Dissertations / Theses: 'MBA- Market Basket Analysis'

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Verma, Nikhil <1989&gt. "Market basket analysis." Master's Degree Thesis, Università Ca' Foscari Venezia, 2017. http://hdl.handle.net/10579/10564.

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The field of market basket analysis, the search for meaningful associations in customer purchase data, is one of the oldest areas of data mining. The typical solution involves the mining and analysis of association rules, which take the form of statements such as ‘‘people who buy diapers are likely to buy beer’’. It is well-known, however, that typical transaction datasets can support hundreds or thousands of obvious association rules for each interesting rule, and filtering through the rules is a non-trivial task.One may use an interestingness measure to quantify the usefulness of various rules, but there is no single agreed-upon measure and different measures can result in very different rankings of association rules. In this work, we take a different approach to mining transaction data. By modeling the data as a product network, we discover expressive communities (clusters) in the data, which can then be targeted for further analysis. We demonstrate that our network based approach can concisely isolate influence among products, mitigating the need to search through massive lists of association rules. We develop an interestingness measure for communities of products and show that it isolates useful, actionable communities. Finally, we build upon our experience with product networks to propose a comprehensive analysis strategy by combining both traditional and network-based techniques. This framework is capable of generating insights that are difficult to achieve with traditional analysis methods.

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Svensson, J. Håkan, and Fadi Abbas. "Mobile Imaging: A Market Analysis : MBA-thesis i marketing." Thesis, University of Gävle, Department of Business Administration and Economics, 2008. http://urn.kb.se/resolve?urn=urn:nbn:se:hig:diva-3533.

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Abstract Camera phones are moving into the rapid growth stage and they will rapidly be the most common image capture device in the world. Analysis agencies Gartner, ABI-Research and Future Image estimate that over 650 million camera phones were shipped in 2007 and that by the end of the decade there will be a global population of over one billion mobile imaging handsets -- more than double the number of digital still cameras (DSC). Although handset shipments are flourishing, consumers are not using their camera phones to the fullest extent. Researchers reveal that the number of photos taken, shared, and printed is relatively very low compared with DSC, resulting in a significant unrealised revenue potential for the mobile imaging industry. Despite the current limitations, recent researches done by Nokia marketing reveal that 40% of camera phone users indicate the camera phone is their primary camera. The researches suggest that with improvements in functionality, quality, usability and usage model, camera phones have the potential to be the most common and most frequently used type of camera. We believe the industry needs to stimulate more photo activity among camera phone owners to speed up (1) handset purchases, (2) picture taking, and (3) sharing, storing and printing services. To achieve this we believe companies with niche imaging technologies; such as faster decoding, less memory usage, minor processor (CPU) demands, rich features, and rich user experience have slightly high chance to outstand itself in this market. Also companies within the mobile imaging sphere which should have a competitive edge are the one who can solve inhibitors. Five out of every six sold cameras will be embedded in mobile phones. We believe the dominance of camera-phones will impact the imaging market in a variety of ways that will benefit the industry. This report aims to initiate a simple approach to give a high level view for companies aiming and searching for mobile imaging opportunities and should help extending the mobile focus thinking and area. Initiating a study on mobile imaging has been a challenge and this is due to two main reasons; the speed of development within this particular industry and the access to credible sources whether commercial or scientific. There is a significant fierce competition in the industry and it has been a great advantage to the authors to have had access to commercial reports and information sources first hand. When it comes to theories and methods, they have been taped from both the mainstream marketing literature and guerrilla marketing. There has been no obvious advantage to exclude mainstream marketing theories for this fast growing and quickly changing industry and methods described has proven worth while for the outcome of this study.The mainstream marketing literature has been utilised in the market analysis performed on the materiel obtained and when looking to the future possibilities and opportunities as well.The MIO model or the MIO-perspective, has been an excellent tool to help digest the information in a structural way and the three perspectives that are the foundation of the model; Market, Interaction and Organisation are all needed in any successful Marketing activity whether it is a fast moving business like the mobile image one or a more traditional industry e.g. the car industry. The model identifies the present situation, the future, strategy and action plan, all important components in forming the business plan.When describing both the present environment and when searching for new opportunities, the usage of the classic 4P’s is outstanding. Product, Price, Promotion and Place are all important parameters to elaborate on and as the MIO model points out, one should first focus on the industry as a whole and not once own enterprise in order to find profitable ways to develop the business. Some conclusions drawn from the study are; the more megapixels camera phone are released the more the customers’ awareness and education is raised and refined. This leads to better customers inconvenience to get the best out of their phones and the myth of getting a free digital camera does not live any more. The customers are getting better informed and they want their right to one converged high quality device where the camera is as important as the call functions of a phone device.

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Stevanato, Alessandra <1990&gt. "Market basket analysis: il caso dei prodotti "white" nella realtà aziendale Pam." Master's Degree Thesis, Università Ca' Foscari Venezia, 2015. http://hdl.handle.net/10579/5737.

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La market basket analysis utilizza le informazioni che derivano dalle molteplici esperienze di acquisto per dirci chi sono i consumatori, perché fanno determinati acquisti, ma anche quali prodotti vengono venduti insieme e quali sono più propensi a essere soggetti a promozioni. Questo elaborato ha lo scopo di studiare un gruppo di prodotti chiamato "white", comprendente riso, latte e biscotti,analizzandone le vendite all'interno della realtà aziendale Pam.

