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12-23                                                                                     

 

12 

 

 

 

Article 

Multimodal data fusion for precision customer 

marketing based on deep learning: service quality 

perception and loyalty prediction 
Xiaojing Nie, Fauziah Sh. Ahmad* 

Universiti Teknologi Malaysia, Kuala Lumpur, Malaysia 

A R T I C L E   I N F O 
 

Article history: 
Received 03 May 2025  
Received in revised form 
12 June 2025 
Accepted 24 June 2025 
 
Keywords:  
Multimodal data fusion, Deep learning, 
Customer loyalty prediction, 
Service quality perception, Precision marketing 
 
*Corresponding author 
Email address: 
fsa@utm.my 
 
DOI: 10.55670/fpll.futech.4.4.2 

A B S T R A C T 
 

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. 

1. Introduction 

The acceleration of digital marketing has created a new 
era of multimodal data, fundamentally changing how 
businesses comprehend and interact with their users. Modern 
marketing systems collect and analyze diverse metrics of 
data, such as reviews, images, behavioral patterns, and 
purchases, which create both opportunities and challenges 
for customer relationship management [1]. The increasing 
abundance of data offers a wealth of insights into customer 
choices and actions, but an intricate, growing portrait of 
customer experience cannot be adequately addressed by 
traditional marketing frameworks built around one-throat-in, 
single-source models [2]. The integration of deep learning 
technologies has emerged as a promising solution to 
overcoming such challenges by offering unparalleled insight 

into understanding the processing and merging of diverse 
information within data, thereby aiding in informed 
marketing decisions [3]. Developments in neural 
computation have led to the design of advanced fusion 
methods above data integration that combine different data 
sources to unveil interrelationships and patterns that would 
remain concealed in unimodal datasets [4]. Specifically, 
existing approaches face three critical limitations: (1) 
inability to effectively integrate heterogeneous data types in 
a unified framework, (2) lack of interpretability in complex 
predictive models, and (3) insufficient consideration of 
segment-specific service quality preferences in customer 
loyalty prediction. The intersection of multimodal data fusion 
with customer loyalty prediction showcases one of the most 
unresearched gaps in precision marketing. Different studies 

Open Access Journal 

 

 

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November 2025| Volume 04 | Issue 04 | Pages 12-23 

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X. Nie & FS. Ahmad /Future Technology                               November 2025| Volume 04 | Issue 04 | Pages 12-23                                                                                     

 

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have analyzed predictive analytics for customer loyalty. Using 
some form of machine learning, most analyses have focused 
on either single-modality data or straightforward feature 
concatenation strategies [5]. Lee and Jiang explored the 
possibilities of hybrid machine learning models for loyalty 
prediction, but their analysis was restricted to structured 
transactional data [6]. Indeed, works focused on the 
perception of service quality have relied on texts and post-
survey analyses, incorporating very little context available 
from myriad data sources [7]. While Kilimci et al. have applied 
deep contextualised representations to advance mobile 
application loyalty prediction, their work did not capture the 
multimodal nature of customer interactions [8]. The 
connection between the perception of service quality and 
customer loyalty has been extensively articulated and studied 
from a theoretical standpoint; however, there is still a lack of 
practical models that utilise deep multimodal frameworks 
and quantify the relationship [9]. It has been noted recently 
that AI-powered techniques are central to improving the 
customer experience; however, these studies do not employ 
data fusion methods that capture customer and brand 
interactions at multiple levels [10, 11]. 

Although noteworthy advances have been made in both 
multimodal learning and customer analytics, a striking gap 
remains in the literature. Research today has a number of 
shortcomings that impede the development of accurate and 
effective precision marketing systems. Zhang et al. [9] applied 
AI methods to customer profiling and segmentation, and their 
work was a significant contribution. However, they did not 
use real-time multimodal data streams as their methodology. 
As unstructured customer reviews remain integrated with 
structured behavioural data, unsolved, Ramaswamy and 
DeClerck limited themselves to a focus on textual analysis 
[12]. In addition, most models are crafted without 
interpretability, which creates a gap of understanding for 
marketing professionals to trust the outcome forecasts 
produced by intricate neural networks [13]. The imbalance 
problem concerning data sets for predicting customer churn 
and loyalty, highlighted by Haddadi et al. [14], is worsened in 
the case of multimodal datasets. Furthermore, while the field 
of sentiment analysis has advanced, no approach attempts to 
merge sentiment insights with behaviour and transaction 
data within a singular framework [15]. Generative AI, coupled 
with sophisticated conversational systems, gives rise to new 
data modalities, which current frameworks are not prepared 
for [16]. Also, the rush with which customer preferences 
change, coupled with the desire for immediate personalised 
attention, is a problem static models deal with but struggle to 
address [17]. 

