Deep Reinforcement Learning for Dynamic Marketing Cost Control
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Solution Overview
Problem
In marketing scenarios, especially financial marketing, excessive incentives lead to higher marketing costs and budget overruns, necessitating a solution to minimize costs while maintaining marketing effectiveness.
Innovation Solution
A deep reinforcement learning system is employed to determine marketing behaviors that minimize costs while ensuring user conversion, by using an agent and execution environment to calculate a reward score negatively correlated with the cost of marketing behaviors, optimizing marketing strategies based on user responses and cost considerations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If excessive incentives are provided to users in marketing information, then the possibility of users using financial products increases, but marketing costs increase and budget overruns occur
Solution Approach 1:
The patent dynamically adjusts incentive parameters (coupon amounts, discount rates) based on real-time feedback from the reinforcement learning system. The agent learns optimal parameter values that maximize user conversion while controlling marketing costs, transitioning from static to adaptive parameter selection.
Solution Approach 2:
The system implements a closed-loop feedback mechanism where the agent receives rewards or penalties based on the actual outcomes of marketing actions. This feedback drives continuous optimization of marketing strategies, allowing the system to learn from past performance and adjust future actions to balance effectiveness and cost.
2Loss of energy
If proper solutions are implemented to minimize marketing cost, then budget control improves, but marketing effectiveness may be compromised
Solution Approach 1:
The patent transforms static marketing strategies into dynamic, adaptive ones. The reinforcement learning agent continuously adjusts marketing actions based on changing conditions and learned patterns, enabling the system to optimize the balance between cost and effectiveness in real-time rather than relying on fixed rules.
Solution Approach 2:
The system employs self-learning through reinforcement learning, where the agent autonomously improves its marketing strategy by learning from interactions with the environment. This self-service capability allows the system to automatically discover cost-effective marketing approaches without constant manual intervention or optimization.
Data Source
AI summary
Embodiments of the present specification provide methods for performing marketing cost control by using a deep reinforcement learning system. One method includes the following: determining a cost of a marketing activity; determining a reward score of reinforcement learning that is negatively correlated with the cost; and returning the reward score to a smart agent of a deep reinforcement learning system, for the smart agent to update a marketing strategy, wherein the smart agent is configured to determine a marketing activity based on the marketing strategy and status of an execution environment of the deep reinforcement learning system.