Optimizing Customer Loyalty with Reinforcement Learning-Driven Discounts

Personalized discounting strategies are vital for businesses aiming to maximize revenue while enhancing customer engagement. Traditional rule-based approaches often fail to adapt to dynamic customer behaviors. In this blog, we propose a Reinforcement Learning (RL)-based framework for optimizing personalized discounts, utilizing Q-learning to tailor offers to individual customers based on their behavioral patterns and purchase histories. The system adapts in real time, learning the optimal discounting strategy that balances short-term conversions with long-term customer retention. 

As businesses shift toward customer-centric strategies, personalized pricing and discounting are crucial in driving conversions and fostering loyalty. Rule-based systems, while simple, lack the flexibility to adjust to each customer’s unique preferences, leading to missed opportunities for maximizing revenue and customer lifetime value (CLV). We explore how Reinforcement Learning (RL), specifically Q-learning, can be applied to dynamic, personalized discounting. The RL model learns by interacting with customers and offering tailored discounts based on their past interactions, balancing immediate sales with long-term goals.

Proposed Solution

1. Data Processor - Collect and format customer information into states.
2. RL Agent - Takes the current state and learns an optimal discount strategy.
3. Discount Engine - Executes the action (discount) the RL agent decides.
4. Environment Simulator - Simulates customer reactions to the discount and returns a reward to update the RL agent.

Reinforcement Learning in Personalized Discounting

In the RL framework, the agent (the system) interacts with the environment (customers) and learns a policy to maximize rewards (sales and customer satisfaction). We define the key RL components for this problem:

State

The state represents a customer's context, which can include:

Customer purchase history (e.g., total spend, frequency).

Browsing behavior (e.g., items in cart, viewed items).

Customer segment (e.g., high-value, casual shopper).

Action

The actions are the discount offers:

No discount.

5% discount.

10% discount.

20% discount.

Reward

The reward function represents the business outcome:

Immediate reward: Positive if the customer completes the purchase, negative if no purchase or excessive discount.

Long-term reward: Positive for increased customer retention or frequent returns.

Policy

The policy defines the strategy for selecting actions (discounts) based on the customer's state, aiming to maximize cumulative rewards over time.

Reinforcement Learning Implementation(Q-Learning): Q-learning is a value-based reinforcement learning algorithm. It aims to learn a Q-function that represents the expected cumulative reward for taking a specific action in a given state.

The Q-function is updated using the Bellman Equation:

End-to-End Implementation of Personalized Discounting using Q-Learning Algorithm:

1. Define Customer Environment

The CustomerEnv (class) simulates the customer’s behavior based on their segment (low, medium, or high-value customer). Each segment has a different response to discounts, represented by the reward:

High-value customers respond better to smaller discounts.

Medium-value customers respond to moderate discounts.

Low-value customers are more likely to require larger discounts.

The environment provides the next state (customer segment) and a reward based on the discount.

2. Build the Q-learning Agent

The QLearningAgent class implements the core Q-learning logic:

Q-Table: The agent stores expected rewards for each state-action pair in the Q-table.

Epsilon-Greedy Policy: The agent explores random actions with a probability of epsilon and exploits the best-known action otherwise.

Q-Value Update: The Q-value for each state-action pair is updated using the Bellman equation.

Epsilon Decay: The agent gradually shifts from exploration (random actions) to exploitation (choosing the best-known action) by reducing the epsilon value over time

3. Train the Q-learning Agent:

We train the agent by simulating customer interactions over a number of episodes, where each episode represents a new customer scenario.

4. Evaluate the Model:

We evaluate how well the agent has learned by checking if it can offer optimal discounts based on the customer’s profile.

Conclusion and Future Work

After training the Q-learning agent, the Q-table converges toward an optimal policy for selecting discounts based on the customer segment. The model learns to:

Offer smaller discounts to high-value customers to maintain profitability.

Provide more substantial discounts to low-value customers to incentivize purchases.

By adjusting the hyperparameters, such as the discount factor (γ) and exploration rate (ε), the system can be fine-tuned for different business objectives, such as focusing on immediate revenue or customer retention.

This POC demonstrates the feasibility of using Q-learning for personalized discount strategies. The RL model adapts to individual customer behaviors, optimizing both short-term sales and long-term engagement. Future work could explore more advanced RL techniques, such as Deep Q-learning or policy-gradient methods for larger, more complex datasets and environments.

AUTHOR - FOLLOW
Johny Jose
Manager, Data Science