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PUBLISHED DATE: - 04-09-2024
https://doi.org/10.37547/tajet/Volume06Issue09-02
PAGE NO.: - 6-17
OPTIMIZING E-COMMERCE PRICING STRATEGIES: A
COMPARATIVE ANALYSIS OF MACHINE LEARNING
MODELS FOR PREDICTING CUSTOMER SATISFACTION
Md Salim Chowdhury
College of Graduate and Professional Studies Trine University, USA
Md Shujan Shak
Master of science in information technology, Washington University of science and
technology, USA
Suniti Devi
Department of Management Science and Quantitative Methods, Gannon University, USA
Md Rashel Miah
Department of Digital Communication and Media/Multimedia, Westcliff University, USA
Abdullah Al Mamun
Department of Computer & Info Science, Gannon University, Erie, Pennsylvania, USA
Estak Ahmed
Department Of Computer Science, Monroe College, New Rochelle, New York, USA
Sk Abu Sheleh Hera
Ketner School of Business, Trine University, USA
Fuad Mahmud
Department of Information Assurance and Cybersecurity, Gannon University, USA
MD Shahin Alam Mozumder
Master of science in information technology, Washington University of science and
Technology, USA
RESEARCH ARTICLE
Open Access
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INTRODUCTION
In the rapidly evolving landscape of e-commerce,
pricing strategies play a crucial role in influencing
customer satisfaction and driving business success.
As
businesses
seek
to
enhance
their
competitiveness and optimize their pricing
approaches, leveraging advanced data-driven
methodologies has become increasingly important.
Machine learning models offer powerful tools for
analyzing and predicting customer behavior,
enabling businesses to make informed decisions
that align with market dynamics and consumer
expectations.
This study explores the application of various
machine learning techniques to optimize e-
commerce pricing strategies, focusing on
improving customer satisfaction through precise
and data-driven pricing decisions. By evaluating
and comparing the performance of five prominent
machine learning models
—
Linear Regression,
Decision Trees, Random Forest, Support Vector
Machines (SVM), and Neural Networks
—
the study
aims to identify the most effective approach for
predicting pricing outcomes and enhancing
customer satisfaction.
The dataset employed for this analysis comprises a
rich
collection
of
historical
e-commerce
transaction records, capturing a diverse array of
variables including customer satisfaction scores,
pricing information, and demographic attributes.
This comprehensive dataset, aggregated from
multiple e-commerce platforms, provides a robust
foundation for training and evaluating the models.
Data preprocessing was a critical phase in this
study, involving essential steps such as outlier
removal, missing value imputation, normalization
of variables, and encoding of categorical data.
These preprocessing techniques ensured the
dataset's quality and suitability for machine
learning applications, enabling accurate and
reliable model training.
The evaluation of model performance utilized a
range of metrics
—
Mean Absolute Error (MAE),
Root Mean Square Error (RMSE), R-squared (R²),
and F1-Score
—
each offering valuable insights into
different aspects of model effectiveness. By
systematically comparing these metrics, the study
assesses how well each model manages prediction
accuracy, error handling, explanatory power, and
balance between precision and recall.
Ultimately, the study provides actionable
recommendations for businesses seeking to
enhance their pricing strategies. The comparative
analysis highlights the strengths and limitations of
Abstract
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each model, offering guidance on selecting the most
appropriate approach based on specific business
needs, resource constraints, and the importance of
model interpretability versus predictive accuracy.
In summary, this research underscores the
significance of employing sophisticated machine
learning techniques in optimizing e-commerce
pricing strategies. By integrating these techniques
into pricing decisions, businesses can better align
their
pricing
strategies
with
customer
expectations, improve satisfaction, and achieve
more favorable business outcomes.
LITERATURE REVIEW
The application of machine learning (ML)
techniques to e-commerce pricing strategies has
garnered significant interest in recent years. These
methodologies
offer
advanced
analytical
capabilities that can substantially enhance pricing
decisions and improve customer satisfaction. This
literature review provides an overview of key
research, and findings related to ML in e-commerce
pricing, focusing on the use of various predictive
models and their impact on pricing strategies.
Machine learning models have been extensively
explored for their potential to optimize pricing
strategies in e-commerce. Research by Aggarwal
and Gupta (2018) highlights the effectiveness of
supervised learning algorithms, including Linear
Regression and Decision Trees, in predicting
optimal pricing strategies based on historical data.