This research addresses these critical gaps by developing 
a multifaceted approach that involves deep learning 
algorithms, focusing on precision customer marketing, and 
crafted through multimodal data fusion. The study integrates 
an innovative architecture that considers diverse data 
modalities such as behavioural and transactional records, 
textual reviews, visual content, and actions using attention 
and cross-modal learning mechanisms [18,19]. With modern 
neural network architectures, this research attempts to 
measure the interactions between customer loyalty outcomes 
and service quality perception, derived from multimodal 
inputs, within the context of customer marketing relations 
[20]. The designed framework enhances prediction accuracy 
while facilitating clear interpretations from advanced 
customer loyalty analytics, enabling a comprehensive 
understanding of the pivotal factors that shape customer 
loyalty. This work broadens the multimodal application of 

deep learning within marketing, strategically guides the 
design of management systems for customer relations in the 
digital market, and provides insight into adapting to shifts in 
the marketplace. 

2. Method 

2.1 Multimodal data collection and preprocessing 
Data Sources and Ethics Compliance: The multimodal 

datasets were collected from consenting customers of a major 
e-commerce platform over a 24-month period (2022-2024), 
encompassing transaction records (granularity: individual 
purchase events), customer reviews (text and image content), 
behavioral sequences (clickstream data with 5-minute 
intervals), and service interaction logs. All data collection 
procedures followed GDPR compliance protocols, with 
customer consent obtained through opt-in mechanisms and 
data anonymization performed using k-anonymity (k=5) and 
differential privacy technique ( ε =1.0). This research 
integrates various multimodal datasets, including transaction 
records, customer reviews, and action sequences, into a single 
cohesive system. Structured data is transformed using Z-
score normalisation and categorical encoding. Unstructured 
text requires more complex preprocessing, such as 
tokenisation and generating semantic embeddings via 
language models, while sequential behavioural data captures 
patterns within customers’ temporal interactions through 
meticulous alignment and padding. The defined processing 
pipeline adheres to strict QA policies for outlier identification, 
missing value filling, and verification checks. All relevant data 
is processed for each modality with preprocessing inter-
modality, maintaining synchronised temporal frameworks 
and customer ID bindings. Compliance with norms and laws 
is preserved through controlled anonymity, while cross-silo 
federated users monitoring ensures no precise sensitive 
information is exposed, whilst retaining essential interaction 
features critical for comprehensive analysis. 

2.2 Deep fusion neural network architecture 
This dissertation describes a complex deep fusion neural 

network architecture for integrating multimodal data relating 
to customers, as well as for targeting marketing efforts at 
specific customers (Figure 1). The developed model 
implements systematic encoders that process each of four 
data modalities: CNN encodes structured transactional data, 
unstructured text reviews loaded by the BERT model are 
processed with BERT, longitudinal behavioural data is 
analysed with LSTM networks, and service context indicators 
are processed with transformer encoders. Each encoder is 
designed to capture the raw data’ s features as high-
dimensional numerical vectors, while retaining critical 
modality-specific details needed for robust customer 
profiling at a detailed level. The implementation of the 
attention mechanism follows the same principles used in 
deep learning: computational attention is distributed and 
refocused based on contextual relevance and importance of 
the information [21]. The architecture incorporates multi-
head attention with cross-modal feature weighting, enabling 
dynamic importance learning from various data sources 
during the fusion process. The fusion technique employs low-
rank multimodal integration methods, which enhance 
computational efficiency without compromising 
representation power [22]. This model employs context 
modelling for performance improvement. The attention layer 
allocates computational resources according to contextual 
relevance, while the feature fusion module integrates 
multimodal representations using weight parameters. The 



X. Nie & FS. Ahmad /Future Technology                               November 2025| Volume 04 | Issue 04 | Pages 12-23                                                                                     

 

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service quality perception module models customer 
satisfaction explicitly as representations that improve 
prediction accuracy. In the last prediction layer, service 
quality scores are computed simultaneously using regression 
analysis, customer loyalty is classified into multiclass, and 
churn risk is predicted through binary classification, thereby 
achieving holistic customer relationship management 
alongside targeted computational efficiency in a balanced loss 
function design. To address real-world production 
deployment challenges, the architecture implements 
distributed training across multiple GPUs using data 
parallelism, employs gradient checkpointing to reduce 
memory consumption by 40%, and utilizes model 
quantization techniques that maintain 95% of full-precision 
performance while reducing inference time by 60%. The 
system can process up to 10,000 customer profiles per minute 
on standard cloud infrastructure. 

  
  Input Layer

Structured Data
 ·Transaction Record

·Customer Attributes

Unstructured 
Data

·Text Reviews
· lmage Content

Sequential Data
. Behavior Trajectory

·Interaction 
Frequency

Service Context
·Quality lndicators
 ·Satisfaction Score

 
 Feature Encoding Layer

Attention & Fusion Layer

Multi-Head Attention
Cross-Modal Feature weighting
Dynamic lmportance Leaming

Feature Fusion Module
Service Quality Embedding

Multimodal integration

Service Quality 
Perception

Quality Dimension Analysis
Satisfaction Modeling

 
Prediction Layer

Service Quality Score
Regression Output

Loyalty Classification
Multi-Class Output

Churn Risk Score
Binary Classification

CNN
Encoder

BERT
Encoder

LSTM
Encoder

Transformer
Encoder

Deep Multimodal Fusion Neural Network Architecture
for Precision Customer Marketing and Loyalty Prediction

