Predicting Purchase Proneness of Anonymous User in Mobile Commerce

In recent years, mobile commerce is developing rapidly because of the popularity of mobile devices. However, for the difficulty of the mobile device input, the users of the e-commerce websites usually don’t log on the website when they are browsing, which resulting in a situation that a large number of website visitors are anonymous users. In order to increase sales revenue and expand market share, an effective prediction of anonymous users’ purchases proneness is very helpful in providing targeted marketing strategy for website to induce anonymous users to purchase. In the past, customer segmentation was mainly analyzed and modeled by customers’ historical data. But the history data of anonymous users can’t be obtained on mobile commerce sites. This method is difficult to put into management practice. In order to solve this problem, this paper proposes a method based on random forest of using user clickstream data to forecast purchase proneness in real time. This method includes two stages: the model training part and the user purchasing proneness prediction part. In the model training part, a classifier based on random forest algorithm is trained. In the users\u27 predicting part, the classifier is used to predict the user\u27s purchase proneness in real time. The method proposed can be effectively applied in the real-time prediction of anonymous users\u27 purchasing proneness, and the results of prediction will help enterprises implement the marketing measures in real time

Bicluster Analysis of Cheng and Church's Algorithm to Identify Patterns of People's Welfare in Indonesia

Biclustering is a method of grouping numerical data where rows and columns are grouped simultaneously. The Cheng and Church (CC) algorithm is one of the bi-clustering algorithms that try to find the maximum bi-cluster with a high similarity value, called MSR (Mean Square Residue). The association of rows and columns is called a bi-cluster if the MSR is lower than a predetermined threshold value (delta). Detection of people's welfare in Indonesia using Bi-Clustering is essential to get an overview of the characteristics of people's interest in each province in Indonesia. Bi-Clustering using the CC algorithm requires a threshold value (delta) determined by finding the MSR value of the actual data. The threshold value (delta) must be smaller than the MSR of the actual data. This study's threshold values are 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, and 0.8. After evaluating the optimum delta by considering the MSR value and the bi-cluster formed, the optimum delta is obtained as 0.1, with the number of bi-cluster included as 4

Pattern Recognition of Food Security in Indonesia Using Biclustering Plaid Model

Biclustering come in various algorithms, selecting the most suitable biclustering algorithm can be a challenging task. The performance of algorithms can vary significantly depending on the specific data characteristics. The Plaid model is one of popular biclustering algorithms, has gained recognition for its efficiency and versatility across various applications, including food security. Indonesia deals with complex food security challenges. The nation's unique geographic and socioeconomic diversity demands region-specific food security solutions. Identifying province-specific food security patterns is crucial for effective policymaking and resource allocation, ultimately promoting food sufficiency and stability at the regional level. This study assesses the performance of the Plaid model in identifying food security patterns at the provincial level in Indonesia. To optimize biclusters, we explore various parameter tuning scenarios (the choice of model, the number of layers, and the threshold value for row and column releases). The selection criteria are based on the change ratio of the initial matrix's mean square residue to the mean square residue of the Plaid model, the average mean square residue, and the number of biclusters. The constant column model was selected with a mean square residue change ratio of 0.52, an average mean square plaid model residue of 4.81, and it generates 6 overlapping biclusters. The results show each bicluster has unique characteristics. Notably, Bicluster 1 that consist of 2 provinces, exhibits the lowest food security levels, marked by variables X1, X2, X4, and X7. Furthermore, the variables X1, X4, and X7 consistently appear across several biclusters. This highlights the importance of prioritizing these three variables to improve the food security status of the regions.

Painsight: An Extendable Opinion Mining Framework for Detecting Pain Points Based on Online Customer Reviews

As the e-commerce market continues to expand and online transactions proliferate, customer reviews have emerged as a critical element in shaping the purchasing decisions of prospective buyers. Previous studies have endeavored to identify key aspects of customer reviews through the development of sentiment analysis models and topic models. However, extracting specific dissatisfaction factors remains a challenging task. In this study, we delineate the pain point detection problem and propose Painsight, an unsupervised framework for automatically extracting distinct dissatisfaction factors from customer reviews without relying on ground truth labels. Painsight employs pre-trained language models to construct sentiment analysis and topic models, leveraging attribution scores derived from model gradients to extract dissatisfaction factors. Upon application of the proposed methodology to customer review data spanning five product categories, we successfully identified and categorized dissatisfaction factors within each group, as well as isolated factors for each type. Notably, Painsight outperformed benchmark methods, achieving substantial performance enhancements and exceptional results in human evaluations.Comment: WASSA at ACL 202

