Purchasing Conversion Rate Research Articles

Research on recommended model of personalized interior environment

Personalized recommendation systems are a common feature of e-commerce, streaming services, and other fields. However, they are not common in the field of interior environment design. Existing international research tends to prioritize deep learning-based recommendation algorithms. These methods rely heavily on large-scale data and complex models to improve the accuracy of recommendations. However, they often face significant limitations. Therefore, this paper proposes a personalized recommendation model for interior environment design based on clustering collaborative filtering algorithm and user portrait. The model improves the traditional collaborative filtering recommendation algorithm. Based on the established UPICF recommendation algorithm, the constructed model is verified through performance analysis and practical application test. The experimental results show that when the number of neighbors is 20, the MAE of the model is 0.7035, which has better personalized recommendation performance. In addition, in the personalized recommendation application of interior environment design, the click-through rate of the model is increased by 0.115, and the purchase conversion rate of users is increased by about 0.270, which further proves the effectiveness of the model. The results show that this model can provide more accurate, diversified and personalized indoor environment design solutions to meet the individual needs of users.

Consumer Segmentation and Decision: Explainable Machine Learning Insights

ABSTRACT The e-commerce market is experiencing a period of rapid growth, with online shopping becoming a prevalent consumer behavior. However, the challenge of identifying and segmenting high-value customer groups, and subsequently enhancing the conversion rate of purchases made on the shop’s platform, has remained a significant obstacle for merchants engaged in e-commerce. This study presents a novel approach that combines consumer involvement theory, quality signaling theory and consumer heterogeneity theory to develop a multi-algorithmic, interpretable machine learning model based on shapely additive explanations (SHAP). The results revealed the existence of four distinct consumer groups: the comprehensively involved, interactive, reading-oriented, and low-involvement groups. Comprehensively involved and interactive consumers have the highest purchase conversion rate and should be given priority attention. This study addresses the limitations of single theoretical perspective and “black box” problem of machine learning models for decision-making behavior, and can bring management insights for merchants to improve shop operation.

Improving Shopping Mall Revenue by Real-Time Customized Digital Coupon Issuance

With the development of big data and deep learning technology, big data and deep learning technology have also been applied to the marketing field, which was a part of business administration. Customer churn management is one of the most important areas of marketing. In this paper, we proposed a method to prevent customer churn and increase purchase conversion rate by issuing customized discount coupons to customers with high churn rate based on big data in real time. After segmenting customer segments with two-dimensional segment analysis, a real-time churn rate estimation model based on clickstream data was generated for each segment. After that, we issued customized coupons to our customers. Finally, we tested the conversion rate and sales growth. A two-dimensional cluster analysis-based churn rate estimation combined with a recommendation system was found to be significantly more useful than the respective simple models. Using this proposed model, it is possible to increase sales by automatically estimating the customer’s churn probability and shopping propensity without the burden of marketing costs in the online shopping mall. Keyword: Customer churn prediction, deep learning, digital marketing, ecommerce, recommendation system.

Cross-border E-commerce Business Data Processing in the Background of Digital Economy

Abstract This paper analyzes the cross-border e-commerce industry chain, and explores its customer value and impact on marketing strategy through data mining techniques.The whole process data mining methodology of data cleaning, integration, selection, transformation, mining, pattern evaluation and knowledge representation is adopted to deal with the cross-border e-commerce data.This paper focuses on the analysis of the customer value, the construction of the user profile, and the user behavioral patterns based on RFM model.The results show that the customer value is mainly determined by the customer life cycle and the purchase rate. The results show that the customer life cycle and purchase rate mainly determines the customer value of cross-border e-commerce.In the construction of user profiles, the characteristics and behavioral patterns of different users can be effectively identified through data standardization and the construction of user attribute models.The RFM model reveals the different levels of the user’s activity level, consumption ability and consumption value, which provides the basis for formulating differentiated marketing strategies.The data show that among the 4,130 user samples, the user behaviors and behavioral patterns were identified through clustering and knowledge representation. In the sample of 4130 users, cluster analysis shows that most of the users (72.64%) are ordinary customers. In contrast, the proportion of high-value customers (VIP customers) is only 0.24%, which suggests that cross-border e-commerce platforms need to formulate corresponding service and marketing strategies for different user groups to improve customer satisfaction and purchase conversion rate. At the same time, the cross-border e-commerce industry is experiencing changes such as platform fission, marketing innovation, and sinking of service targets, which poses new challenges and opportunities for e-commerce platforms’ operation and management.