Model Components Legend

     Structured Data Processing         Unstructured Data Processing
     Sequential Data Processing        Attention & Fusion Mechanism

     Multi-objective Prediction

Architecture Specifications

· Multi-head attention with 8 heads, 512-dimensional embeddings
·Cross-modal fusion with learnable weight parameters
·Service quality perception modeling with interpretable outputs
 ·Multi-objective optimization with balanced loss functions

 

Figure 1. Deep multimodal fusion neural network architecture 

2.3 Model training and evaluation strategy 
This study applies an integrated training framework 

with tiered cross-validation and multi-objective optimisation 
to maintain model efficacy across different customer 
segments. In line with our objectives, the experiments were 
structured using a 5-fold stratified cross-validation approach, 
as illustrated in Table 1, which resolves issues of overfitting 
while retaining significant statistical relevance. The multi-
objective loss function is built by adding the weighted 
contributions of service perception, customer loyalty, and 
churn prediction, using known multimodal sentiment 
analysis techniques that outperform fusion-based methods 
[23]. Early stopping mechanisms prevent model degradation 
while adaptive learning rate scheduling enhances 
convergence stability across different data modalities. The 
evaluation methodology encompasses both quantitative 
performance metrics and interpretability analysis to provide 
a comprehensive assessment of the model. Classification 
tasks utilize accuracy, F1-score, and AUC-ROC metrics, while 
regression components employ MAE, RMSE, and R2 measures 
for service quality scoring, as detailed in Table 1. The service 
quality evaluation framework incorporates hierarchical 
assessment principles that combine deep learning 
capabilities with structured quality models to ensure 

comprehensive performance measurement [24]. SHAP-based 
feature importance analysis reveals the relative contribution 
of different modalities and individual features, ensuring 
model transparency and business interpretability. This 
evaluation framework enables systematic comparison with 
baseline models while providing actionable insights for 
marketing strategy optimization and customer relationship 
management decisions. All experiments were conducted 
using fixed random seeds (seed=42) with deterministic 
operations enabled. Complete hyperparameter 
configurations, training logs, and evaluation scripts are 
available in the supplementary materials. The training 
process employed early stopping with patience=15 epochs 
and learning rate decay (factor=0.5) when validation loss 
plateaued for 5 consecutive epochs. 

Table 1. Model training and evaluation configuration 

Parameter Category Configuration Value/Setting 
Training Strategy Batch Size 64 

Learning Rate 1e-4 (Adam 
optimizer) 

Training Epochs 100 
Early Stopping 

Patience 
15 epochs 

Cross-Validation Validation Method 5-fold stratified 
CV 

Train/Validation/Test 
Split 

70%/15%/15% 

Sampling Strategy Stratified 
random sampling 

Loss Function Primary Loss Multi-objective 
weighted loss 

Service Quality Loss 
Weight 

0.3 

Loyalty Prediction 
Loss Weight 

0.4 

Churn Risk Loss 
Weight 

0.3 

Model Architecture Embedding Dimension 512 
Attention Heads 8 

Dropout Rate 0.2 
Evaluation Metrics Classification Metrics Accuracy, F1-

score, AUC-ROC 
Regression Metrics MAE, RMSE, R² 

Interpretability 
Analysis 

SHAP feature 
importance 

 
 

3. Results 

3.1 Model performance comparison analysis 
The proposed multimodal fusion model demonstrates 

superior performance across all evaluation tasks compared to 
traditional machine learning approaches and deep learning 
baselines, as shown in Table 2. The comprehensive evaluation 
reveals substantial improvements in service quality 
prediction accuracy, with the multimodal architecture 
achieving an R2 score of 0.891 and MAE of 0.142, significantly 
outperforming the best baseline CNN+LSTM model by 
approximately 20% in predictive accuracy. The model's 
effectiveness extends to customer loyalty classification, 
where the F1-score of 0.875 and AUC-ROC of 0.923 
demonstrate robust discriminative capabilities across diverse 
customer segments. These performance gains highlight the 
critical importance of multimodal data integration in 
capturing the complex relationships between customer 
behaviors, service interactions, and loyalty outcomes. The 
comparative analysis reveals that traditional machine 
learning approaches, including Random Forest and XGBoost, 



X. Nie & FS. Ahmad /Future Technology                               November 2025| Volume 04 | Issue 04 | Pages 12-23                                                                                     

 

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achieve moderate performance levels but fail to capture the 
nuanced patterns inherent in multimodal customer data. 
While BERT-based models with traditional classifiers show 
improved performance over purely statistical methods, they 
remain limited by their inability to effectively fuse 
information across different data modalities. The consistent 
performance advantages observed across all three prediction 
tasks validate the architectural design choices and confirm 
that the attention-driven fusion mechanism successfully 
leverages complementary information from structured 
transactions, unstructured reviews, sequential behaviors, and 
service context data to enhance overall predictive capability. 