They emphasize that these models can capture
complex patterns in pricing and customer
behavior, thus aiding in dynamic pricing
adjustments.
Further advancements in ML techniques, such as
Random Forest and Support Vector Machines
(SVM), have been shown to enhance pricing
predictions. For instance, Zhang et al. (2020)
demonstrate that Random Forest, with its
ensemble approach, provides robust predictions
by reducing variance and improving accuracy.
Similarly, SVMs have been found effective in
classifying customer preferences and adjusting
pricing strategies accordingly (Cortes & Vapnik,
1995).
The introduction of Neural Networks, particularly
deep learning models, has marked a significant
shift in pricing optimization. LeCun, Bengio, and
Hinton (2015) discuss the advantages of Neural
Networks in capturing intricate relationships
within large datasets, which traditional models
might miss. Neural Networks, with their ability to
learn non-linear patterns, have been shown to
outperform other models in various tasks,
including
pricing
strategy
optimization
(Goodfellow, Bengio, & Courville, 2016). The
application of Neural Networks in e-commerce
pricing, as indicated by Nguyen et al. (2019), has
led to improvements in prediction accuracy and
customer satisfaction due to their capability to
handle large and complex datasets.
Evaluating the performance of ML models is crucial
for understanding their effectiveness in pricing
strategies. Metrics such as Mean Absolute Error
(MAE), Root Mean Square Error (RMSE), R-squared
(R²), and F1-Score are commonly used to assess
model accuracy and reliability. According to
Hyndman and Athanasopoulos (2018), MAE and
RMSE are fundamental for measuring prediction
errors, with RMSE providing a more sensitive
assessment due to its penalization of larger errors.
R² is useful for understanding the proportion of
variance explained by the model, while F1-Score is
particularly relevant in classification tasks where
the balance between precision and recall is
important (Powers, 2011).
Comparative analyses of ML models have been
conducted to determine their suitability for various
applications, including pricing strategies. For
example,
Kotsiantis
(2007)
provides
a
comprehensive review of different models,
highlighting the strengths and limitations of each in
predictive tasks. The findings suggest that while
complex models like Neural Networks offer high
accuracy, simpler models such as Random Forest
can provide a good balance between performance
and interpretability. This balance is crucial for
businesses that need to justify pricing decisions to
stakeholders and align with practical resource
constraints (Breiman, 2001; Quinlan, 1986).
The practical application of ML models in e-
commerce pricing has been shown to enhance
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customer satisfaction and business outcomes.
Research by Chen et al. (2019) demonstrates that
effective pricing strategies, informed by predictive
analytics, can lead to increased customer loyalty
and reduced churn. By leveraging advanced ML
models, businesses can set prices that better align
with customer expectations, ultimately improving
profitability
and
competitive
advantage
(Brynjolfsson, Hu, & Simester, 2013).
METHODS AND MATERIALS
Data Collection and Preprocessing
The dataset utilized for this study is a
comprehensive collection of historical e-commerce
transaction records. It encompasses a range of
variables including customer satisfaction scores,
detailed pricing information, and various
demographic attributes of the customers. To
ensure that the dataset is both comprehensive and
representative, data were aggregated from a wide
range of e-commerce platforms. This approach was
taken to capture a broad spectrum of e-commerce
activities and customer interactions, providing a
diverse and robust dataset for analysis. By
collecting data from multiple sources, the study
benefits from a richer and more varied dataset that
reflects different market conditions and customer
behaviors.
The data preprocessing phase was critical to
enhancing the quality and usability of the dataset.
During this stage, several key operations were
performed to clean and prepare the data for
analysis. Outliers, which could skew the results,
were identified and removed to ensure the
accuracy of the analysis. Missing values were
addressed through appropriate imputation
techniques to maintain the integrity of the dataset.
Furthermore, to facilitate consistent analysis,
pricing and satisfaction scores were normalized,
ensuring that the data was on a comparable scale.
Categorical variables were also encoded,
transforming them into numerical formats that are
suitable for machine learning algorithms. This
preprocessing work was essential for creating a
reliable dataset that could be effectively used for
training and evaluating machine learning models.
Following the preprocessing, the dataset was
systematically divided into two distinct subsets: a
training set and a testing set. The training set,
which constituted 70% of the entire dataset, was
utilized to develop and train the machine learning
models. This portion of the data was used to teach
the models to recognize patterns and make
predictions based on the historical e-commerce
records. The remaining 30% of the dataset was set
aside as the testing set. This subset was reserved
for evaluating the performance and accuracy of the
trained models, providing an unbiased assessment
of how well the models generalize to new, unseen
data. This careful partitioning of the dataset
ensures that the models are both well-trained and
rigorously tested.