Predicting online customer purchase: The integration of customer characteristics and browsing patterns

Despite the relentless growth of online retail, e-commerce platforms still suffer from a low purchase conversion rate. Researchers and practitioners have attempted to understand customer purchase behavior, but it remains elusive due to customers' heterogeneous and complex decision-making processes. This study addresses a gap in existing research by combining two types of factors affecting purchase behavior: past customer characteristics and current website browsing behavior. This study employs recency, frequency, and monetary value (RFM) to extract variables of customer characteristics and utilizes graph metrics to comprehensively measure browsing patterns. Based on these variables, this study conducts predictive analysis for purchase behavior and clustering analysis for session-level customer segmentation. Our findings reveal that integrating customer characteristics and browsing patterns significantly enhances purchase prediction and proposes a novel customer segmentation. This study not only provides theoretical and methodological contributions to decision support and e-commerce literature but also offers practical insights for real-time personalized marketing strategies.

Which Influencers Can Maximize PCR of E-Commerce?

The Web has provided an increasing proportion of use as a medium for e-commerce in addition to various recommender systems. It can be used for analyzing recommendation system-based feedback (e.g., a form in which a user inputs their preferences for various items as numerical values into a specific evaluation system) to estimate customer interest; in addition, analyzing multi-modal types of feedback (e.g., product purchase traces, inquiry lists, inquiry times, and comments) with deep learning can also be used to estimate user interest. As many companies around the world promote their products through micro-influencers on the Web, related research has continued to predict the purchase conversion rate of the influencer through a variety of technologies. In this work, we present a multi-modal micro-influencer analysis scheme for a marketing maximization strategy. Our scheme uses the multi-modal data stored in Mecha Solution’s own shopping mall of Korea, as well as famous Korean Internet platforms, and Coupang, Naver, and Oliveyoung’s data such as article posting comments and statistics information. By extracting the main characteristics of the real article postings from real users as opposed to those from factitious influencers posting articles and comments and identifying articles other than advertisements, influencer scores are obtained, assuming that articles other than advertisements can further increase the purchase conversion rate. Based on influencer score, we propose a multi-modal micro-influencer analysis scheme that recommends influencers use content-based collaborative filtering and user-based collaborative filtering for items that the influencer has not yet reviewed. The experiment was implemented to prove that the proposed scheme successfully achieves this goal.

Improving Shopping Mall Revenue by Real-Time Customized Digital Coupon Issuance

With the development of big data and deep learning technology, big data and deep learning technology have also been applied to the marketing field, which was a part of business administration. Customer churn management is one of the most important areas of marketing. In this paper, we proposed a method to prevent customer churn and increase purchase conversion rate by issuing customized discount coupons to customers with high churn rate based on big data in real time. After segmenting customer segments with two-dimensional segment analysis, a real-time churn rate estimation model based on clickstream data was generated for each segment. After that, we issued customized coupons to our customers. Finally, we tested the conversion rate and sales growth. A two-dimensional cluster analysis-based churn rate estimation combined with a recommendation system was found to be significantly more useful than the respective simple models. Using this proposed model, it is possible to increase sales by automatically estimating the customer’s churn probability and shopping propensity without the burden of marketing costs in the online shopping mall.

User Consumption Behavior Recognition Based on SMOTE and Improved AdaBoost

The sudden outbreak of COVID-19 has dealt a huge blow to traditional education and training companies. Institutions use the WeChat platform to attract users, but how to identify high-quality users has always been a difficult point for enterprises. In this paper, researchers proposed a classification algorithm based on SMOTE and the improved AdaBoost, which fuses feature information weights and sample weights to effectively solve the problems of overfitting and sample imbalance. To justify the study, it was compared with other traditional machine-learning algorithms. The accuracy and recall of the model increased by 19% and 36%, respectively, and the AUC value reached 0.98, indicating that the model could effectively identify the user's purchase intention. The proposed algorithm also ensures that it works well in spam identification and fraud detection. This research is of great significance for educational institutions to identify high-quality users of the WeChat platform and increase purchase conversion rate.

