Paper Title: Multimodal data fusion for precision customer marketing based on deep learning: service quality perception and loyalty prediction
Authors: Xiaojing Nie, Fauziah Sh. Ahmad
Corresponding Author: Fauziah Sh. Ahmad (fsa@utm.my)/Malaysia
Abstract
Contemporary marketing faces challenges in analyzing complex, multidimensional customer-brand relationships from unprecedented volumes of multimodal data. Traditional analytical approaches inadequately capture this complexity, limiting precision marketing effectiveness. This research develops and validates a comprehensive multimodal data fusion framework utilizing deep learning architectures to enhance service quality perception analysis and customer loyalty prediction. The methodology integrates four data modalities—textual reviews, behavioral patterns, transactional records, and visual content—through specialized neural encoders: CNN for structured data, BERT transformers for textual analysis, LSTM networks for sequential behaviors, and transformer-based encoders for service indicators. Multi-head attention mechanisms and cross-modal feature weighting strategies unify these components while maintaining interpretability through SHAP-based analysis. Experimental validation across 15,420 customers demonstrates substantial performance improvements: service quality prediction (R² = 0.891, MAE = 0.142), customer loyalty classification (F1-score = 0.875, AUC-ROC = 0.923), and churn risk assessment (F1-score = 0.864, AUC-ROC = 0.917), significantly outperforming traditional baselines. Marketing optimization results demonstrate remarkable enhancements: conversion rates (+43.5%), ROI (+56.8%), click-through rates (+81.3%), and revenue per user (+71.1%), all of which are statistically significant (p < 0.001). Customer segmentation analysis reveals that value customers prioritize operational excellence and technical expertise, while regular customers emphasize interpersonal service dimensions. This framework advances multimodal learning theory in marketing contexts, providing practical foundations for next-generation customer relationship management systems. It enables enhanced customer engagement and business value creation through integrated data strategies.
Keywords
Multimodal data fusion, Deep learning, Customer loyalty prediction, Service quality perception, Precision marketing