Model Selection and Training
In this study, we evaluated the performance of five
distinct machine learning models, each chosen for
its prominent application and efficacy in predictive
analytics. The models selected for this evaluation
include Linear Regression, Decision Trees, Random
Forest, Support Vector Machines (SVM), and
Neural Networks. The rationale behind selecting
these models stems from their widespread use in
various predictive tasks and their proven track
records in delivering accurate results across
diverse datasets.
To ensure a comprehensive analysis, each model
was meticulously trained on the designated
training subset of the data, employing well-
established training methodologies. For the Neural
Networks model, a multi-layer perceptron (MLP)
architecture was utilized. This architecture is
defined by a specific configuration of layers and
neurons, tailored to capture complex patterns
within the data. Training of the Neural Networks
was carried out using the backpropagation
algorithm, which adjusts the weights of the
network through gradient descent optimization.
This process involves minimizing the error by
iteratively updating the network's parameters
based on the gradient of the loss function.
Furthermore, to enhance the performance of each
model, hyperparameters were meticulously fine-
tuned. For Decision Trees, this involved adjusting
parameters such as tree depth, which controls the
maximum levels of the tree. For Random Forest, the
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number of estimators, or individual decision trees
within the forest, was optimized. The
hyperparameter tuning process employed grid
search techniques in conjunction with cross-
validation. Grid search systematically explores
various combinations of hyperparameters to
identify the optimal settings, while cross-validation
assesses the model’s performance by partitioning
the training data into subsets and validating the
model on each subset. This rigorous approach
ensures that the model's performance is robust and
generalizable.
Evaluation Metrics
Model performance was rigorously evaluated using
a comprehensive set of evaluation metrics, each
serving a distinct purpose in assessing the
effectiveness of the predictive models. The metrics
employed include Mean Absolute Error (MAE),
Root Mean Square Error (RMSE), R-squared (R²),
and F1-Score. Each metric provides valuable
insights into different aspects of model
performance, contributing to a well-rounded
assessment.
Mean Absolute Error (MAE) is utilized to quantify
the average magnitude of errors in the model's
predictions. This metric calculates the average
absolute difference between the predicted values
and the actual values. MAE is particularly useful for
understanding the typical size of the prediction
errors, providing a straightforward measure of
how close the predictions are to the true values. It
offers an intuitive sense of the model's accuracy,
with lower MAE values indicating better predictive
performance.
Root Mean Square Error (RMSE) is another critical
metric used to evaluate model performance. Unlike
MAE, RMSE emphasizes larger errors more
significantly, due to the squaring of the differences
between predicted and actual values before
averaging. This means that RMSE is sensitive to
outliers and provides a measure of the standard
deviation of the residuals. By penalizing larger
errors more heavily, RMSE offers a nuanced view of
the model's error distribution, highlighting the
impact of significant deviations on overall
performance.
R-squared (R²) is employed to measure the
proportion of variance in the dependent variable
that is explained by the independent variables in
the model. This metric provides an indication of
how well the model captures the variability in
customer satisfaction or pricing predictions. An R-
squared value close to 1 indicates that a substantial
proportion of the variance is explained by the
model, reflecting strong explanatory power.
Conversely, an R-squared value close to 0 suggests
that the model does not account for much of the
variance, indicating limited explanatory capability.
F1-Score is used to assess the balance between
precision and recall in the context of predicting
customer satisfaction. Precision refers to the
proportion of true positive predictions among all
positive predictions made by the model, while
recall measures the proportion of actual positive
cases that were correctly identified by the model.
The F1-Score is the harmonic mean of precision
and recall, providing a single metric that captures
both aspects. This metric is particularly useful in
scenarios where there is an imbalance between
positive and negative classes, ensuring that both
false positives and false negatives are
appropriately considered in the evaluation.
Together, these metrics offer a holistic evaluation
of each model's performance, covering aspects
such as accuracy, error distribution, explanatory
power, and balance between precision and recall.
By analyzing these metrics collectively, a
comprehensive understanding of each model's
strengths and weaknesses is achieved, facilitating
informed decisions regarding model selection and
optimization.]
Comparative Analysis
A comparative analysis was conducted to evaluate
the performance of the models across the defined
metrics. Performance metrics for each model were
visualized using bar charts, facilitating a
straightforward
comparison.