Poverty/investment slow distribution effect analysis based on Hopfield neural network

With the rapid development of the Internet platform, the Internet economy has become a new situation for evaluating the poverty/investment slow effect. What has brought development to the Internet economy is a variety of e-commerce platforms. However, in the face of huge Internet users and different users’ shopping habits, reasonably personalized e-commerce platform recommendations for users can improve the user’s website shopping experience. However, due to the huge user data and the diversity of e-commerce platforms, it is a huge challenge to reasonably recommend e-commerce platforms to users. The quality of the e-commerce’s personalized recommendations also affects the purchase conversion rate. Clustering algorithm is an algorithm involved in grouping data in machine learning. The same set of data has the same attributes and characteristics, and the attributes or features between different sets of data will be relatively large. In this paper, by using the mean shift clustering algorithm to characterize the behavior data. According to the characteristics of the grouping, we can recommend the e-commerce platform commonly used between the groups. However, using mean shift clustering for personalized recommendation faces the problem of too high user data dimensions. Therefore, we first conduct behavioral analysis of user data by leveraging the well-known Hopfield Neural Network. We define user behavior sequences for user behavior data and classify user behavior. We transform the grouped user behavior into an embedded vector, and linearly transform the embedded vectors of different lengths into the same semantic space. We process the vectors in the semantic space through the self-attention layer and perform mean shift clustering. Experiments show that our method can reduce the complexity of user poverty distribution effect toward complex data and improve the quality of personalized recommendation.

Portraying Footwear Customer Enter-Store Behavior in China: Based on Computer Vision Technology

Existing studies on customer behavior lack quantitative and high efficiency study, their technologies rely heavily on hardware. Therefore, the information of consumers in offline stores was insufficient, which made enterprises unable to accurately track consumers. However, computer vision (CV) is an expert in identifying and tracking people’s behavior, and its function is suitable for investigating enter-store customer behavior. Therefore, the aim of our study was to develop an offline consumer behavior portraying system based on CV. Then we used this system to investigate enter-store consumption behavior. We selected 71 shoe stores in China, then installed the system in store for a three-month data collection, and evaluated the impact of customer's age, gender, enter time, and region factors on enter-store behavior in China. Through our system, we successfully study ways to improve the purchase conversion rate of enter-store consumers, which could guide enterprises to adjust better marketing and operation strategies.

Resource allocation and scheduling in the intelligent edge computing context

With the rapid development of the edge computing, Internet economy has become a new situation in economic development. What has brought development to the Internet economy is a variety of family-based platforms (i.e., edge computing). However, in the face of huge Internet users and different users’ shopping habits, reasonably personalized e-commerce platform allocations for users can improve the user’s website shopping experience. However, due to the huge user data and the diversity of SDN (software defined network) platforms, it is a huge challenge to reasonably allocate e-commerce resources/platforms to users. The quality of the SDN personalized resource allocations also affects the purchase conversion rate. Clustering algorithm is an algorithm involved in grouping data in machine learning. The same set of data has the same attributes and characteristics, and the attributes or features between different sets of data will be relatively large. In this paper, by using the mean shift clustering algorithm to characterize the behavior data. According to the characteristics of the grouping, we can allocate the e-commerce platform commonly used between the groups. However, using mean shift clustering for personalized allocation faces the problem of too high user data dimensions. Therefore, we first conduct computational efficiency analysis toward each user. We define user behavior sequences for user behavior data and classify user behavior. We transform the grouped user behavior into an embedded vector, and linearly transform the embedded vectors of different lengths into the same semantic space. We process the vectors in the semantic space through the self-attention layer and perform mean shift clustering. Experiments show that, in the edge computing context, our method can reduce the complexity of resource allocation toward complex data and improve the quality of the allocated data.