Detecting Pain Points from User-Generated Social Media Posts Using Machine Learning

Artificial intelligence, particularly machine learning, carries high potential to automatically detect customers’ pain points, which is a particular concern the customer expresses that the company can address. However, unstructured data scattered across social media make detection a nontrivial task. Thus, to help firms gain deeper insights into customers’ pain points, the authors experiment with and evaluate the performance of various machine learning models to automatically detect pain points and pain point types for enhanced customer insights. The data consist of 4.2 million user-generated tweets targeting 20 global brands from five separate industries. Among the models they train, neural networks show the best performance at overall pain point detection, with an accuracy of 85% (F1 score = .80). The best model for detecting five specific pain points was RoBERTa 100 samples using SYNONYM augmentation. This study adds another foundational building block of machine learning research in marketing academia through the application and comparative evaluation of machine learning models for natural language–based content identification and classification. In addition, the authors suggest that firms use pain point profiling, a technique for applying subclasses to the identified pain point messages to gain a deeper understanding of their customers’ concerns.©2022 SAGE Publications. The article is protected by copyright and reuse is restricted to non-commercial and no derivative uses. Users may also download and save a local copy of an article accessed in an institutional repository for the user's personal reference.fi=vertaisarvioitu|en=peerReviewed

Majutusteenuse arendamine P54 külaliskorterite näitel

https://www.ester.ee/record=b5260631*es

A magyar gazdasági felsőoktatás „boldogság térképe”

A GDP az elmúlt években számos kritikával illetett. A helyettesítésére kidolgozott mutatók közül komplex megközelítésével és egyedi gondolkodásmódjával került a kutatások középpontjába a GNH (Gross National Happiness). A szerzők tanulmányukban az eredeti (Bhutánban kidolgozott GNH of Business) kérdőíves kutatás logikáját és számítási módját mutatják be, a gazdasági felsőoktatás munkatársainak, vezetőinek véleményét feltárva. A kvantitatív kutatásba valamennyi hazai felsőoktatási intézmény gazdasági oktatással foglalkozó karát/intézetét bevonták. 239 munkavállalói és 14 vezetői kérdőívet értékelve az eredmények azt mutatják, hogy a gazdasági felsőoktatási intézmények boldogságindex-értéke az átlagos szint alatt marad. A munkatársi boldogság számított értéke lényegesen alacsonyabb a szervezeti feltételeket minősítő értékeknél. A legnagyobb hiányosságok az „Életszínvonal” területén tapasztalhatók, míg a többi terület alacsony, de kiegyensúlyozott módon minősíthető. A szervezeti feltételek tekintetében a „Kulturális és ökológiai sokszínűség” mutatói szorulnak komoly fejlesztésre

A magyar gazdasági felsőoktatás „boldogság térképe”

A GDP az elmúlt években számos kritikával illetett. A helyettesítésére kidolgozott mutatók közül komplex megközelítésével és egyedi gondolkodásmódjával került a kutatások középpontjába a GNH (Gross National Happiness). A szerzők tanulmányukban az eredeti (Bhutánban kidolgozott GNH of Business) kérdőíves kutatás logikáját és számítási módját mutatják be, a gazdasági felsőoktatás munkatársainak, vezetőinek véleményét feltárva. A kvantitatív kutatásba valamennyi hazai felsőoktatási intézmény gazdasági oktatással foglalkozó karát/intézetét bevonták. 239 munkavállalói és 14 vezetői kérdőívet értékelve az eredmények azt mutatják, hogy a gazdasági felsőoktatási intézmények boldogságindex-értéke az átlagos szint alatt marad. A munkatársi boldogság számított értéke lényegesen alacsonyabb a szervezeti feltételeket minősítő értékeknél. A legnagyobb hiányosságok az „Életszínvonal” területén tapasztalhatók, míg a többi terület alacsony, de kiegyensúlyozott módon minősíthető. A szervezeti feltételek tekintetében a „Kulturális és ökológiai sokszínűség” mutatói szorulnak komoly fejlesztésre

Spirituaalturismitoodete arendamine Energia talu näitel

https://www.ester.ee/record=b5166237*es