Transformer Networks for Context-Aware Customer Relationship Management: Generating Personalized Engagement Sequences

Abstract

Customer Relationship Management (CRM) plays a pivotal role in ensuring businesses optimize customer engage- ment, retention, and satisfaction. Traditional CRM systems have typically relied on rule-based approaches or simple algorithms for customer interaction, which may fail to capture the dynamic and evolving nature of customer behavior. In this paper, we introduce a novel application of Transformer networks, a state- of-the-art deep learning architecture, to enhance CRM systems by generating personalized, multi-step engagement sequences and predicting customer churn risk. Our approach leverages two specialized Transformer models: a Sequence Transformer for the task of generating multi-step engagement plans and a Churn Transformer for predicting the risk of customer churn. These models harness the power of self-attention mechanisms to understand the sequential and contextual dynamics of customer behavior across time.

To evaluate the effectiveness of these models, we use simulated datasets inspired by real-world benchmarks, such as MovieLens, Amazon Product Data, and Kaggle Customer Churn. The Se- quence Transformer is trained to predict a series of actions for customer engagement based on historical interactions, while the Churn Transformer estimates the likelihood of customer attrition based on behavioral and demographic data. The results of our experiments show that after 10 epochs of training, the Sequence Transformer achieves an accuracy of 0.0167, while the Churn Transformer reaches an accuracy of 0.4000. Despite modest accuracy values, the models exhibit steady improvement, with training losses decreasing consistently from an initial value of 4.0456 to 3.7837 for the Sequence Transformer, and from 0.8096 to 0.7047 for the Churn Transformer.

The mathematical foundation behind the Sequence Trans- former involves minimizing the average cross-entropy loss over the predicted engagement sequence steps. Specifically, the loss function is defined as:

3

 

3                                            i i

 

Lseq = 1 Σ CrossEntropy(yˆ , y ),                                      (1)

i=1

where yˆi represents the predicted action for step i, and yi is the true action for the corresponding step in the sequence.

Similarly, the Churn Transformer optimizes the binary cross- entropy loss to estimate the likelihood of customer churn. The loss function is defined as:

1 Σ

 

                                       N

Lchurn = −                   [yj log(yˆj ) + (1 − yj ) log(1 − yˆj )] , (2)

N

j=1

where yj is the true churn label for customer j, and yˆj is the predicted churn probability.

Through detailed visualizations, including sample engagement plans, attention weight heatmaps, and ROC curves, this paper

illustrates the performance of the models and highlights the potential of Transformer networks in revolutionizing proactive, context-aware CRM strategies. While the accuracy results are constrained by the limitations of simulated datasets, the work lays a solid foundation for future enhancements, including the use of real-world data and more complex Transformer variants, ultimately contributing to more effective customer engagement and retention strategies.