This
analysis
considered accuracy, error rates, the model's
ability to explain variance, and the balance
between precision and recall. Statistical analysis
was employed to detect significant differences in
performance metrics and to determine the model
that best meets the criteria for optimizing pricing
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strategies.
Model Selection and Recommendations
The comparative analysis revealed that the Neural
Networks model demonstrated the highest
performance across all evaluated metrics,
including MAE, RMSE, R², and F1-Score. This
indicates its robustness in capturing complex
relationships between pricing and customer
satisfaction.
However,
the
computational
complexity and resource demands of Neural
Networks may be a constraint for some businesses.
In such cases, the Random Forest model is
recommended as a viable alternative, offering a
balanced trade-off between accuracy and
interpretability. Businesses are advised to select a
model based on their specific requirements,
available resources, and the relative importance of
model interpretability versus prediction accuracy
to effectively tailor their pricing strategies.
RESULT
The results of the machine learning models
outlined in the analysis provide valuable insights
into how businesses can optimize their e-
commerce pricing strategies to enhance customer
satisfaction. By understanding the relationship
between pricing and customer satisfaction, these
models enable businesses to make data-driven
decisions that align with customer expectations,
ultimately leading to better business outcomes.
Mean Absolute Error (MAE)
in Price Prediction: The MAE values indicate how
closely the predicted prices align with actual
customer satisfaction scores. A lower MAE, as seen
with the Neural Networks model, suggests that the
pricing strategies generated by this model are
more precise in reflecting what customers are
willing to pay. This precision minimizes the risk of
setting prices too high or too low, both of which can
negatively impact customer satisfaction. For
instance, if a price is too high, customers may feel
overcharged and dissatisfied; if too low, customers
might perceive the product as low quality. The
Neural Networks model, with its lowest MAE, can
help set prices that are perceived as fair and
appropriate by customers, thereby enhancing
satisfaction and loyalty.
Table 1: Mean Absolute Error (MAE)
Model
MAE
Linear Regression
0.152
Decision Trees
0.145
Random Forest
0.130
SVM
0.142
Neural Networks
0.126
Table 1 shows that the Mean Absolute Error (MAE)
for different models reveals their predictive
accuracy for customer satisfaction scores. Neural
Networks achieved the lowest MAE of 0.126,
reflecting its high precision in aligning predicted
prices with actual satisfaction, making it highly
effective for fine-tuning pricing strategies. Random
Forest followed with an MAE of 0.130, indicating its
strong performance in providing accurate pricing
predictions, which is crucial for dynamic pricing
environments where small deviations can
significantly impact satisfaction. Linear Regression
had the highest MAE of 0.152, suggesting it may
struggle with capturing the complexities of
customer satisfaction in e-commerce pricing. This
higher error rate implies that pricing strategies
based on this model might be less aligned with
customer preferences, potentially leading to
greater dissatisfaction.
Root Mean Square Error (RMSE)
Handling Larger Pricing Errors: RMSE is
particularly useful for identifying models that can
prevent significant pricing mistakes. Large pricing
errors can lead to substantial customer
dissatisfaction, as customers may feel that the
prices are unjustified or inconsistent with their
expectations. The Neural Networks model, with the
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lowest RMSE, is most effective in minimizing these
large errors, which is crucial in maintaining
customer trust and satisfaction. By reducing the
likelihood of dramatic pricing errors, businesses
can avoid scenarios where customers might
abandon their shopping carts or seek alternative
sellers, thus improving retention and conversion
rates.
Table 2: Root Mean Square Error (RMSE)
Model
RMSE
Linear Regression
0.198
Decision Trees
0.185
Random Forest
0.160
SVM
0.178
Neural Networks
0.155
Table 2 demonstrates that the Root Mean Square
Error (RMSE) for various models highlights their
effectiveness in managing pricing errors in e-
commerce. Neural Networks achieved the lowest
RMSE of 0.155, indicating its superior ability to
minimize large pricing errors, which is crucial in
preventing lost sales and maintaining customer
loyalty. Random Forest also performed well with
an RMSE of 0.160, suggesting its reliability in
keeping pricing strategies aligned with customer
expectations and reducing the risk of significant
misalignment. In contrast, Linear Regression
recorded the highest RMSE of 0.198, showing its
difficulty in managing larger deviations in pricing,
which could lead to increased customer
dissatisfaction due to substantial errors in price
R-squared (R²)
Explaining Variance in Customer Satisfaction: R²
values indicate how well the pricing model
accounts for the factors that influence customer
satisfaction. A higher R², such as the one achieved
by the Neural Networks model, shows that the
model
effectively
captures
the
complex
relationship between price and customer
satisfaction. This capability is essential for
developing pricing strategies that consider various
factors, such as customer demographics,
purchasing history, and market trends. By
understanding these factors, businesses can set
prices that are more likely to meet customer
expectations, leading to higher satisfaction levels.
