. The classification of variables and/or corresponding response functions per the prediction model may consider dependencies between variables and dependencies between successive states. An evaluator may then be invoked to apply the prediction model to test a proposed marketing strategy offline. Application of the prediction model is designed to predict user response to simulated offers/actions and enable evaluation of marketing strategies with respect to one or more long-term objectives.