Market Segmentation Analysis and Visualization Using K-Mode Clustering Algorithm for E-Commerce Business

Today all business organizations are adopting data driven strategies to generate more revenue out of their business. Growing startups are investing a lot of money in data economy to maximize profits of business organizations by developing intelligent tools backed by machine learning and artificial intelligence. The nature of BI tool depends on factor like business goals, size, model, technology etc. In this paper architecture of business intelligence tool and decision process has been discussed with a focus on market segmentation, based on user behavior analysis using k-mode clustering algorithm and user geographical distributions. The proposed toolkit also incorporates interactive visualizations and maps

Segmentation Analysis of Students in X Course with RFM Model and Clustering

In the business world, the competition to maintain and obtain more customers has become tougher. The presence of new players entering the market is driven by the developments of internet and advertisement. The X guitar course is an institution engaged in the field of non-formal education services. The customers are the course student that has made the payment transaction. The map of customer segmentation is one of the most important components in finding the main needs of each customer. Know the main needs of each customer is expected to increase the customer’s loyalty. Customer segmentation can be done by using the clustering method through a data mining approach in the form of RFM (Recency, Frequency and Monetary) Models. Recency is the data of the last payment transaction date. Frequency shows the number of course payment transactions. Monetary comes from the nominal amount of the transaction. RFM data is combined with the Fuzzy Gustafson-Kessel and K-Means clustering method to produce output in the form of k-clusters of customer. The formed segment is expected to represent the need of customers that vary by using validation process with the Global Silhouette Index. The customer population of the course is 225 students. It has been concluded that the RFM score for each subject by using 3 FGK clusters is the optimum cluster model with the largest Silhouette Index, which is 0.523. This research is expected to provide an in-depth analysis of customer segmentation for X guitar course

Psychographic And Behavioral Segmentation Of Food Delivery Application Customers To Increase Intention To Use

Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Knowledge Management and Business IntelligenceThis study presents a framework for segmenting Food Delivery Application (FDA) customers based on psychographic and behavioral variables as an alternative to existing segmentation. Customer segments are proposed by applying clustering methods to primary data from an electronic survey. Psychographic and behavioral constructs are formulated as hypotheses based on existing literature, and then evaluated as segmentation variables regarding their discriminatory power for customer segmentation. Detected relevant variables are used in the application of clustering techniques to find adequate boundaries within customer groupings for segmentation purposes. Characterization of customer segments is performed and enriched with implications of findings in FDA marketing strategies. This paper contributes to theory by providing new findings on segmentation that are relevant for an online context. In addition, it contributes to practice by detailing implications of customer segments in an online sales strategy, allowing marketing managers and FDA businesses to capitalize knowledge in their conversion funnel designs

Editorial for Vol. 26 No. 1

This first issue of CIT\u27s Volume 26 (March 2018) brings one opinion paper and five regular papers, these latter from the broad areas of computer networks, image processing, cluster analysis as well as information retrieval

BIG DATA IN MARKETING & RETAILING

Data is increasingly being created, stored, analyzed, and applied. Big Data is becoming an everyday phrase that appears in popular media and people’s daily conversations. This paper provides a framework to define Big Data from technical and business perspectives, to present its enormous value in different fields, to share its applications in marketing and retailing, market segmentation, targeting and positioning as well in developing marketing mix. We also provide some real life industry examples, to shed light on the challenges in harnessing the potential of Big Data, and to discuss its future. Big Data will separate the winners from the losers in the business field in the future. The leading companies in the Big Data field, such as Google, Amazon, and Wal-Mart, will continue to build their competitive advantage, both in marketing and other areas, by acting on the insights developed from Big Data analysis

A data-driven approach to improve online consumer subscriptions by combining data visualization and machine learning methods