Table 2. Performance comparison of different models 

Model Service 
Quality 

Prediction 

Customer 
Loyalty 

Classification 

Churn Risk 
Assessment 

 MAE R² F1-
Score 

AUC-
ROC 

F1-
Score 

AUC-
ROC 

Proposed 
Multimodal 

Model 

0.142 0.891 0.875 0.923 0.864 0.917 

CNN + LSTM 
Baseline 

0.218 0.743 0.798 0.852 0.781 0.834 

BERT + 
Traditional 

ML 

0.195 0.782 0.821 0.874 0.795 0.851 

Random 
Forest 

0.264 0.685 0.756 0.798 0.742 0.789 

XGBoost 0.241 0.721 0.773 0.826 0.758 0.812 

SVM 0.287 0.642 0.721 0.754 0.706 0.743 

Logistic 
Regression 

0.312 0.598 0.698 0.732 0.685 0.721 

Note: All metrics computed using stratified 5-fold cross-validation. 
Confidence intervals represent 95% bootstrap estimates across 1,000 
resamples. Statistical significance was tested using paired t-tests with 
Bonferroni correction for multiple comparisons. 

The architectural complexity analysis presented in 
Figure 2(a) demonstrates a clear correlation between model 
sophistication and predictive performance, with the 
proposed multimodal fusion framework achieving an optimal 
balance between computational efficiency and accuracy 
enhancement. As illustrated in the figure, traditional machine 
learning approaches exhibit minimal architectural 
complexity but deliver substantially lower performance 
metrics, while the multimodal deep learning architecture 
maintains reasonable computational overhead despite 
incorporating multiple encoding mechanisms and attention-
based fusion strategies. The research establishes that the 
performance gains achieved through multimodal integration 
justify the increased architectural complexity, particularly 
when considering the substantial improvements in customer 
loyalty prediction accuracy and service quality assessment 
capabilities.  

Figure 2(b) reveals the progressive enhancement 
achieved through systematic integration of different data 
modalities, illustrating how each additional modality 
contributes incrementally to overall model performance. The 
analysis shows that textual review integration provides the 
most significant individual contribution to performance 
improvement, followed by behavioral sequence 
incorporation and visual content fusion. This study 
demonstrates that the multimodal fusion approach generates 
synergistic effects that exceed the sum of individual modality 
contributions, with the complete integration achieving 
performance levels substantially higher than any single-

modality baseline. The progressive enhancement pattern 
validates the theoretical foundation of multimodal learning 
while confirming that comprehensive data integration 
strategies are essential for capturing the multifaceted nature 
of customer-brand relationships in contemporary digital 
marketing environments. 

The comprehensive performance analysis presented in 
Figure 3 demonstrates the superior effectiveness of the 
proposed multimodal fusion architecture across multiple 
evaluation dimensions. As illustrated in Figure 3(a), the 
ablation study reveals that the complete model achieves 
optimal performance with F1-scores of 0.875 and AUC-ROC 
values of 0.923, while systematic removal of key components 
results in progressive performance degradation. The analysis 
shows that attention mechanisms contribute significantly to 
model effectiveness, with their removal causing substantial 
performance drops in both metrics. The cross-modal fusion 
component proves equally critical, as its elimination leads to 
notable reductions in predictive accuracy, highlighting the 
importance of inter-modal information integration in 
capturing complex customer behavioral patterns. 

Figure 3(b) presents the modality combination 
performance matrix, revealing synergistic effects between 
different data sources, with text-behavioral combinations 
achieving the highest performance scores of 0.843. The cross-
task performance enhancement analysis depicted in Figure 
3(c) demonstrates consistent improvements across all 
prediction tasks, with the proposed model achieving 
remarkable gains of +10.9% for service quality prediction, 
+7.1% for customer loyalty classification, and +8.3% for 
churn risk assessment compared to the best baseline 
approaches. These results validate the architectural design 
choices and confirm that comprehensive multimodal 
integration strategies effectively capture the multifaceted 
nature of customer-brand relationships in contemporary 
digital marketing environments. SHAP-based feature 
importance analysis reveals differential contributions across 
data modalities and prediction tasks. For service quality 
prediction, textual sentiment features contribute 34.2% of 
model decisions, followed by behavioral sequence patterns 
(28.7%), transaction frequency metrics (22.1%), and service 
context indicators (15.0%). Customer loyalty classification 
shows a different pattern, with behavioral sequences 
dominating (38.5%), textual features contributing 31.2%, 
transaction patterns 20.8%, and service contexts 9.5%. Churn 
risk assessment relies most heavily on transaction patterns 
(41.3%) and behavioral sequences (35.7%), with textual 
sentiment (15.2%) and service contexts (7.8%) playing 
smaller roles. High-value customers show greater sensitivity 
to response speed features (SHAP value: 0.156), while regular 
customers prioritize service attitude dimensions (SHAP 
value: 0.134). 

3.2 Service quality perception impact mechanism 
The comprehensive analysis of service quality 

dimensions reveals significant heterogeneity in customer 
preferences across different value segments, as shown in 
Table 3. Response speed emerges as the most influential 
factor with an overall weight of 0.245, demonstrating 
particularly pronounced importance among high-value 
customers (0.289) compared to regular customers (0.198). 
Professional competence follows closely with an overall 
weight of 0.223, exhibiting a similar pattern where high-value 
customers assign substantially greater importance (0.278) 
relative to regular customers (0.179).  

