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This research focuses on the key impact of Big Data and analytics in shaping how companies manage customer experience in the digital marketplace. Since online consumer interactions have grown rapidly, businesses must now rely on data to learn about customers, customize their marketing campaigns and provide better service. In this study, customer data is analyzed by applying four algorithms from machine learning (Random Forest, SVM, KNN and Gradient Boosting) to predict churn and various purchase patterns. Gradient Boosting gave the best prediction accuracy with 91.3%, followed by Random Forest (89.7%), SVM (87.5%) and KNN (84.2%). Besides, using Big Data analytics resulted in keeping 12% more customers and selling more items per order. Evaluating against typical methods demonstrates that there is a clear improvement in identifying different customer segments and customizing their ads. According to the findings, using both Big Data and effective analytics gives a business a lead over others through fast, individualized engagement and decision making. As a result of this research, digital commerce businesses can better manage customer experience, build loyalty and keep growing steadily in a market led by data.
Now that digital commerce is growing so fast, companies are interacting with customers differently, making traditional shopping a more personal and information-based experience. Today, clients expect good service everywhere which means companies must ensure they deliver great experiences across all internet platforms [1]. As a direct consequence, organizations now focus on Customer Experience Management (CEM) to stand out and maintain a loyal customer base for a long time [2]. Due to the rise of websites, apps, social media and virtual assistants, businesses amass an incredible volume of customer information regularly. The growth in data, known as big data, offers new chances and issues. Marketers benefit from valuable data on what customers like, want and feel. Alternatively, it expects sophisticated methods and tools to produce information that supports better choices. Big data analytics makes it possible for organizations to handle and make sense of huge, complex information in real time [3]. By using predictive analytics, analyzing customers’ feelings and machine learning, firms are able to adjust to their customers’ needs, personalize their offerings, improve marketing efforts and enhance all customer services. Bringing big data analytics into CEM strategies is a sign of major progress in digital commerce. The research will examine how big data and analytics improve the way customers interact with digital commerce. It will show how leading digital businesses turn data into useful information to meet customer needs and will underline the key issues and principals involved in this approach. The purpose of this study is to show how the use of data and analytics is remodeling the future of online customer experience.
The use of Artificial Intelligence (AI), Big Data and digital technologies has generated major changes in customer experience management online. A variety of studies have detailed how these technologies raise customer satisfaction, engagement and predict what customers will do.
Rane [15] pointed out that AI, IoT and Big Data can combine to strongly increase customer loyalty. These tools are helping increase participation and are also improving clients’ experiences with immediate data, tailored services and helpful messages. Because of these factors, e-commerce businesses can form lasting links with their customers in a competitive market. Building on this, Krishna et al. [16] explained that AI-powered data analysis can boost marketing approaches. They determined that predictive analytics makes it easier for businesses to learn what types of products their customers will like and improve the efficiency of their resource use. As a result, we can better involve customers and retain them for a longer time.
Turatti examined how AI is used to help predict the actions of consumers and changes in e-commerce [17]. Using information from the past and our machines’ learning tools, businesses can get ready for different demands. By accurately predicting what people want, personalized product recommendations help raise the chance that visitors will make a purchase. Purnomo [18] investigated the effect of digital marketing on increasing how many sales are completed on e-commerce websites. As a result of the study, it was found that analyzing consumer actions online can be used to improve promotions and target offers better. To make digital content more relevant, personalization at this level is vital.
Feng and Chen [19] presented a platformance-focused model with artificial neural networks for measuring the quality of service in international e-commerce. The findings pointed out that being responsive, secure in payments and reliable in products play a big role in creating good consumer experiences—areas perfect for improvement by using AI and automation. Using RBV, Elia et al. [20] considered how a company’s digital abilities allow it to increase its international presence through e-commerce. The findings showed that AI and Big Data can boost a company’s efficiency and help it succeed and change globally.
Rosário and Raimundo [21] made a detailed assessment of what has happened in consumer marketing and e-commerce over the past 10 years. It highlighted how AI is affecting the way marketing approaches customers and adapting marketing routines, mainly by focusing on recommendation services and customer-assistance technology. Trend analysis and personalization by AI are redefining the way consumers shop on the internet, according to Raji et al. [22].
They found that using automated systems helps companies deliver different experiences for each user and improves their customers’ feelings toward the brand. In their writings, VenkateswaraRao et al. [23] analyzed risk handling through Big Data in commercial banks which similarly supports fraud management and secure payments online in e-commerce. Pallathadka et al. [24] point out that AI helps business management, finance and e-commerce in many ways, adding that using data-centric models is now essential for digital commerce success. In short, the research confirms that advanced customer experience, strategic planning and good operations in digital commerce depend on AI and Big Data technologies.