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Duan, Lian. "Effective and efficient correlation analysis with application to market basket analysis and network community detection." Diss., University of Iowa, 2012. https://ir.uiowa.edu/etd/3288.

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Finding the most interesting correlations among items is essential for problems in many commercial, medical, and scientific domains. For example, what kinds of items should be recommended with regard to what has been purchased by a customer? How to arrange the store shelf in order to increase sales? How to partition the whole social network into several communities for successful advertising campaigns? Which set of individuals on a social network should we target to convince in order to trigger a large cascade of further adoptions? When conducting correlation analysis, traditional methods have both effectiveness and efficiency problems, which will be addressed in this dissertation. Here, we explore the effectiveness problem in three ways. First, we expand the set of desirable properties and study the property satisfaction for different correlation measures. Second, we study different techniques to adjust original correlation measure, and propose two new correlation measures: the Simplified χ² with Continuity Correction and the Simplified χ² with Support. Third, we study the upper and lower bounds of different measures and categorize them by the bound differences. Combining with the above three directions, we provide guidelines for users to choose the proper measure according to their situations. With the proper correlation measure, we start to solve the efficiency problem for a large dataset. Here, we propose a fully-correlated itemset (FCI) framework to decouple the correlation measure from the need for efficient search. By wrapping the desired measure in our FCI framework, we take advantage of the desired measure's superiority in evaluating itemsets, eliminate itemsets with irrelevant items, and achieve good computational performance. In addition, we identify a 1-dimensional monotone property of the upper bound of any good correlation measure, and different 2-dimensional monotone properties for different types of correlation measures. We can either use the 2-dimensional search algorithm to retrieve correlated pairs above a certain threshold, or our new Token-Ring algorithm to find top-κ correlated pairs to prune many pairs without computing their correlations. In order to speed up FCI search, we build an enumeration tree to save the fully-correlated value (FCV) for all the FCIs under an initial threshold. We can either efficiently retrieve the desired FCIs for any given threshold above the initial threshold or incrementally grow the tree if the given threshold is below the initial threshold. With the theoretical analysis on correlation search, we applied our research to two typical applications: Market Basket Analysis and Network Community Detection.

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Hager, Garrett W., and Thomas T. King. "An industry analysis of the MBA market and the competitive positioning of the GSBPP." Monterey, California. Naval Postgraduate School, 2007. http://hdl.handle.net/10945/10260.

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MBA Professional Report The purpose of this Master of Business Administration (MBA) project is to perform an industry analysis of the MBA market utilizing the principles of industry analysis noted in Michael Porter's "Five Force Analysis" model. The intent of this project is to create an awareness of how these competitive forces shape the MBA market, define the relationships between stakeholders within the marlet, and affect its overall attractiveness of the MBA market. The goal is to provide GSBPP policy makers a tool to assist in the understanding of the industry environment and the development of a sustainable competitive position for the GSBPP. The authors believe the GSBPP can benefit greatly from this knowledge. The starting point of this project was to perform a comprehensive search and analysis of secondary resources for data concerning the nature of competition, incentives of buyers (students and employers/recruiters) and sellers (faculty), and threat of substitutes within the MBA market. It is noted that the nature of competition is a critical force affecting the MBA market, as an MBA program's reputation drives almost all aspects of its strategy. Full understanding of these market forces should help the GCBPP to aptly compete in this market.

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Ansari, Akbar. "Evaluatin Strategy for Candidates drug and implementation to product D : MBA-thesis in marketing." Thesis, University of Gävle, Department of Business Administration and Economics, 2008. http://urn.kb.se/resolve?urn=urn:nbn:se:hig:diva-694.

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Biovitrumis is one of the larger biopharma companies in Euprope and conducts research in different niche areas. One of the interested areas for Biovitrum is Exocrine Pancreatic insufficiency. Biovitrum has a candidate drug (product D) in early development process for treatment of. Exocrine Pancreatic Insufficiency.The Drug development is a long, complicated and costly process. Therefore, it is very important for managers to know the commercial value of a future drug. The purpose of this report is to develop a model for evaluation of candidate drug in early development phase and analysis of market potential for product D for treatment of Chronic pancreatitis. Chronic pancreatitis is disease within sub group of Exocrine Pancreatic Insufficiency.The deductive reasoning approach and quantitative data is used in this report. Only secondary data is collected for this study. The sources of secondary data are research papers, Google Pub-Med database and consultant companies.A new model is developed for evaluation of candidate drug .This model can analyse the market potential, unmet medical need, and calculate net present values. This study shows that there is unmet medical need in chronic pancreatitis. The result also shows that product D has comparative advantages over present products in the market and future competitors. The product D can full fill the unmet medical need for treatment of Chronic pancreatitis

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Mild, Andreas, and Thomas Reutterer. "An improved collaborative filtering approach for predicting cross-category purchases based on binary market basket data." SFB Adaptive Information Systems and Modelling in Economics and Management Science, WU Vienna University of Economics and Business, 2002. http://epub.wu.ac.at/414/1/document.pdf.

