CATS: Clustering-Aggregated and Time Series for Business Customer Purchase Intention Prediction

TL;DR

This paper introduces CAGRU, a novel model combining clustering and attention-based GRU networks to improve customer purchase intention prediction by addressing data imbalance and segment-specific behaviors.

Contribution

The paper proposes a unified clustering and attention mechanism GRU model that leverages multi-modal data for more accurate customer purchase prediction, especially in imbalanced datasets.

Findings

CAGRU outperforms traditional methods in purchase prediction accuracy.

Segment-specific training improves behavioral modeling.

The approach effectively handles data imbalance in customer groups.

Abstract

Accurately predicting customers' purchase intentions is critical to the success of a business strategy. Current researches mainly focus on analyzing the specific types of products that customers are likely to purchase in the future, little attention has been paid to the critical factor of whether customers will engage in repurchase behavior. Predicting whether a customer will make the next purchase is a classic time series forecasting task. However, in real-world purchasing behavior, customer groups typically exhibit imbalance - i.e., there are a large number of occasional buyers and a small number of loyal customers. This head-to-tail distribution makes traditional time series forecasting methods face certain limitations when dealing with such problems. To address the above challenges, this paper proposes a unified Clustering and Attention mechanism GRU model (CAGRU) that leverages multi-modal data for customer purchase intention prediction. The framework first performs customer profiling with respect to the customer characteristics and clusters the customers to delineate the different customer clusters that contain similar features. Then, the time series features of different customer clusters are extracted by GRU neural network and an attention mechanism is introduced to capture the significance of sequence locations. Furthermore, to mitigate the head-to-tail distribution of customer segments, we train the model separately for each customer segment, to adapt and capture more accurately the differences in behavioral characteristics between different customer segments, as well as the similar characteristics of the customers within the same customer segment. We constructed four datasets and conducted extensive experiments to demonstrate the superiority of the proposed CAGRU approach.