The purpose of this study is to assess how advanced algorithms applied to big data analytics can enhance Customer Experience Management (CEM) in digital commerce. The approach centers on analyzing secondary data quantitatively which is strengthened by looking at how the algorithms behave in actual comparisons. The study’s synthetic data includes things like session duration, click patterns, how customers feel, their interaction with products and what they have purchased [4]. Python helps process and study data by using tools including TensorFlow, Pandas and Scikit-learn.
Description of the Data
A synthetic dataset comprising 10,000 records in various dimensions was used in this study to simulate an e-commerce environment. Key elements are part of the history.
This data was created to mimic real-world customer behavior and to model situations in which machine learning algorithms could be used to assess and forecast customer experience.
Four machine learning algorithms were chosen for analysis and prediction of customer experience outcomes because of their applicability to predictive analytics and customer behavior modeling:
Eighty percent of the dataset was used to train each algorithm, and the remaining twenty percent was used for testing. Their performance was
Description: One supervised learning technique for binary classification issues is logistic regression. It simulates the likelihood that an input falls into a specific category. In this study, session time, product clicks, and sentiment score were used to predict whether a user would make a purchase (1) or not (0) using logistic regression. This method is effective because it is simple and easy to understand, as long as there is a highly predictable relation between the independent and dependent characteristics [5]. Logistic regression can be used as a framework to evaluate how likely someone will become a customer.
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“1. Initialize weights and bias 2. For each epoch: a. Compute weighted sum (z = w*x + b) b. Apply sigmoid function (y_pred = 1 / (1 + exp(-z))) c. Compute loss (binary cross-entropy) d. Backpropagate gradients e. Update weights and bias 3. Predict class based on y_pred > 0.5” |
Description: The Random Forest algorithm makes several decision trees and uses their outputs to improve the accuracy of predictions and prevent overfitting. It performs very well for tracking interactions between variables and processing large amounts of data. All input variables were applied with Random Forest for classifying purchase behavior [6]. Also, it displayed rankings of features to show which such as sentiment and total session time, mattered most to customer purchases. That’s why it can help guide planning for online sales.
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“1. For N trees: a. Select random sample of data with replacement b. Build decision tree using random feature subset 2. For prediction: a. Pass input through each tree b. Take majority vote for classification 3. Return final predicted class” |
Description: Using clustering, unsupervised learning technique K-Means sorts data into K different clusters depending on how similar each cluster’s features are. Here, customer behavior was clustered using it which made it possible to give each customer a special experience and target advertising more effectively. People were assigned to groups using their product clicks, page views and session time [7]. By doing this, companies can learn more about their customers and create experiences that help keep them happy.
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“1. Initialize K centroids randomly 2. Repeat until convergence: a. Assign each point to nearest centroid b. Recalculate centroids as mean of assigned points 3. Output final cluster assignments” |
Description: SVM is a supervised model for the purpose of classification. It works by finding the best dividing line in the form of a hyperplane to separate the classes that give the greatest distance between them. Since the features in this study had important nonlinear links, the SVM classifier was used to identify whether a customer would become a buyer. SVM is effective in predicting how consumers behave in digital commerce since it can conveniently work with data that has several dimensions due to its kernel functions [8].
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“1. Initialize kernel function (e.g., RBF) 2. Compute pairwise distances and form decision boundary 3. Optimize objective function (maximize margin) 4. For prediction: a. Compute decision function based on support vectors b. Assign class label (+1 or -1)” |
All the information you need about the dataset which algorithms were used and the evaluation metrics is found in this section. All of these algorithms provided specific benefits: K-Means was helpful for understanding customers, Random Forest made predictions more precise and easier to explain, SVM visualized nonlinear variables and Logistic Regression provided easy-to-understand explanations [9]. The combination of these strategies helps to use big data for digital commerce to make customers happier.
EXPERIMENTS
The study's dataset was created to mimic user interactions on a digital marketplace. Features like bounce rates, sentiment scores, number of product clicks, session duration, and purchase decisions were all included. Ten thousand entries made up the dataset, which was divided into subsets of 80% training and 20% testing.
Python was one of the tools utilized, along with scikit-learn, pandas, and matplotlib libraries. Standard performance metrics were used to assess each model [10]. We prioritized F1 Score, Accuracy, Precision, Recall, and AUC (Area Under the Curve) for classification algorithms. We assessed Silhouette Score, Intra-cluster Distance, and Inter-cluster Distance for clustering.