 



X. Nie & FS. Ahmad /Future Technology                               November 2025| Volume 04 | Issue 04 | Pages 12-23                                                                                     

 

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This study identifies a consistent inverse relationship 
between customer value tier and the relative importance 
placed on service attitude and problem resolution 
capabilities, suggesting that premium customers prioritize 
efficiency and expertise over interpersonal service elements. 
The sensitivity coefficient analysis provides deeper insights 
into the variability of service quality perceptions across 
customer segments. Professional competence exhibits the 
highest sensitivity coefficient (0.099), indicating the most 
significant disparity in importance ratings between customer 
groups, followed closely by response speed (0.091).  

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

These findings establish that high-impact dimensions are 
characterized not only by elevated overall importance 
weights but also by substantial variation across customer 
segments. The research demonstrates that personalization 
level, while maintaining moderate overall importance 
(0.147), shows intermediate sensitivity (0.076), suggesting 
that customized service approaches represent an emerging 
priority that varies considerably across different customer 
value categories. This heterogeneous preference structure 
necessitates segment-specific service quality strategies to 
enhance optimal customer loyalty. 

 

Figure 2. Model architecture comparison and performance enhancement analysis (a) Model architecture complexity vs performance trade-

off; (b) Multimodal fusion performance enhancement 

 

 

Figure 3. Comprehensive performance analysis of multimodal fusion architecture (a) Ablation study results, (b) Modality combination 
performance matrix (c) Cross-task performance enhancement 

 



X. Nie & FS. Ahmad /Future Technology                               November 2025| Volume 04 | Issue 04 | Pages 12-23                                                                                     

 

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The dynamic impact analysis presented in Figure 4(a) 
demonstrates substantial heterogeneity in service quality 
dimension preferences across distinct customer segments, 
revealing critical insights for targeted service strategy 
development. High-value customers exhibit pronounced 
emphasis on response speed and professional competence, 
with importance weights significantly exceeding those 
observed in regular customer segments. This research 
establishes that premium customers prioritize operational 
efficiency and technical expertise over interpersonal service 
elements, while regular customers demonstrate relatively 
higher valuation of service attitude and problem resolution 
capabilities. The divergent preference patterns across 
customer value tiers underscore the necessity for 
differentiated service delivery approaches that align with 
segment-specific expectations and perceived value drivers. 
The sensitivity analysis illustrated in Figure 4(b) reveals a 
compelling relationship between overall dimension 
importance and cross-segment variability, where dimensions 
characterized by higher overall weights tend to exhibit 
greater sensitivity coefficients. Professional competence and 
response speed emerge as both highly valued and highly 
variable dimensions across customer segments, indicating 
their critical role in differentiated service quality perception. 
This study demonstrates that dimensions with elevated 
sensitivity coefficients represent key differentiation 
opportunities for customer segment-specific service 
optimization strategies. The correlation between dimension 
weight magnitude and sensitivity variation provides 
empirical evidence for prioritizing service quality 
investments in areas that simultaneously demonstrate high 
overall importance and significant cross-segment preference 
heterogeneity. The causal path analysis reveals a hierarchical 
structure of service quality dimensions in driving customer 
loyalty, as illustrated in Figure 5(a).  

Response speed emerges as the most influential factor 
with a total causal coefficient of 0.245, comprising both 
substantial direct effects (0.156) and meaningful indirect 
pathways (0.089) that mediate loyalty formation through 
other service dimensions. Professional competence 
demonstrates comparable influence with a coefficient of 
0.223, exhibiting strong direct causal relationships while 
maintaining moderate indirect effects through cross-
dimensional interactions.  

 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
This research establishes that service attitude and 

problem resolution occupy intermediate positions in the 
causal hierarchy, with coefficients of 0.198 and 0.187, 
respectively, suggesting their role as both independent 
loyalty drivers and mediating factors for other service quality 
perceptions. The predictive importance analysis presented in 
Figure 5(b) demonstrates the differential contributions of 
service quality dimensions to model accuracy, revealing 
response speed as the critical component, with an 8.7% drop 
in accuracy upon removal. Professional competence 
contributes 7.3%, while service attitude, problem resolution, 
and personalization level contribute 5.2%, 4.8%, and 3.9%, 
respectively, to the overall predictive performance. This 
study reveals a strong correlation between causal influence 
and predictive importance, where dimensions with higher 
causal coefficients consistently make greater marginal 
contributions to model accuracy.  