For example, if the model identifies that customers
in a particular segment are more price-sensitive, it
can suggest lower prices for that group to maintain
satisfaction and encourage repeat purchases.
Table 3: R-squared (R²)
Model
R-squared (R²)
Linear Regression
0.72
Decision Trees
0.78
Random Forest
0.82
SVM
0.79
Neural Networks
0.84
Table 3 illustrates that R-squared (R²) values
reveal how well different models explain the
variance in customer satisfaction based on pricing
strategies. Neural Networks achieved the highest
R² value of 0.84, demonstrating its strong
capability to capture the factors influencing
customer satisfaction, which implies that it can
develop pricing strategies more closely aligned
with customer expectations, leading to improved
satisfaction. Random Forest also performed well
with an R² of 0.82, showing its effectiveness in
explaining the relationship between pricing and
satisfaction, making it a valuable tool for
optimizing pricing strategies. In contrast, Linear
Regression had the lowest R² value of 0.72,
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suggesting it may not adequately capture the
complexities of customer satisfaction, potentially
resulting in less effective pricing strategies that do
not fully meet customer expectations.
F1-Score
Balancing Precision and Recall in Satisfaction
Prediction: The F1-score is particularly important
when the goal is to accurately predict whether
customers will be satisfied with a given price. A
high F1-score, as demonstrated by the Neural
Networks model, indicates that the model can
accurately predict customer satisfaction without
missing out on potential satisfied customers or
incorrectly predicting dissatisfaction. This balance
is crucial for e-commerce businesses that deal with
diverse customer bases and varying levels of price
sensitivity. By correctly identifying when a price
will lead to satisfaction, businesses can fine-tune
their pricing strategies to cater to different
customer needs, ensuring that prices are both
competitive and acceptable to customers across
different segments.
Table 4: F1-Score
Model
F1-Score
Linear Regression
NA
Decision Trees
0.81
Random Forest
0.86
SVM
0.83
Neural Networks
0.88
Table 4 highlights the F1-scores of various models,
emphasizing their effectiveness in binary
classification tasks like predicting customer
satisfaction based on pricing strategies. Neural
Networks achieved the highest F1-score of 0.88,
showcasing its excellent performance in balancing
precision and recall, thus accurately predicting
customer
satisfaction
and
minimizing
misclassification risks. Random Forest also
demonstrated strong performance with an F1-
score of 0.86, making it a reliable model for
scenarios where balancing precision and recall is
crucial for evaluating pricing strategies. In
contrast, Linear Regression does not have an F1-
score since it is primarily used for regression tasks
rather than classification, indicating it may not be
suitable for predicting binary outcomes such as
customer satisfaction with pricing.
Model Comparison and Selection
The combined bar chart presents the performance
metrics of different machine learning models,
including Linear Regression, Decision Trees,
Random Forest, SVM, and Neural Networks. The
metrics shown are Mean Absolute Error (MAE),
Root Mean Square Error (RMSE), R-squared (R²),
and F1-Score. Each metric is crucial for evaluating
the models' effectiveness in predicting customer
satisfaction based on pricing strategies. MAE and
RMSE measure prediction accuracy and error
magnitude, R-squared indicates how well the
models explain the variance in satisfaction, and the
F1-Score reflects their ability to balance precision
and recall in classification tasks. This chart enables
a comparative analysis to identify which models
excel in various aspects of performance.
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Choosing the Right Model for Dynamic Pricing: The
results clearly show that the Neural Networks
model is the most effective in predicting customer
satisfaction based on pricing strategies. This
model's ability to outperform others across all
metrics suggests that it can serve as a robust tool
for setting prices that are closely aligned with
customer expectations. However, the complexity
and computational demands of Neural Networks
might be a consideration for some businesses,
particularly those that require more interpretable
models or have limited resources.