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Retail managers have been interested in learning about cross-category purchase behavior of their customers for a fairly long time. More recently, the task of inferring cross-category relationship patterns among retail assortments is gaining attraction due to its promotional potential within recommender systems used in online environments. Collaborative filtering algorithms are frequently used in such settings for the prediction of choices, preferences and/or ratings of online users. This paper investigates the suitability of such methods for situations when only binary pick-any customer information (i.e., choice/nonchoice of items, such as shopping basket data) is available. We present an extension of collaborative filtering algorithms for such data situations and apply it to a real-world retail transaction dataset. The new method is benchmarked against more conventional algorithms and can be shown to deliver superior results in terms of predictive accuracy. (author's abstract) Series: Report Series SFB "Adaptive Information Systems and Modelling in Economics and Management Science"

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Santarcangelo, Vito. "Visual Behavior Analysis in Retail Scenario." Doctoral thesis, Università di Catania, 2018. http://hdl.handle.net/10761/4135.

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The retail world is today highly competitive and has seen its logics completely revolutionized by the introduction of e-commerce that have prompted a reaction from the retail market, requiring greater attention to the consumer. We therefore moved from the world of traditional marketing (generic flyer) to that of 1to1 marketing (specific attention to the customer, profiling and personalization of the assortment offer). In this context the need arises to introduce innovative tools that can allow the physical sales spaces to be kept competitive, interacting more with the customer in order to create a more relevant commercial proposal. As a consequence, the computer vision represented one of the possible means to carry out the behavioral analysis of the consumer useful for dynamically adapting the assortment proposal. DOOH (Digital Out Of Home) in its most widespread form of interactive point-of sale kiosks is one of the best tools to get in touch with the customer, create a synergy with him, listen to his needs in order to improve the offer, the level of service and therefore customer satisfaction. Next to DOOH, it is necessary to introduce further and time-continuous monitoring tools, which map the entire customer's shopping experience into the point of sale. For this purpose the egocentric vision is introduced through the use of cam narratives on board the trolleys, which allow a timely story of the consumer, called Visual Market Basket Analysis (evolution of Market Basket Analysis), which generates process functional alerts to the improvement of the service offered. The story of these approaches is provided in this PhD thesis, which tells the three-year course carried out, its experiments and possible future developments. This study has been conducted thanks to the support of Centro Studi S.r.l., a sister company of a privately owned consumer goods distribution company called Orizzonti Holding Group, located in southern Italy. The study has been implemented through an industrial application approach, in a real context (Futura Supermarkets). Consequently, the PhD thesis has considered the typical difficulties of a challenging environment, starting from the creation and acquisition of a dataset to the integration of the approach in the current business processes.

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Schröder, Nadine. "Using Multidimensional Item Response Theory Models to Explain Multi-Category Purchases." Vahlen, 2017. http://epub.wu.ac.at/6538/1/0344%2D1369%2D2017%2D2%2D27.pdf.

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We apply multidimensional item response theory models (MIRT) to analyse multi-category purchase decisions. We further compare their performance to benchmark models by means of topic models. Estimation is based on two types of data sets. One contains only binary the other polytomous purchase decisions. We show that MIRT are superior w. r. t. our chosen benchmark models. In particular, MIRT are able to reveal intuitive latent traits that can be interpreted as characteristics of households relevant for multi-category purchase decisions. With the help of latent traits marketers are able to predict future purchase behaviour for various types of households. These information may guide shop managers for cross selling activities and product recommendations.

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Baruzzo, Nicolo' <1993&gt. "REGOLE ASSOCIATIVE: IL MARKET BASKET ANALYSIS PER IDENTIFICARE LE RELAZIONI NEI PROCESSI D’ACQUISTO DEI CONSUMATORI MEDIANTE GLI AMBIENTI STATISTICI “R” E “WEKA”." Master's Degree Thesis, Università Ca' Foscari Venezia, 2019. http://hdl.handle.net/10579/14477.

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Con questo elaborato si andrà ad analizzare un dataset di scontrini (ricevuti dal soggetto In's, appartenete al gruppo PAM) utilizzando le regole associative per mezzo di due ambienti statistici. L'obbiettivo è individuare delle associazioni che esistono tra panieri di beni nel comportamento di acquisto dei consumatori.

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Tavares, Pedro Daniel. "Segmentação dos usuários de cartão de crédito por meio da análise de cesto de compras." Universidade de São Paulo, 2012. http://www.teses.usp.br/teses/disponiveis/12/12139/tde-16032012-183822/.

