Offer personalization using deep learning

Here are key takeaways from a fascinating research paper by Ankur Verma. The research introduces an innovative approach to optimizing promotional offers in retail using artificial intelligence techniques.

The paper addresses the need for personalized marketing. Personalized offers can help retailers balance promotional spending and profits by targeting customers with suitable discounts at the right time.

The challenge

Traditional methods of managing promotions manually are complex and often inefficient. With increasing online shopping and market competition, retailers need innovative ways to balance promotional spending with sales and profits.

The solution

The solution involves using a non-linear machine learning model called a Temporal Convolutional Network (TCN), a type of sequence-based neural network, to predict how likely a customer is to purchase an item at a given time. The time window is decided based on the nature of the business and the customer’s purchase pattern. This prediction helps set optimal discount levels for each customer and item combination.

Predict which customers are likely to buy specific items.

Determine how different discount levels affect purchase likelihood.

Suggest optimal discount offers for each customer-item combination.

How does it work?

Optimization for profit: By understanding how different discount levels affect purchase probabilities, the model estimates the “offer-elasticity,” which is how sensitive a customer’s purchase decision is to changes in discount levels. This information is used to optimize discounts to maximize revenue and customer retention.

Data utilization: The model uses historical transaction data to make predictions. It considers factors like past purchase behavior, customer demographics, and item characteristics to tailor offers effectively.

Modeling methodology: The model treats each consumer-item pair as an individual object, using bi-weekly time series data to predict purchase probabilities. Features are generated from transactional data, including temporal and static attributes of consumers and items. The model architecture combines entity embeddings with TCN, using dilated convolutions to handle sequential data without information leakage from the future to the past. It uses F1-maximization to determine the suitable probability cut-off for each customer basis the probability distribution.Techniques like RMSProp, Adam optimizers, and Stochastic Weight Averaging (SWA) are used for model optimization.

Business impact: Implementing this AI-driven approach can significantly enhance a retailer’s bottom line by ensuring that promotions are practical and efficient, leading to increased sales and improved customer loyalty.

In summary, the paper highlights the potential of leveraging AI to refine promotional strategies, which would ultimately benefit the business by optimizing promotional spending and enhancing customer engagement.

The complete paper is available here: https://arxiv.org/abs/2010.08130

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