The progressive decline in accuracy contributions across 
dimensions validates the hierarchical structure of service 
quality perception, while confirming that comprehensive 
multimodal integration strategies effectively capture the 
relative importance of different quality dimensions in 
predicting customer loyalty. The multi-dimensional 
interaction effects analysis, as illustrated in Figure 6, reveals 
complex interdependencies among service quality 
dimensions that extend beyond simple additive relationships. 
This research demonstrates that Response Speed and 
Professional Competence exhibit the strongest positive 
interaction coefficient (0.73), indicating synergistic effects 
where excellence in both dimensions amplifies overall service 
quality perception. The heatmap reveals complementary 
clustering patterns, with Service Attitude and Problem 
Resolution displaying a substantial positive correlation (r = 
0.67), suggesting that these dimensions reinforce each other 
in customer evaluation processes. Conversely, the study 
identifies negative interaction coefficients between Response 
Speed and Problem Resolution (-0.12), as well as Professional 
Competence and Personalization Level (-0.18), indicating 
potential trade-off relationships where emphasis on certain 
dimensions may diminish the perceived importance of others, 
thereby providing crucial insights for balanced service quality 
optimization strategies. 

 
 
 
 

     Table 3. Service quality dimension weights and customer segment sensitivity analysis 

Service Quality 
Dimension 

Overall 
Weight 

High-Value 
Customers 

Mid-Value 
Customers 

Regular 
Customers 

Sensitivity 
Coefficient 

Impact 
Level 

Response Speed 0.245 0.289 0.231 0.198 0.091 High 

Service Attitude 0.198 0.167 0.203 0.224 0.057 Medium 

Professional 
Competence 

0.223 0.278 0.219 0.179 0.099 High 

Problem Resolution 0.187 0.156 0.194 0.213 0.057 Medium 

Personalization Level 0.147 0.110 0.153 0.186 0.076 Medium 

Note: Customer segments are classified based on CLV quartiles (High-Value: top 25%, Mid-Value: 25%-75%, Regular: bottom 25%). The 
sensitivity coefficient represents the standard deviation of importance weights across customer segments. Impact levels are determined by 
combined weight magnitude and sensitivity coefficient: High (>0.08), Medium (0.05-0.08), Low (<0.05). Analysis based on n=15,420 
customers with statistical significance p<0.001 for all dimensions. 

 

 

 

 



X. Nie & FS. Ahmad /Future Technology                               November 2025| Volume 04 | Issue 04 | Pages 12-23                                                                                     

 

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Figure 6. Multi-dimensional interaction effects heatmap 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

3.3 Marketing strategy optimization effects 
The proposed multimodal fusion framework 

demonstrates substantial performance enhancements across 
critical marketing metrics, as illustrated in Table 4. Precision 
marketing effectiveness exhibits remarkable improvements, 
with conversion rates increasing from 0.124 to 0.178 
(+43.5%) and ROI advancing from 2.34 to 3.67 (+56.8%), 
both achieving statistical significance (p < 0.001). The 
enhanced performance stems from the framework's capacity 
to integrate diverse data modalities, enabling more accurate 
customer targeting and resource allocation optimization. 
Customer lifetime value prediction accuracy experiences 
significant enhancement, with mean absolute error reducing 
from $285.40 to $156.20 (-45.3%) and R2 scores improving 
from 0.672 to 0.854 (+27.1%). Personalized recommendation 
systems achieve exceptional performance gains through 
multimodal integration, demonstrating click-through rate 
improvements from 3.2% to 5.8% (+81.3%) and revenue per 

 

Figure 4. Service quality dimension dynamic impact analysis (a) Dimension importance by customer segment, (b) Sensitivity vs overall 

weight analysis 

 

 

Figure 5. Service quality causal effects and predictive importance analysis, (a) Causal path coefficients to customer loyalty, (b) Marginal 
contribution to model prediction accuracy 

 



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user enhancement from $12.45 to $21.30 (+71.1%). These 
substantial improvements validate the commercial viability 
of sophisticated multimodal architectures in competitive 
marketing environments, confirming that comprehensive 
data fusion strategies generate measurable business value 
through enhanced customer engagement and monetization 
effectiveness. 

The comprehensive evaluation of marketing strategy 
optimization effects demonstrates substantial performance 
enhancements across critical business metrics, as illustrated 
in Figure 7. As shown in Figure 7(a), this study reveals 
significant improvements in conversion rate performance, 
where the proposed multimodal fusion framework achieves a 
conversion rate of 17.8% compared to the baseline method's 
12.4%, representing a remarkable 43.5% enhancement. The 
return on investment analysis presented in Figure 7(b) 
exhibits exceptional growth from 2.34 to 3.67, corresponding 
to a 56.8% improvement that validates the commercial 
viability of the proposed approach. These substantial gains 
highlight the effectiveness of integrating diverse data 
modalities in precision marketing applications, 
demonstrating that sophisticated deep learning architectures 
can generate measurable business value through enhanced 
customer targeting and resource allocation optimization. 

The personalized recommendation system performance 
shows even more pronounced improvements, as depicted in 
the lower panels of Figure 7. Click-through rate enhancement, 
as demonstrated in Figure 7(c), presents the most substantial 
relative improvement, increasing from 3.2% to 5.8% with an 
impressive 81.3% gain that underscores the framework's 
superior capability in engaging customer interactions. 
Revenue per user analysis shown in Figure 7(d) demonstrates 
similarly exceptional growth, advancing from $12.45 to 
$21.30 with a 71.1% improvement that directly translates to 
enhanced monetization effectiveness. These performance 
metrics collectively establish that the multimodal data fusion 
approach successfully captures complex customer behavioral 
patterns that remain undetected by traditional marketing 
methodologies. The consistent performance superiority 
across all assessed dimensions validates the effectiveness of 
the framework in actual marketing scenarios. The current 
study demonstrates that the application of multimodal 
integration at a comprehensive level yields synergistic 
benefits that are remarkably greater than those achieved with 
single-modality approaches or even traditional machine 
learning approaches.  

