Reasearch on Cross-National E-commerce User Behavior Analysis and Conversion Rate Improvement Based on the Improved XLSTM Algorithm

Traditional methods of user behavior analysis have relied primarily on descriptive and inferential statistical techniques to derive insights from user data. These methods were commonly used in the earlier days of e-commerce when the focus was on basic demographic analysis and activity tracking.

Descriptive Analytics: Descriptive analytics refers to the process of summarizing and interpreting historical data to gain insights into user behavior. Common techniques include calculating basic metrics such as the average time spent on a website, the number of page views, bounce rates, and cart abandonment rates. While useful, descriptive analytics provides limited insights into the future behavior of users, as it only describes past events.

Segmentation and Clustering: Another traditional method involves segmenting users into groups based on predefined characteristics such as age, gender, or location. This segmentation helps businesses tailor their marketing and promotional efforts to different user groups. Techniques like K-means clustering or hierarchical clustering are often used to identify patterns in user data[9], allowing businesses to categorize users based on shared behaviors[10].However, these techniques can be oversimplified, often ignoring dynamic factors like seasonality or changing user preferences over time.

Conversion Funnel Analysis: The conversion funnel is a model that maps out the typical stages a user goes through before completing a desired action, such as making a purchase. Funnel analysis helps businesses understand where users drop off in the purchase process and identify potential bottlenecks. By analyzing these drop-off points, businesses can make changes to the website or marketing strategies to improve conversion rates. While effective, this method is limited in that it focuses on a predefined path and doesn't adapt to the complexities of individual user journeys[11].

Survey and Feedback Collection: Collecting direct feedback from users via surveys, reviews, or customer satisfaction ratings has long been a staple of traditional behavior analysis. These methods are straightforward and help businesses understand the subjective motivations behind user actions[12]. However, they suffer from low response rates and potential biases, which can limit the reliability of the insights gained[13].

While traditional methods like segmentation and funnel analysis have been valuable, they lack the ability to account for the complexity and dynamic nature of modern user behavior, particularly in the context of cross-national e-commerce.

Machine learning-based methods offer a significant advancement over traditional techniques by enabling the analysis of large volumes of data with greater precision. These methods can identify complex patterns and predict future user behavior, which is crucial for improving user engagement and conversion rates in e-commerce platforms.

Supervised Learning: In supervised learning, algorithms are trained on labeled data to predict outcomes based on input features[14]. For example, customer behavior data such as age, location, time spent on the website, and past purchase history can be used to predict whether a user is likely to complete a purchase or abandon the cart[15]. Common supervised learning techniques include decision trees, random forests, support vector machines (SVM)[16], and logistic regression. These methods can be highly effective at classification tasks, such as predicting user conversion rates or segmenting users into likely buyers and non-buyers. However, these models rely heavily on the quality and relevance of the labeled data and can be limited in capturing temporal dynamics[17].

Unsupervised Learning: Unsupervised learning, in contrast, is used when the data is unlabeled, and the goal is to identify hidden patterns without predefined outcomes[18]. Clustering algorithms, such as K-means or DBSCAN, can be used to group users based on similarities in their behavior, such as their browsing patterns or purchase histories. Principal Component Analysis (PCA) is also used to reduce the dimensionality of complex data and identify key features that explain user behavior. These methods allow businesses to segment customers in a more granular and dynamic way, but they require careful validation and interpretation of the results[19].

Reinforcement Learning: Reinforcement learning (RL)[20] is a subset of machine learning where algorithms learn to make sequences of decisions by interacting with the environment and receiving feedback in the form of rewards or penalties. In the context of user behavior analysis, RL can be used to optimize e-commerce websites by dynamically adjusting content or promotional offers based on user interactions. For example, RL can help determine the best time to present a discount to a user or which product to recommend based on real-time feedback. While RL has shown great promise in personalizing user experiences, it requires large amounts of data and computational resources, making it more complex to implement.

Deep Learning: Deep learning, particularly Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM) networks, has become one of the most powerful methods for analyzing sequential data. These models are particularly well-suited for predicting user behavior over time, such as predicting which users are likely to make repeat purchases or abandon a shopping cart based on their past actions. XLSTM (Extended LSTM), an improved version of LSTM, builds on this by handling more complex relationships within user data, such as multi-dimensional features across different time periods or locations. XLSTM models can also capture long-term dependencies in user behavior, which is a significant improvement over traditional methods.

Machine learning-based methods have proven to be highly effective in analyzing and predicting user behavior, offering more sophisticated and dynamic insights compared to traditional techniques. By leveraging these advanced algorithms, e-commerce businesses can improve user engagement, optimize personalized recommendations, and ultimately drive higher conversion rates.

Personalization is a cornerstone of modern e-commerce. By analyzing user behavior, businesses can tailor product recommendations, content, and marketing messages to individual users. Personalized experiences increase the likelihood of conversions by offering users products they are more likely to be interested in. For example, Amazon's recommendation engine analyzes past user interactions to suggest relevant products, which has been shown to increase purchase frequency.

Machine learning algorithms, particularly collaborative filtering and content-based filtering, are widely used to personalize product recommendations. These algorithms analyze user behavior and preferences to predict what products users might be interested in. The use of dynamic pricing, personalized discounts, and promotions based on user segmentation further enhances the personalized shopping experience and can boost conversion rates.

Understanding the user's journey through the website is critical for improving conversion rates. This includes analyzing the flow of user interactions, from landing pages to checkout. A seamless and intuitive user experience can significantly reduce friction points, such as long checkout forms or complicated navigation, that may cause users to abandon their purchase[22].