Intelligent Recommendation System Based on Mathematical Modeling in Personalized Data Mining

With the advent of the era of big data, data mining has become one of the key technologies in the field of research and business. In order to improve the efficiency of data mining, this paper studies data mining based on the intelligent recommendation system. Firstly, this paper makes mathematical modeling of the intelligent recommendation system based on association rules. After analyzing the requirements of the intelligent recommendation system, Java 2 Platform, Enterprise Edition, technology is used to divide the system architecture into the presentation layer, business logic layer, and data layer. Recommendation module is divided into three substages: data representation, model learning, and recommendation engine. Then, the fuzzy clustering algorithm is used to optimize the system. After the system is built, the performance of the system is evaluated, and the evaluation indexes include accuracy, coverage, and response time. Finally, the system is put into a trial operation of an e-commerce platform. The click-through rate and purchase conversion rate of recommended products before and after the operation are compared, and a questionnaire survey is randomly launched to the platform users to analyze the user satisfaction. The experimental data show that the MAE of this system is the lowest, maintained at about 0.73, and its accuracy is the highest; before the recommended threshold exceeds 0.5, the average coverage rate of this system is the highest: 0.75; in Q1–Q5 subsets, the shortest response time of the system is 0.2 s. Before and after the operation of the system, the average click-through rate increased by 11.04%, and the average purchase rate increased by 9.35%. Among the 1216 users, 43% of the users were satisfied with 4 and 9% with 1. This shows that the system algorithm convergence speed is fast; it can recommend products more in line with user needs and interests and promote higher click-through rate and purchase rate, but user satisfaction can be further improved.

Prediction of Internet User’s Purchase Behavior Based on Mixed kernel SVM Model

In order to better predict the purchase behavior of online consumers and improve the purchase conversion rate of e-commerce companies, a model of consumer purchase intention prediction based on mixed kernel function support vector machine is proposed. In the research, the prediction model of consumer purchase intention is designed as a two-classification problem, and the feature selection algorithm mRMR is used to obtain the ranking of features, seeking to obtain better or similar classification results when choosing fewer features. On its basis, the particle swarm optimization algorithm (PSO) is used to optimize the parameters of the model. Experiments show that, to a certain extent, the constructed mixed kernel function can effectively improve the classification effect of SVM.

"I Think You'll Like It"

Understanding the roles of social factors in online purchase behavior has been a long standing research problem. The recently emerging social e-commerce platforms leverage the stimulated word-of-mouth effect to promote the sales of items, which offers a peek into the complex interplay between the social influence and online purchasing behavior. In this paper, we investigate this problem on a full-scale purchase behavior dataset from one of the leading social e-commerce platforms, Beidian. Specifically, we conduct a comparison study between the social e-commerce and conventional e-commerce that are both integrated in Beidian to examine how social factors affect user's purchase behavior. We reveal that social e-commerce leads to a 3.09~10.37 times higher purchase conversion rate compared with the conventional settings, which indicates users make purchase with significantly fewer item explorations. Then, we propose and validate four primary mechanisms that contribute to the efficient purchase conversion: better matching, social enrichment, social proof and price sensitivity. Moreover, we identify several behavioral indicators that are able to measure the effect of these mechanisms, based on which we design an accurate predictive model (AUC=0.7738) for user's purchase decision. These results combine to shed light on how to understand and model the purchase behavior in social e-commerce.

Customer Purchase Intent Prediction Under Online Multi-Channel Promotion: A Feature-Combined Deep Learning Framework

The micro-level customer purchase intent in promotions is crucial for the overall purchase conversion rate of promotions. In the context of joint promotions on multiple online channels, customers can access and compare prices and services by navigating between channels to aid their purchase decision. The interactions between customer and promotion channels offer another angle to predict their purchase intent during promotions. In this paper, we propose a feature-combined deep learning framework, in which a full-connected long short-term networks (FC-LSTM) is used for modeling the interactions between customers and promotion channels, as well as the nonlinear sequence correlations and cumulative effects between customer's browsing behavior. To improve the performance of the prediction, the framework incorporates other features of customer profile including purchase history and demographics, integrating them into an end-to-end framework. We apply our method in a real prediction task for online multichannel promotion for concert tickets. Extensive experiments show that the proposed approach exhibits overall good performance compared with state-of-the-art methods on standard metrics such as precision, recall, f-measure, area under curve (AUC), and lift.