Effective online consumer research helps companies on defining a successful strategy to increase user loyalty and shape brand engagement. Digital innovation introduced a dramatic change in businesses, particularly in the online news industry. Content consumers have a wide offer across different channels which increases the digital challenge for online news media companies to retain their readers and convert them into online subscribers. Furthermore, digital news publishers often strive to balance revenue sources in online business models. Thus, this study fills a gap in the literature on media consumer research by proposing a data-driven approach that combines two machine learning models to allow managers dynamically improve their marketing and editorial strategies. Firstly, the authors present an online user profiling to identify consumer segments based on the interplay between several engagement’ variables substantiated in the literature research. Second, as few studies have explored the factors influencing users’ intention to pay for such services, the XGBoost machine learning algorithm identifies the predictors of consumer's willingness to pay. Third, a dashboard presents the key performance indicators across the audience funnel. Thus, practical implications and business suggestions are presented in a two-fold strategy to maximize revenue from digital subscriptions and advertising. Findings provide new insights into an engagement approach and the relation to acquire a digital subscription in online content platforms. We believe that the provided recommendations are potentially useful to help marketing and editorial teams to manage their customer engagement process across the funnel in a more efficient way.info:eu-repo/semantics/publishedVersio

Fuzzy Clustering Approach for Marketing Recycled Products of Tabriz Municipality Waste Management Organization

The main concern of municipalities is the realization of sustainable revenues. Organizations affiliated with municipalities should play a role in generating revenue by defining specialized tasks while assisting municipal tasks. Tabriz Municipality Waste Management Organization seeks to achieve this by defining its strategies and goals. The organization has implemented various projects to generate revenue from recycled products. Poor planning and failure to fully outsource are among the obstacles of this organization. Therefore, marketing of recycled products is an important project. Lack of careful planning in this regard, marketing costs and weakness of private sector investment projects are the most important obstacles facing the organization. This article has determined the degree of homogeneity of waste organization projects in the marketing of recycled products with a fuzzy clustering approach and according to the opinions of experts. The results show that some of the organization's projects lack value. Instead, some projects, such as the construction of a recycling town with a variety of recycled products, renewable energy recycling, and plastic recycling with a variety of products, have similar features in the product mix marketing element, and this can reduce marketing costs and Focus on such projects

Will the US Economy Recover in 2010? A Minimal Spanning Tree Study

We calculated the cross correlations between the half-hourly times series of the ten Dow Jones US economic sectors over the period February 2000 to August 2008, the two-year intervals 2002--2003, 2004--2005, 2008--2009, and also over 11 segments within the present financial crisis, to construct minimal spanning trees (MSTs) of the US economy at the sector level. In all MSTs, a core-fringe structure is found, with consumer goods, consumer services, and the industrials consistently making up the core, and basic materials, oil and gas, healthcare, telecommunications, and utilities residing predominantly on the fringe. More importantly, we find that the MSTs can be classified into two distinct, statistically robust, topologies: (i) star-like, with the industrials at the center, associated with low-volatility economic growth; and (ii) chain-like, associated with high-volatility economic crisis. Finally, we present statistical evidence, based on the emergence of a star-like MST in Sep 2009, and the MST staying robustly star-like throughout the Greek Debt Crisis, that the US economy is on track to a recovery.Comment: elsarticle class, includes amsmath.sty, graphicx.sty and url.sty. 68 pages, 16 figures, 8 tables. Abridged version of the manuscript presented at the Econophysics Colloquim 2010, incorporating reviewer comment

CHORUS Deliverable 2.2: Second report - identification of multi-disciplinary key issues for gap analysis toward EU multimedia search engines roadmap

After addressing the state-of-the-art during the first year of Chorus and establishing the existing landscape in multimedia search engines, we have identified and analyzed gaps within European research effort during our second year. In this period we focused on three directions, notably technological issues, user-centred issues and use-cases and socio- economic and legal aspects. These were assessed by two central studies: firstly, a concerted vision of functional breakdown of generic multimedia search engine, and secondly, a representative use-cases descriptions with the related discussion on requirement for technological challenges. Both studies have been carried out in cooperation and consultation with the community at large through EC concertation meetings (multimedia search engines cluster), several meetings with our Think-Tank, presentations in international conferences, and surveys addressed to EU projects coordinators as well as National initiatives coordinators. Based on the obtained feedback we identified two types of gaps, namely core technological gaps that involve research challenges, and “enablers”, which are not necessarily technical research challenges, but have impact on innovation progress. New socio-economic trends are presented as well as emerging legal challenges