Random Forest as a Balanced Alternative: The
Random Forest model, while slightly less precise
than Neural Networks, offers a good balance
between performance and interpretability. It could
be particularly useful in scenarios where
businesses need to explain pricing decisions to
stakeholders or when model transparency is
essential. This model’s strong performance across
MAE, RMSE, R², and F1-score also makes it a
reliable choice for dynamic pricing, ensuring that
prices are both competitive and customer-centric.
By leveraging the predictive power of these
machine learning models, e-commerce businesses
can set prices that not only reflect market
conditions but also resonate with customer
expectations. The ability to predict the right price
based on customer satisfaction enables businesses
to enhance customer loyalty, reduce churn, and
ultimately improve profitability. Whether choosing
a highly accurate but complex model like Neural
Networks or a more balanced option like Random
Forest, the key is to align pricing strategies with the
insights gained from these models, ensuring that
prices are fair, competitive, and customer focused.
DISCUSSION AND CONCLUSION
Discussion
The findings from this study underscore the critical
role of advanced machine learning techniques in
optimizing e-commerce pricing strategies. By
evaluating five prominent models
—
Linear
Regression, Decision Trees, Random Forest,
Support Vector Machines (SVM), and Neural
Networks
—
this research provides valuable
insights into how businesses can leverage these
technologies to enhance customer satisfaction and
drive competitive advantage.
The Neural Networks model emerged as the top
performer across all evaluated metrics, including
Mean Absolute Error (MAE), Root Mean Square
Error (RMSE), R-squared (R²), and F1-Score. Its
superior ability to capture complex, non-linear
relationships within the data enables it to deliver
highly accurate predictions, effectively aligning
pricing strategies with customer satisfaction. This
model’s proficiency in managing large datasets and
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identifying intricate patterns makes it particularly
advantageous for businesses seeking to fine-tune
their pricing approaches. However, the high
computational cost and complexity associated with
Neural Networks may limit its practical application
for smaller businesses or those with limited
resources.
In contrast, the Random Forest model, while not
surpassing Neural Networks in accuracy, provides
a commendable balance between performance and
interpretability. Its ensemble approach and
robustness against overfitting make it a practical
choice for dynamic pricing scenarios where both
accuracy and model transparency are essential.
The Random Forest model’s ability to handle a
variety of data types and deliver reliable
predictions without extensive computational
demands positions it as a viable alternative for
businesses that prioritize a blend of accuracy and
ease of understanding.
Linear Regression, Decision Trees, and SVMs,
though less effective than Neural Networks and
Random Forest, still offer valuable insights. Linear
Regression, with its simplicity, may serve as a
starting point for businesses with less complex
needs
or
those
seeking
straightforward
interpretability. Decision Trees, while providing
clear decision rules, may be limited by their
tendency to overfit. SVMs, known for their
effectiveness in classification tasks, demonstrated
moderate performance in this study but may
require further refinement for optimal pricing
predictions.
The application of these models in real-world e-
commerce scenarios highlights the importance of
selecting a model that aligns with specific business
requirements. Businesses should consider factors
such as the complexity of their pricing strategies,
computational resources, and the need for model
interpretability when choosing the most
appropriate
machine
learning
approach.
Integrating these advanced models into pricing
decisions enables businesses to better align their
strategies with customer expectations, ultimately
enhancing satisfaction, loyalty, and profitability.
CONCLUSION
This study provides a comprehensive evaluation of
machine learning models for optimizing e-
commerce pricing strategies, with a focus on
improving customer satisfaction. The results
indicate that Neural Networks offer the highest
level of predictive accuracy and performance,
making them an ideal choice for businesses aiming
to leverage sophisticated data-driven pricing
approaches. Despite their advantages, the
complexity and resource requirements of Neural
Networks may necessitate consideration of more
accessible alternatives such as Random Forest,
which provides a balanced performance with
reasonable interpretability.
The insights gained from this research emphasize
the transformative potential of machine learning in
e-commerce pricing. By harnessing these
technologies, businesses can set prices that reflect
market conditions and customer preferences more
effectively, leading to enhanced satisfaction and
competitive advantage. Future research could
explore hybrid models or innovative techniques to
further refine pricing strategies, particularly in
addressing the limitations observed in simpler
models.
Ultimately, the integration of machine learning into
pricing strategies represents a significant
advancement for e-commerce businesses. The
ability to predict and adjust pricing based on
detailed data analysis allows for more precise and
customer-centric decisions, fostering greater
satisfaction and driving long-term success in a
competitive marketplace.
Acknowledgement:
All the author contributed
equally
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