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Esta dissertação de mestrado tem como objetivo, elaborar um modelo de segmentação baseando-se no comportamento comprovado de consumo de clientes, valendo-se das técnicas de Análise de Associação e Análise de Cesto de Compras, aplicadas aos dados das faturas de cartão de crédito dos clientes. A partir do modelo proposto, testou-se a previsibilidade das próximas transações dos clientes por meio de uma amostra de validação. A motivação desta pesquisa provém de três pilares: Contexto Científico, Tecnológico e Mercadológico. No Contexto Científico, apesar de já terem sido publicados artigos que associam a utilização do cartão de crédito a perfis de segmentação de clientes, não se encontram publicados estudos que relacionam dados da própria utilização do cartão como fonte de informação do cliente. A razão mais provável para isso é a dificuldade no levantamento dos dados fundamentais para este tipo de pesquisa. Com o apoio de uma grande instituição financeira, este trabalho está se tornando viável, sob o preceito da análise apenas sobre bases de clientes anônimos e que não transpareça informações estratégicas da instituição. No contexto tecnológico, com a tecnologia de informação em crescente desenvolvimento, as operações feitas com cartão de crédito tem o processamento on-line em tempo real, promovendo a troca de informação entre o estabelecimento comercial e a instituição emissora do cartão de crédito no momento em que a cobrança é lançada e aceita pelo consumidor final. Isso possibilita que ações promocionais sejam realizadas em toda a cadeia de valor de cartões de crédito, gerando mais valor para os clientes e empresas. No contexto mercadológico, o Brasil apresentou altas taxas de crescimento do mercado de cartões de crédito nas últimas décadas, substituindo os outros meios mais antigos de pagamento e de crediário. Especialmente no Brasil, observam-se compras pagas com o uso do cartão de crédito parceladas com e sem juros, o que contribui para a substituição de outras formas de crédito. Como benefício deste trabalho, concluiu-se que a partir do conhecimento do consumo do cliente, pode-se aplicar a análise de cesto de compras para prever as próximas transações dos clientes, a fim de segmentar os clientes para estimulá-los a aderir a uma determinada oferta. The objective of this research is elaborating a Segmentation Model based on credit card client\'s behavior using Link Analysis and Market Basket Analysis techniques. The proposed model was used to testing the predictability of next client transactions through validation sample. Scientific, technological and marketing scenarios are the three motivational pillars of this research. On scientific context there were published studies that associate credit card use with segmentation profile of customer. However these studies do not establish relationship between data from own clients credit card utilization. One probably reason for this lack analysis into studies is the difficult collect of fundamental data. This research was feasible with the support of a great Brazilian financial group. On technological context is observed a wide information technology development. Credit cards transactions have on-line processing. This scenario allows exchange information between market and credit card institution at the moment of final client transaction approval. This technology permits that actions be realized along credit card value chain based on transactions that have been made. On marketing context, during the latest decades, Brazil has shown large growth rates on credit card beyond older ways of payment. In Brazil, is observed a wide utilization of credit cards in installment purchases contributing for the replacement of other ways of credits. This research conclude that from the knowledge of client consume profile, using the Market Basket Analysis technique, it is possible to get a forecast of purchase transactions with the objective to stimulate the consumer in accept particular offer.

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Prášil, Zdeněk. "Využití data miningu v řízení podniku." Master's thesis, Vysoká škola ekonomická v Praze, 2010. http://www.nusl.cz/ntk/nusl-150279.

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The thesis is focused on data mining and its use in management of an enterprise. The thesis is structured into theoretical and practical part. Aim of the theoretical part was to find out: 1/ the most used methods of the data mining, 2/ typical application areas, 3/ typical problems solved in the application areas. Aim of the practical part was: 1/ to demonstrate use of the data mining in small Czech e-shop for understanding of the structure of the sale data, 2/ to demonstrate, how the data mining analysis can help to increase marketing results. In my analyses of the literature data I found decision trees, linear and logistic regression, neural network, segmentation methods and association rules are the most used methods of the data mining analysis. CRM and marketing, financial institutions, insurance and telecommunication companies, retail trade and production are the application areas using the data mining the most. The specific tasks of the data mining focus on relationships between marketing sales and customers to make better business. In the analysis of the e-shop data I revealed the types of goods which are buying together. Based on this fact I proposed that the strategy supporting this type of shopping is crucial for the business success. As a conclusion I proved the data mining is methods appropriate also for the small e-shop and have capacity to improve its marketing strategy.

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Mendes, António Miguel Lobo Machado Teixeira. "Market basket analysis." Master's thesis, 2020. http://hdl.handle.net/10362/104155.

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The purpose of this thesis is to identify relevant relationships between Recheio’s product categories with the aim of increasing the firm’s sales. A Market Basket Analysis was performed to unveil these relationshipswhere atotal of 53 different Association Ruleswere generated. By including the obtained results in its order placement process, Recheio will be able to capture more value from each order by making sustained suggestions to its customers regarding what additional products to buy.

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Chang, Wen-Chi, and 張紋綺. "Applying Market Basket Analysis for Recommendation System." Thesis, 2012. http://ndltd.ncl.edu.tw/handle/19128973600928189307.

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碩士 國立臺灣大學 國際企業學研究所 100 The development of information enable customers to compare rapidly and communicate more frequently through the website. Due to the innovation of information technology, the enterprises have to respect an issue that how to use the information to cultivate the relationship between enterprises and customers. Recently, the development of the recommendation system has not only become the research target of professors also attract the concern of practice. However, most of enterprises build the recommendation system without sufficient technology and resources. Therefore, this article will construct the recommendation system with simple and scientific methods and as the reference system to enterprises. This article will adopt the concept of market basket analysis base on the trading records of customers, and we will focus on the analysis of aggregated customers, group customers, and individuals with the conditional probability matrix and Hierarchical Bayesian Probit model. We will use the analysis to filter out various market baskets as the recommended products to the different customers. The result can show not only the correlation between different products, but also the re-purchase rate of products. On the other hand, we find that the hitting rate of Hierarchical Bayesian Probit model is higher than the hitting rate of the average probability without any information.