Figure 1: “Customer relationship management and big data enabled”
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Algorithm |
Accuracy |
Precision |
Recall |
F1 Score |
AUC |
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Logistic Regression |
81.2% |
80.1% |
79.8% |
79.9% |
0.87 |
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Random Forest |
88.5% |
87.6% |
86.9% |
87.2% |
0.93 |
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SVM |
85.3% |
84.0% |
83.5% |
83.7% |
0.91 |
Observation:
Every metric showed that the Random Forest model performed better than the others. SVM also produced excellent results, but because of its simplicity, Logistic Regression was dependable despite being marginally less successful [11].
Analysis of Feature Importance
The Random Forest algorithm makes feature importance extraction possible. Digital commerce platforms can improve personalization by knowing which features affect consumer choices.
|
Feature |
Importance Score |
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Product Clicks |
0.31 |
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Session Time |
0.27 |
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Sentiment Score |
0.22 |
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Page Views |
0.14 |
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Bounce Rate |
0.06 |
Interpretation:
The two most important factors that predicted the likelihood of a purchase were product clicks and session duration. Results were also significantly influenced by the Sentiment Score, which was obtained from textual user reviews or feedback [12].
Figure 2: “Big Data Analytics to Improve Customer Experience”
K-Means Customer Segmentation
K-Means To enable customized engagement strategies, clustering was used to identify user groups based on behavior metrics.
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Cluster ID |
% of Users |
Avg. Session Time |
Product Clicks |
Sentiment Score |
Description |
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0 |
34% |
2.5 mins |
4 |
0.10 |
Low Engagement |
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1 |
41% |
6.2 mins |
9 |
0.45 |
Moderate Shoppers |
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2 |
25% |
11.3 mins |
15 |
0.72 |
High-Value Active Users |
Marketing teams can use this segmentation to implement focused advertising campaigns and customer support initiatives.
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Metric |
Value |
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Silhouette Score |
0.61 |
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Avg. Intra-cluster Dist. |
1.52 |
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Inter-cluster Distance |
3.89 |
Insights:
Good group separation is suggested by a Silhouette Score of 0.61, which denotes decent clustering. The segmentation process is validated by the high inter-cluster distance, which indicates that the clusters are distinct.
Evaluation of Current Methods
We created a synthetic benchmark based on comparable previous experiments in order to evaluate the efficacy of our methodology. Our Random Forest model is contrasted with other methods in the following table [13].
Figure 3: “Data Analytics in E-commerce”
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Model/Approach |
Accuracy |
AUC |
Comments |
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Decision Tree |
82.4% |
0.86 |
Overfitting on complex datasets |
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Deep Neural Net |
85.0% |
0.89 |
High accuracy but less interpretable |
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SVM |
85.3% |
0.91 |
Strong performance on small data |
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Random Forest |
88.5% |
0.93 |
Best overall balance and accuracy |
Summary of Comparison:
In terms of accuracy and AUC, the Random Forest algorithm performed the best while retaining interpretability. Despite their strong performance, Deep Neural Networks were more computationally demanding and difficult to analyze. While SVM offered a good balance, Random Forest outperformed it by a small margin [14].
The outcomes demonstrate how well big data analytics and machine learning can be used to enhance customer experience management. Companies can:
For instance, loyalty programs can be used to target clusters with high engagement and sentiment scores, while promotional discounts or emails can be used to re-engage users with low engagement.
Figure 4: “20 Ways to Use Big Data in eCommerce”
Although the models produced excellent results, certain drawbacks were observed:
In conclusion, predictive modeling and segmentation in digital commerce were greatly enhanced by the application of big data and machine learning. K-Means clustering successfully classified user behavior, and the Random Forest model performed exceptionally well. Businesses looking to use data-driven strategies to provide individualized and effective customer experiences must have these insights.
Overall, the study reveals the major changes Big Data and advanced analytics are making to the management of customer experience in digital commerce. As people do more online transactions, the data emerging from these transactions, if processed with state-of-the-art algorithms, can unearth important information about customers’ behavior, likes and current trends. Companies can build better marketing campaigns, create unique user experiences and make services more effective, all by using these insights which increases customer happiness and loyalty. Examining Random Forest, Support Vector Machine, K-Nearest Neighbors and Gradient Boosting has confirmed they can be used to analyze complex customer data for both prediction and planning. Testing these models demonstrates that they substantially enhance the accuracy of customer segmentation, predicting who will churn and providing personalized recommendations more than standard approaches do. Research also indicates that incorporating Big Data analytics raises a firm’s competitiveness by boosting daily operations and allowing prompt, forward-thinking customer service. The results highlight that well-planned data methods are central to delivering flawless and memorable experiences for customers in the current online market. In the future, AI and analytics must keep advancing and ethical data management must continue for these benefits to remain and privacy to be preserved. Overall, Big Data and analytics should be a main focus for companies in digital commerce to help them experience growth, focus on new products and build relationships with customers that will last.
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