The Impact of Coupons on the Search-to-Purchase Funnel: Theory and Empirical Evidence

Firms use coupons to stimulate demand. Although couponing is popular in practice, limited research has examined the causal effects of coupons on visit, search, and purchase behaviors among heterogeneous customers. In this paper, we explore coupon effects using data from a randomized field experiment with an online retailer in which customers were divided into two heterogeneous customer segments (low value and high value) with two types of coupon discounts (base value and better value). We find couponing is effective in increasing revenue, primarily by attracting customers who purchase without coupon redemption, and the lift in revenue per customer is larger for the high-value segment. Using clickstream data of customer visit and search behavior, we find most of the revenue lift arises from a corresponding lift in the likelihood of visiting the website under couponing. Though the lift in visit likelihood is relatively homogeneous across customer segments under the base coupon, the high-value segment has a higher purchase conversion rate than the low-value segment, leading to an amplified revenue lift. We also find a deeper discount leads to higher redemption and purchase conversion for the high-value segment but does not change visit likelihood. Finally, most of the search behaviors are unchanged under couponing, suggesting the mix of customers brought in under couponing are similar to those who visit without receiving the coupon promotion.

M Commerce: Experiencing the Phygital Retail

Emergence and evolution of information technologies paved way for the online transactions, termed as e-commerce. E-commerce was soon rechristened as wired e-commerce due to the rapid rise of mobile phone and other handheld devices facilitating access to internet while on the go and this led to the coining of the term m-commerce, the wireless avatar of e-commerce. Term Mobile commerce or simply m-commerce has been attributed to the collection of location based commercial services that are delivered by various internet enabled handheld devices such as mobile phones, tablets and palmtop devices. Though, essentially, both-e-commerce and m-commerce- are similar as the transactions are electronic in nature and are facilitated by internet but the element of mobility is one big differentiator between the two. Now, it has become difficult for the people to imagine e-commerce without the mobility dimension appended to it. In today’s online business environment, m-commerce is growing as next stage of e-commerce. Further, M-commerce has opened up new vistas for marketing the products, for targeting the customers at multiple points, for offering customized services using location based features and for enabling enhanced shopping and service experiences. It also offers opportunities for services that are more efficient and more user-friendly. The marketers may also offer new approaches, apps, and in-store solutions for enhanced customer engagement after researching the needs, roles and use contexts of the customers. Marketers have already started talking about mobile conversion funnel besides desktop conversion funnel. It is quite interesting to note that in mature markets, shape of the funnel is different and has higher purchase conversion rate.

A Web Usage Mining for Modeling Buying Behavior at a Web Store using Network Analysis

Understanding visitors’ invisible behaviors and responding with appropriate answers are important issues in continually increasing online market. To promote online transactions, customers’ behavior should be predicted correctly to keep low purchase conversion rate. In this study, we suggest an approach based on the idea that customers’ sessions in a web store can be transformed into the structure of a graph, which are represented as density of a session based on a graph theory. Online users visit lots of sites and their activities include information acquisition and browsing. The history of these activities can be used to construct a relationship network among web sites. This study analyzes this visit history made by website visitors with graph theory. The density of a network refers to the differentiated degrees of relationship among objects. In this study, we dichotomize into “purchase” and “no purchase group” since predicting whether a customer will buy or not buy our products is an important issue in web stores. We collect data on sessions which are a sequence of page views or a period of sustained web browsing. We model the sessions on the basis of density of a graph, which resulted in DOS (Density of a Session). The performance of other predictors including DOS is compared to that of suggested method in this study. Predictors are TVT (Total Visit Time during a period of a visit), AVT (The Average Time per Page Viewed), TNC (Total Number of Clicks), TPP (Total Number of Product-Related Pages Viewed), and DOS (Density of a Session Based on Graph Analysis). The study found that all predictors except total visit time are useful to differentiate between “purchase” and “no purchase” group. And we conducted Logit Analysis to examine the performance of each purchase prediction method. The results from Logit Analysis show that DOS predicts purchase behavior better in comparison with other predictors. It means understanding customers’ sessions with respect to a graph structure is useful to predict whether a customer will buy or not buy products in a web store.