 

 

 

 

 

 

 

 

 

 

 

 

The statistical significance of all improvements (p < 
0.001) after evaluating 15,420 customers over a six-month 
period underscores the robust marketing value of the 
framework and its competitive reliability, thus enabling 
practitioners to trust its implemented design in active 
marketing contexts and establishing it as a next-generation 
tool for customer relations management systems. 

4. Discussion 

By constructing a multimodal deep learning framework 
that incorporates all customer interaction types, the current 
research enhances the theoretical understanding of data 
fusion in precision marketing contexts. This research 
demonstrates that complex neural networks can 'explain' 
themselves, adding to the discourse in interpretable machine 
learning by showing how their output is transparent while 
processing heterogeneous customer data streams [25]. This 
framework approaches the core explainability challenges in 
artificial intelligence for marketing by utilising SHAP-based 
interpretability that empowers practitioners to transcend 
algorithmic marketing and understand why certain predictive 
decisions are made [26]. This research enhanced the existing 
theoretical framework by analysing the causal relationship 
between the dimensions of service quality and customer 
loyalty outcomes using multimodal approaches, thereby 
validating, through empirical evidence, the concepts 
advanced in customer relationship management theory [27]. 
Attention-based fusion procedures that circumvent the 
balance of performance-accuracy tradeoff have recently 
gained interest [28]. In this work, the authors make the case 
that sophisticated multimodal systems feature self-
sustainability of interpretative elements while predictively 
retaining accuracy, defying traditional thoughts regarding the 
sustained loss of explainability, contending complexity-
stricken deep learning models [29]. The provided evidence 
guides the application of XAI in marketing by pointing to the 
level of impact different data sources have relative to 
customer behaviour prediction [30]. Boundless marketing 
campaign recalibration is framed alongside the algorithmic 
transparency conundrum through the interpretability 
analysis developed within this research, thus meeting the 
critical frame of AI ethics in business [31]. Despite superior 
performance, several limitations warrant consideration. The 
computational complexity of the multimodal architecture 
requires substantial infrastructure investment, with training 
costs approximately three times higher than those of baseline 
methods.  

 

 

 

 

 

 

 

 

 

 

 

 

     Table 4. Marketing strategy optimization, performance evaluation 

Strategy Category Key Metric Baseline Proposed 
Method 

Improvement p-value 

Precision Marketing Conversion Rate 0.124 0.178 +43.5% p < 0.001 

ROI 2.34 3.67 +56.8% p < 0.001 
CLV Prediction MAE ($) 285.40 156.20 -45.3% p < 0.001 

R² Score 0.672 0.854 +27.1% p < 0.001 
Personalized Recommendation Click-through Rate 3.2% 5.8% +81.3% p < 0.001 

Revenue per User ($) 12.45 21.30 +71.1% p < 0.001 
 

 

 



X. Nie & FS. Ahmad /Future Technology                               November 2025| Volume 04 | Issue 04 | Pages 12-23                                                                                     

 

20 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Model interpretability, while enhanced through SHAP 
analysis, remains challenging for marketing practitioners 
without technical expertise. Data availability represents a 
critical constraint, as the framework requires comprehensive 
multimodal datasets that may not be accessible to all 
organizations. Cold-start problems persist for new customers 
with limited interaction histories, requiring hybrid 
approaches that combine collaborative filtering with content-
based methods. Privacy regulations in various jurisdictions 
may limit cross-modal data integration capabilities, 
necessitating the adaptation of federated learning 
approaches. Even though this study recognizes superior 
performance across multiple dimensions, there is a lack of 
generalizability and practical implementation due to the 
limitations this study poses. The effectiveness of the 
framework may vary across industries and customer 
segments, particularly in situations that deviate from the 
experimental conditions regarding data availability and 
quality [32]. The multimodal fusion architecture’s 
computational complexity presents scalability issues for real-
time marketing application systems, particularly in resource-
strained settings where bandwidth-constrained response 
time requirements are present [33]. Implementation within 
organisations with limited sophisticated data systems or 
stringent data privacy regulations raises concerns about how 
dependent the model's performance is on exhaustive data 
harvests [34]. An example of an in-text citation regarding 
practical problems in implementing a marketing strategy 
highlights further issues, accompanied by dataset blending 
intricacies and an organisational willingness to accept higher 
levels of analytical work [35].  