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HUNG-HSIANG, LU, and 陸泓翔. "Market Basket Analysis：3C Database as an Example." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/5245mz.

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碩士 國立暨南國際大學 國際企業學系 105 With the development of science and technology, 3C has become one of the indispensable products. Consumers can receive more and more products informations whether it’s from the brick and mortar store or online sales. Enterprises can recommend suitable products to stimulate consumption by according members’ past consuming behavior. Maintaining a good relationship with consumers is an important issue from now on. In this study, we use the factor analysis to find the product combinations and the correlation between products from the 3C database. Finally, we use the chi-square automatic interaction detection to find the target consumers in every basket. The result of this study shows that we can distinguish the consumers’ target by ages and gender not only in the whole target consumers but also in the refund of credit cards. We hope to promote the amount of sales by driving poor-sale goods with good-sale goods. Integrating the above-mentioned, enterprises can determine strategies appropriately on the display of products, products promotion and joint promotion to increase the sales.

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Huang, Huai-Hsuan, and 黃懷萱. "Analysis of Links and Communities in Retail Market Basket." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/r659m9.

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碩士 國立政治大學 資訊管理學系 107 This research develops a set of techniques for analysis of co-purchased in the retail industry using social network analysis (SNA), and actually implements those techniques on the transaction data from the domestic well-known convenience stores. First, different weighting methods are used to express the actual strengths and cash flows of co-purchased products. In addition, this research uses the community detection to identify co-purchased products from transaction data. If a set of products are frequently purchased together, they form a self-contained community whose internal connections are stronger than external connections. Also, the value and importance of each community will be measured. This research further uses the metrics from the network analysis to identify roles of products among communities. Finally, on top of products purchased together, this research uses the longest link to identify the indirect relationships of co-purchased products. Overall, this research aims to propose an analysis method to better understand co-purchased products from the transaction data. We visually represent co-purchased products using product network, evaluate networks via different weights, and discover relationships among products from the network. The proposed method should help retailers better understand their transaction data, and hence provide information to improve their marketing strategies.

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Wu, Pei-Sheng, and 吳培聖. "Market Basket Analysis-Take Online Shopping with Amazon.com For Example." Thesis, 2013. http://ndltd.ncl.edu.tw/handle/04282761030201999844.

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碩士 國立暨南國際大學 國際企業學系 101 Due to the development and progress of internet, consumers not only can buy commodities through online shopping platform, but also receive more information about products than before. Once the product alternatives become more available, consumers are able to compare the products from different brands. Therefore, the relationship between firms and consumers is hard to maintain. In order to increase consumer’s demand to buy and establish good relationship, firms have figure out ways to recommend the appropriate commodities initiatively when consumers are browsing the web. Thus, this is the most important issue for firms from now on. This study adopts the concept of market basket analysis to measure the correlation between the products, and, through non-metric multidimensional scaling to analyze consumer's purchase pattern. Object of study divides into two parts. First, we use market basket analysis for overall consumers. Second, we segment the consumers by their purchasing amount of each product, and do market basket analysis for each segment. Through this study, we can find the appropriate product portfolio as the basis of recommendation for marketing. The results of this study, first, for overall consumers, the highest correlation commodities are food and beverage, and art and collectibles as well. Second, for book and magazines' target market, the highest correlation commodities are apparel and beauty supplies. At last, for any cluster, food and beverages, art and collectibles, apparel, and computer games are frequently to be purchased and these four separated are relevant. In conclusion, firms can use joint promotion to motivate consumers’ demand by displaying of products in stores and on the web as well.

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Ansari, Sohaib Zafar. "Market basket analysis : trend analysis of association rules in different time periods." Master's thesis, 2019. http://hdl.handle.net/10362/80955.

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Dissertation presented as the partial requirement for obtaining a Master's degree in Statistics and Information Management, specialization in Marketing Research e CRM Market basket analysis (i.e. Data mining technique in the field of marketing) is the method to find the associations between the items / item sets and based on those associations we can analyze the consumer behavior. In this research we have presented the variability of time, because with the change in time the habits or behavior of the customer also changes. For example, people wear warm clothes in winter and light clothes in summer. Similarly, customers purchase behavior also changes with the change in time. We study the problem of discovering association rules that display regular cyclic variation over time. This problem will allow us to access the changing trends in the purchase behavior of customers in a retail market, and we will be able to analyze the results which will display the changing trends of the association rules. In this research we will study the interaction between association rules and time. We worked on transactional data of a Belgian retail company and analyzed the results which will help the company to build up time period specific marketing strategies, promotional strategies, etc. to increase the profit of their company.

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Anselmo, Filomena Clara Gouveia. "Regras de Associação - Market Basket Analysis - Items Frequentes e Itens Raros." Master's thesis, 2017. https://hdl.handle.net/10216/107693.

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Hsueh, Chun-Wen, and 薛淳文. "Applying Market Basket Analysis to Explore Purchasing Behavior of Supermarket Members." Thesis, 2011. http://ndltd.ncl.edu.tw/handle/87995273523151938565.