A/B testing is one common method used to optimize user journeys. By testing different versions of a webpage, businesses can identify which elements (e.g., call-to-action buttons, images, or text) perform best in terms of driving conversions. In combination with machine learning-based behavior analysis, A/B testing can help businesses optimize the entire user experience and drive higher conversion rates[23].

Cart abandonment is a major issue for e-commerce businesses, with research showing that nearly 70% of online shoppers abandon their shopping carts before completing a purchase. To address this, businesses can implement strategies such as retargeting ads, email remarketing, and offering time-sensitive discounts to encourage users to return and complete their purchase.

Machine learning models can play a key role in predicting which users are likely to abandon their carts based on their behavior patterns, allowing businesses to intervene proactively with targeted incentives or reminders.

LSTM networks are a specialized version of RNNs, designed to address the vanishing gradient problem that is common in traditional RNNs. The key feature of LSTM is its ability to learn long-term dependencies in sequential data. This makes LSTM well-suited for tasks such as time-series prediction, language modeling, and user behavior analysis in e-commerce.

LSTM networks consist of memory cells, which store information over time, and gates that regulate the flow of information into, out of, and within the memory cell. The core idea of LSTM is to maintain a "cell state" that carries relevant information across time steps.

An LSTM cell has three gates:

1) Forget Gate: This gate decides what information should be discarded from the cell state. It looks at the previous hidden state h_t–1 and the current input x_t , and outputs a value between 0 and 1 to indicate which parts of the previous memory should be forgotten. (1) ft=σ(Wf[ ht−1,xt ]+bf) ${{f}_{t}}=\sigma \left( {{W}_{f}}\left[ {{h}_{t-1}},{{x}_{t}} \right]+{{b}_{f}} \right)$

Input Gate: This gate controls how much of the new information should be added to the cell state. It decides the values of the new memory and updates the cell state accordingly. (2) it=σ(Wi[ ht−1,xt ]+bi) ${{i}_{t}}=\sigma \left( {{W}_{i}}\left[ {{h}_{t-1}},{{x}_{t}} \right]+{{b}_{i}} \right)$ (3) C˜t=tanh(WC[ ht−1,xt ]+bC) ${{\tilde{C}}_{t}}=\text{tanh}\left( {{W}_{C}}\left[ {{h}_{t-1}},{{x}_{t}} \right]+{{b}_{C}} \right)$

While LSTM is powerful, it is not always sufficient for handling the complex, multi-dimensional data encountered in real-world e-commerce applications. The XLSTM (Extended Long Short-Term Memory) algorithm introduces several innovations to enhance LSTM’s capabilities, particularly for cross-national e-commerce user behavior analysis. These innovations focus on better handling multi-dimensional data, capturing complex feature interdependencies, and improving memory retention over long sequences.

Key Innovations in XLSTM:

1) Enhanced Forget Gate: XLSTM expands the forget gate to process additional features beyond the typical user behavior data (such as product type, user location, etc.). This allows the model to capture richer, multi-dimensional data dependencies. (7) ft=σ(Wf[ ht−1,xt,zt ]+bf) ${{f}_{t}}=\sigma \left( {{W}_{f}}\left[ {{h}_{t-1}},{{x}_{t}},{{z}_{t}} \right]+{{b}_{f}} \right)$

The XLSTM algorithm offers several key advantages that make it particularly well-suited for analyzing complex user behavior in e-commerce contexts. Firstly, XLSTM excels in handling multi-dimensional data by processing diverse feature types—such as product attributes, user demographics, and temporal information—which enables it to capture the intricate relationships within user behavior more effectively. Secondly, the incorporation of a multi-dimensional cell state and enhanced memory mechanisms allows XLSTM to retain and leverage critical information over extended time periods, thereby improving its ability to identify and analyze long-term trends. Finally, XLSTM's flexibility in processing a wide range of data formats, including numerical, categorical, and sequential data, makes it highly adaptable to the varied and complex datasets typically encountered in e-commerce platforms. These strengths collectively enhance XLSTM's predictive accuracy and make it an ideal model for user behavior analysis in dynamic, data-rich environments.

Figure 1.

XLSTM Network Architecture

These innovations make XLSTM a powerful tool for e-commerce applications, where the goal is to analyze user behavior patterns across diverse product categories, geographic regions, and time periods. XLSTM’s ability to consider multiple features and long-term dependencies allows for more accurate predictions, ultimately contributing to better user experience and conversion optimization.

Visit Timestamps: The timestamp of each user interaction, including clicks, purchases, and adding items to the cart. This allows the model to capture temporal patterns such as peak shopping hours, weekend or holiday spikes in activity.

User Activity Frequency: The frequency of visits per user per day, week, or month. Frequent visitors are often more likely to make a purchase, so analyzing the frequency of visits helps in identifying highly engaged users.

The type of device and platform (mobile, desktop, tablet) used by the user is essential for understanding behavior patterns. Mobile users, for example, might browse but make fewer purchases compared to desktop users due to different browsing habits.

Device Type: Mobile, tablet, desktop.

Operating System and Browser: Which OS and browser were used (e.g., Android vs. iOS, Chrome vs. Safari).

Purchase History: Analysis of past purchases, including product categories, pricing, and frequency. Users who have a history of purchases are more likely to buy again.Browsing History: Items that were viewed or added to the cart but not purchased. This helps identify interests and potential areas for targeted marketing.

Product Ratings and Reviews: Positive ratings and reviews significantly impact purchasing decisions, while negative feedback may discourage purchases.

Social Sharing: Users who share product links on social media platforms such as Instagram and Facebook are likely to have higher brand loyalty and a stronger likelihood of purchasing.

Figure 2-3.

Sample Data Distribution by Product Category and the impact of Product Rating on Conversion Rate