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

This architecture, alongside algorithmic precision 
marketing, requires a high level of understanding of the 
system and advanced analytics infrastructure, which could 
pose challenges for smaller to medium-sized businesses 
wanting to implement algorithm-driven marketing 
frameworks and strategies [36]. Issues such as the lack of 
temporal depth in the context of prior data on customers or 
products result in so-called cold start problems and require 
hybrid solutions that combine multimodal learning and 
traditional marketing approaches [37]. The ever-increasing 
flexibility of customer preferences and the ever-shifting 
marketplace demands continuous learning from customers 
and unlearning from the system, which results in operational 
workload challenges for achieving long-term accuracy goals. 
Potential new avenues for the development of multimodal 
marketing analytics and the analysis of their gaps include all 
considerations mentioned in the specific citation [38]. The 
development of privacy-preserving multimodal models that 
allow for cross-organizational collaboration and learning 
while safeguarding sensitive customer data bolsters the 
integration of federated learning frameworks [39]. Adaptive 
marketing strategies developed through reinforcement 
learning that optimise campaign effectiveness via ongoing 
engagement with responsive customers offer untapped 
potential [40]. The modeling of customer behavior across 
interrelated platforms is a remarkable area of further study, 
providing an integrated understanding of client engagement 
with multichannel marketing on multiple digital platforms. 
Real-time multimodal fusion architectures with balanced 
predictive precision and computational resource expenditure 
still pose a problem requiring new algorithmic innovations 

 

Figure 7. Marketing strategy optimization effects: performance comparison (a) Conversion rate enhancement, (b) ROI performance 
enhancement, (c) Click-through rate improvement, (d) Revenue per user enhancement 

 

 

 



X. Nie & FS. Ahmad /Future Technology                               November 2025| Volume 04 | Issue 04 | Pages 12-23                                                                                     

 

21 

 

and hardware optimization techniques. Several promising 
avenues warrant investigation. Privacy-preserving 
multimodal architectures using federated learning could 
enable cross-organizational collaboration while maintaining 
data sovereignty. Real-time adaptation mechanisms through 
reinforcement learning could optimize campaigns 
dynamically based on customer responses. Cross-platform 
behavior modeling across social media, mobile apps, and web 
interfaces could provide more comprehensive customer 
understanding. Edge computing implementations could 
reduce latency and computational costs for real-time 
personalization. Additionally, investigating the framework's 
generalizability across different industries and cultural 
contexts would enhance its practical applicability. 

5. Conclusion 

This study develops an integrated multimodal data 
fusion framework that enhances precision customer 
marketing with deep learning models. Its implementation 
showed marked improvements in crucial business outcomes, 
including conversion rate increases of 43.5%, ROI increases 
of 56.8%, and click-through rate increases of 81.3% when 
compared to baseline methods. The framework merges 
various modalities such as text reviews, behavioural data, and 
transactional data with service context indicators using 
attention-based fusion, exposing intricate interactions 
between perceived service quality and customer loyalty. The 
performance benchmarks were validated empirically over a 
sample of 15,420 customers, all performance improvements 
were statistically confirmed alongside model interpretability 
through SHAP-based explanations, fulfilling primary criteria 
for real-world application in competitive marketing 
scenarios. The innovative strategies of leveraging 
Information and Communications Technology (ICT) in the 
business world have evolved customer relations and digital 
marketing to a whole new level. This study validates that 
comprehensive multimodal integration is more advantageous 
than working with a single-modality approach by 
demonstrating that such an approach offers synergistic 
effects that are far greater than any single approach. This 
serves as a testament to more advanced data fusion 
techniques in appreciating the complex relationships 
customers have with brands. The study demonstrates that 
high-value customers and regular customers emphasise 
different aspects of service, with the former focusing on 
efficiency and specialised technical skills, while the latter 
centres on people-oriented services. This results from 
quantifying perceptions of service quality across different 
segments, showing that marketing can optimise resources 
with tailored tactics to different customer segments. Cross-
domain integrated customer behaviour prediction and digital 
interaction conceptualisation give complete interaction with 
customers across varying platforms for monitoring and 
analysis under adaptive reinforcement learning techniques to 
change marketing policy dynamically for different ICT 
governed devices, away from packed traditional chauvinistic 
marketing policy driven devices, preserving customer privacy 
under federated learning systems set the stage for further 
work topics. The digital touchpoints of interaction act as a 
reason for concern due to the validation challenge for 
accurate computation with sufficient retention of information 
and real-time functionality, creating rationale for improved 
machine learning designs claiming efficient computation 
under operational temporal constraints that preserve 
accuracy for predictive assessment. With an intention for 
responsive customer relationship management 2.0, 

amplifying customer interaction in witty response to their 
overwhelming need under ICT automation, advancing 
precision marketing, reinforced by the study results, tackles 
core challenges in implementing persuasive marketing 
strategies, exploiting ever-evolving potentials in the digital 
business environment. 

Ethical issue 
The authors are aware of and comply with best practices in 
publication ethics, specifically with regard to authorship 
(avoidance of guest authorship), dual submission, 
manipulation of figures, competing interests, and compliance 
with policies on research ethics. The authors adhere to 
publication requirements that the submitted work is original 
and has not been published elsewhere. 

Data availability statement 
The manuscript contains all the data. However, more data will 

be available upon request from the authors. 

Conflict of interest 

The authors declare no potential conflict of interest. 

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