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碩士 國立臺灣大學 國際企業學研究所 99 Considering the abundance of convenient stores, price advantage of the discount stores, and convenience of internet shopping, supermarkets in Taiwan are facing more competitions than just its competitors in the same market. As consumers become more knowledgeble, product-oriented sales strategies will not win the customers’ trust or gain their loyalties anymore. Therefore, identifying customers into the right segments, and deciding sales strategies accordingly is even more important nowadays. This study attempts to understand the purchasing behavior of the supermarket members by using the database of one of the famous supermarket in Taiwan. In this study, members are separated into family life cycle according to their age, marriage status, and number of children. Market basket analysis is used to explore buying behaviors of different family life cycle. Also, by comparing the average frequency, average amount of money spent, and the market basket contents in the different types of stores for different family life cycle, this study is able to understand if the same family life cycle members have the same purchasing behavior. Thus, based on the results of this research, marketers can suggest sales strategies that are more effective.

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Anselmo, Filomena Clara Gouveia. "Regras de Associação - Market Basket Analysis - Items Frequentes e Itens Raros." Dissertação, 2017. https://hdl.handle.net/10216/107693.

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Lo, Anborson, and 羅元鴻. "Market Basket and Customer Activity Analysis: Take Online Shopping of Food and Drink for Example." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/5bz242.

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碩士 國立暨南國際大學 國際企業學系 104 Nowadays, the Internet is planted deeply in our daily life, including business transactions between people. Compared to brick and mortar stores, online shops immediately record many kinds of customers’ information. The storage of data is huge and quickly gives the feedback. The program could be designed to recommend customers purchase other products and services. 　　Therefore, to increase company sales and maintain continual benefits, it is a critical that the database marketing knows how to find out the relationships between products and customer values. 　　The researcher adopts the customer activity index and the monetary amount calculated from the food transaction information, build the customer activity analysis model. Then, the researcher chooses two groups “Active-Loyal Customers” and “Active-Potential Customers” as the focus customer value groups. After that, the researcher generates two product clusters for each customer value group, through calculating the product relationships by association-rules of market basket analysis. 　　The end result shows that there are three product groups in the product cluster of “Active-Loyal Customers”; and there are two product groups in the product cluster of “Active-Potential Customers”. This means that the probity of the joint purchases of customers in the same products cluster will highly occur. 　　According to the result, if the customer value group is understood, the company can recommend the customer purchase other products in the product group which depends on the product purchased by the customer. Besides, the company can also have joint promotions based on the products in the same product group. These two ways of marketing could not only improve the customers’ purchase behaviors but maintain the customer values. The companies will produce extra profits both now and in the future.

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Oliveira, Joana Raquel Carias de. "Data Mining na procura de nova informação: Market Basket Analysis aplicado a um dataset público." Master's thesis, 2019. http://hdl.handle.net/10400.26/37552.

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Hoje em dia, a população encontra-se sobrecarregada com dados, quando todas as atividades realizadas pelas organizações e pessoas, no seu dia-a-dia, geram dados. Contudo, o facto de termos acesso a um enorme volume de dados não significa que tenhamos acesso a muita informação ou conhecimento. É, portanto, importante trabalhar os dados por forma a gerar informação relevante para a tomada de decisão, pois num mundo globalizado e extremamente competitivo, um minuto pode ser fulcral para fechar um negócio e, para tal, é necessário ter acesso à informação atual, correta e sumarizada. Face ao volume de dados existente e a necessidade de criar vantagens competitivas para as empresas sobreviverem nos seus mercados importa analisar os dados por forma a identificar informação que poderia estar oculta ou padrões nos comportamentos dos consumidores. É aqui que entra o data mining, cujo principal objetivo é analisar os dados e encontrar anomalias, padrões ou novas informações que auxiliem na tomada de decisão. O setor do retalho é um dos setores que mais valor monetário gera mundialmente e um dos setores onde a concorrência é mais feroz, pelo que quanto mais conhecimento e informações as empresas tiverem ao seu dispor maior será a probabilidade de conseguirem adquirir vantagens competitivas. Nesta procura de informação temos como exemplo as regras de associação, uma técnica de data mining cujo objetivo é encontrar itens que ocorrem frequentemente e em conjunto nos cestos de compras dos clientes. Um dos algoritmos concebidos para a geração de regras de associação é o algoritmo Apriori em que a sua génese foi baseada na análise de compras efetuadas num supermercado. Ao aplicar algoritmos para obter regras de associação ao setor do retalho é comum indicar- se que se usou uma técnica de market basket analysis. Este trabalho tem como principais objetivos a análise exploratória de um dataset público com um grande conjunto de compras (Instacart) e a geração de regras de associação recorrendo à utilização do algoritmo Apriori. Consoante os resultados obtidos serão sugeridas ideias para implementar novas estratégia de marketing. Este trabalho iniciou-se com a revisão da literatura, investigando os conceitos de data mining, regras de associação e market basket analysis. Como bússola orientadora para a aplicação de técnicas de data mining seguiu-se a metodologia CRISP. Para a análise exploratória dos dados foi utilizado o software Power BI e para a transformação dos dados e aplicação do algoritmo Apriori e consequentemente a geração das regras de associação recorreu-se à linguagem Python. Nowadays, the population is overloaded with data, where practically all activities performed generate it. However, the fact that we have access to a huge amount of data does not mean that we have access to a lot of information. Therefore, it is important to process the data in order to generate relevant information for decision making as well as to separate the relevant and non-relevant data in order to speed up processes that generate important information in decision making, because in a globalized and extremely competitive world, a minute can decide the fate of closing a deal. Given the volume of data that exists and the need to create competitive advantages for companies to survive in their markets, it is important to analyze the data to identify information that could be hidden or patterns in consumer behavior. This is where data mining comes in, its main goal is to analyze data and find anomalies, patterns or new information to assist in decision making. The retail area is one of the most monetarily generated areas in the world and one of the fiercest competition areas, so companies that have better knowledge and information are likely to gain competitive advantage. In this information quest, we have as an example the association rules, a data mining technique where the objective is to find items that occur frequently and together. One of the algorithms designed to generate association rules is the Apriori algorithm, its genesis was based on the analysis of purchases made in a supermarket. Thus, when applying the rules of association to the retail sector, it is usual to indicate that the market basket analysis technique was used. This work has as main objectives the exploratory data analysis of a public dataset with a huge number of market baskets records (Instacart) and the generation of association rules using the Apriori algorithm. Depending on the results obtained, ideas for implementing a new marketing strategy will be suggested. This work started with the literature review, investigating the concepts of data mining, association rules and market basket analysis. An example of the application of the Apriori algorithm for the generation of association rules was also verified. As a guiding compass for the application of data mining techniques, the CRISP methodology was followed. For exploratory data analysis is was used Power BI software and for data transformation and application of the Apriori algorithm and consequently the generation of association rules was used Python.

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Gonçalves, Rita Sebastiana Vaz. "The beauty or the beast inside retail stores?: a market basket analysis of a cosmetic company." Master's thesis, 2020. http://hdl.handle.net/10071/21886.

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Nowadays, companies have in storage a large quantity of customer data and do not take advantage of this. Given the state of the market, in order to stay competitive, retailers should know and adapt to the needs of their customers. One way to accomplish such task is to perform a Market Basket Analysis to the available customer data. A Market Basket Analysis examines transactional data to find which items are related to each other enabling retailers to create new marketing strategies such as store layout, product placement, pricing and promotions. In this study, a market basket analysis was performed to transactional data provided by a Portuguese cosmetics company with the purpose of finding which items are put in the same “basket” by their current customers. This analysis resulted in the generation of several association rules about customers with similar demographic variables such as, gender, age and geographical location. For example, this analysis allowed to discover the items that customers belonging to a certain gender and age group are likely to buy. For example, through this analysis it was possible to discover that women aged over 60 years old that buy the hair removal service {87880} will also buy the hair removal service {114325} with a confidence of 94%. It was also discovered that women aged between 35 and 44 years old that visit one particular store in the greater Lisbon area buy the makeup foundation 85945 and the cream 99282 will buy the cream 108741 with a likelihood of 89%. One other example of the association discovered is that customers that visit another store in the greater Lisbon area have a very high inclination to buy together the men’s’ perfume {73933}, the aftershave {84539} and the deodorant {105213}. Hoje em dia, as empresas têm uma grande quantidade de dados de clientes armazenada que não é aproveitada e, tendo em conta a situação atual do mercado, para se manterem competitivas, as empresas têm que conhecer e adaptar-se às necessidades dos seus clientes. Uma maneira de fazer isso é realizar uma Market Basket Analysis usando os dados do cliente. A Market Basket Analysis examina os dados transacionais para descobrir que itens estão relacionados, o que permite que as empresas criem novas estratégias de marketing, como o layout de loja, a alocação de produtos, preços e promoções. Neste estudo, será realizada uma Market Basket Analysis recorrendo a dados transacionais fornecidos por uma empresa portuguesa de cosméticos com o objetivo de descobrir que itens são colocados no mesmo carrinho pelos seus clientes atuais. Esta análise resultou na geração de diversas regras de associação sobre clientes com variáveis demográficas semelhantes, como o género, a idade e a localização geográfica. Por exemplo, ao realizar esta análise foi possível descobrir que mulheres com mais de 60 anos que compram o serviço de depilação {87880} vão adquirir também o serviço de depilação {114325} com uma confiança de 94%. Foi também descoberto que as mulheres com idades compreendidas entre os 35 e os 44 anos que visitam uma determinada loja da grande Lisboa que compram a base {85945} e o creme {99282} comprarão também o creme {108741}, com uma probabilidade de 89%. Outro exemplo do que foi descoberto é que os clientes que visitam uma determinada loja na zona da grande Lisboa têm uma tendência muito elevada para comprarem juntos o perfume masculino {73933}, o after-shave {84539} e o desodorizante {105213}.

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Chen, Ting-Hua, and 陳亭華. "Considering a High Utility Market Basket Analysis in Production Fitness: A Case Study of Forging Machine." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/7vfema.

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碩士 國立臺北科技大學 工業工程與管理系 106 With new technological advances, the factory gradually import a large number of machines to assist production. As a result, machine utilization has become one of the important indicators in the factory. There are many factors that affect machine utilization, such as: machine adjustment, repair a die, lack of work, lack of materials and equipment failure, etc. For traditional industry, production managers often based on past experience to arrange production plans. However, this experience is usually less quantifiable. In addition, the size and weights of products are different from each and other. If it is not properly arranged, it may lead a jam in the feed system so that the machine will frequently failure. As a consequence, production fitness is one of the key points that affect machine utilization. This study proposes a method called “High utility Market Basket Analysis” that was applied in forging industry. From the point of view of production fitness, and find out the implicit matching relationship between the machine and the product. The experiment will be divided into two phases. First, the frequently occurring items will be screened through Frequency Itemset Mining. Next, the utility will be measured by using High Utility Itemset Mining. After experiment and analysis, there are 38 rules are extracted. In this study, the rules are actually communicated with the relevant departments of the factory, and then practical rules are found. Finally, we will provide this result to the factory to help carry out related improvement work.

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Chien, Li-Chu, and 簡利曲. "Using Market Basket Analysis of Data Mining Techniques for Exploring the Most Appropriate Product Mix on Internet." Thesis, 2001. http://ndltd.ncl.edu.tw/handle/67353232313824286144.

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碩士 國立臺北大學 企業管理學系 89 Because of the characteristics of instantaneousness, interaction, multimedia, low cost and far-fetching availability, Internet becomes a new access to success for modern industries. Furthermore, it is going to be a trend in the near future. In E-commerce field , visual shop shows in the form of “Business to Consumer”（B2C）. According to most scholars, “visual shop” means customers shop through Internet while the company benefit by cutting down the cost. In addition, to keep touch with increasing customers is another important concern. It is necessary to get information about customers with efficient method and Data mining technique serves this purpose. Data mining is a technique to obtain information or knowledge, which combines data base system and machine learning technique by studying the way people behave. Many researchers also consider it an important index to gain competition of the enterprises. If enterprises can make best of data mining technique to figure out customers’ behavior, obtain personal information from transaction data, and apply the information to marketing activities efficiently, its helps to continuously retain close links with customers, witch can achieve the best profit for the enterprise. This study uses market basket analysis of data mining technique, trying to establish systematic rules from customer’s transition data. From generating the co-occurrence matrix for single items, generating the co-occurrence matrix for two items, generating the co-occurrence matrix for three items, in order to design attracting product mix which supply more convenient shopping medium for customers. It is not common to use market basket analysis in real transition data in domestic study. The following are the conclusions: 1.Most customers, 91.5 percent of them, will buy vegetables. The second choice is stolon which attracts 59.7perconet of customers. The third is bean. About 47.0 percent of customers will choose it. The last is mushroom. Only 8 percent of customers will buy mushroom. 2.Customers who buy organic vegetables on-line always purchase more than one item. Company can sell by package, which can sell in low price to urge the customers to buy their products. Besides, if company can take the factor of cost in to consideration, they can develop more kinds of packages. 3.After understand the purchase order of the customers, company can try social stratum spread out when editing shopping homepage. Market basket analysis gives insight into the merchandise by telling us which products are always purchased together. It also offers the suggestions for the sellers to plan the layout of the store. 4.Spinach and leaf go well with other items in the form of package-promotion. Thus, they can be promotion product or gift.

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Verde, Maria Alexandra Rodrigues Fonseca Baptista. "Market Basket Analysis e Aplicação de Regras de Associação Hierárquica: um caso de estudo numa empresa de retalho portuguesa." Master's thesis, 2016. https://repositorio-aberto.up.pt/handle/10216/86355.

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Verde, Maria Alexandra Rodrigues Fonseca Baptista. "Market Basket Analysis e Aplicação de Regras de Associação Hierárquica: um caso de estudo numa empresa de retalho portuguesa." Dissertação, 2016. https://repositorio-aberto.up.pt/handle/10216/86355.

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Ting, Shi-Chen, and 丁璽禎. "The Effect of Market Orientation, Resource Input and Product Innovation on the Performance of New Product- Analysis of MBA Students with EStrat9 Business Strategy Simulation." Thesis, 2009. http://ndltd.ncl.edu.tw/handle/55706839261544340889.

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碩士 國立交通大學 企業管理碩士學程 97 Enterprises continuously release new products as a strategy for pursuing sustainable development. Developing new products are required to invest huge resources. Nevertheless, the failure rate of new product release is higher than 80%. This study is to explore the effect of market orientation, resource input and product innovation on the performance of new product by investigating the questionnaire survey and the data from EStrat9 business strategy simulation which is executed by MBA students. This study obtains following results: First, the performance on new product is positively influenced by the decision maker taking importance on market orientation. Second, product innovation significantly is influenced by market orientation. However, in this study, product innovation is not the mediator of market orientation and the performance on new product thanks to short decision periods. Third, market orientation and the performance on new product are positively moderated by R&D resource input. Fourth, the simulation participants tend to allocate great deal of resources on R&D and pursue to develop new product with excellent attributes in one time for releasing killer product in the market. The simulation participants are thus inclined to be product orientated. The simulation participants represent “Prima”which is the market leader in this simulation game and has earned certain market share, the new product could thus increase market share in the short time. In practice, new product might not be capable to sustainably survive if corporate decision makers merely allocate great deal of resources on R&D but ignore the importance of resource input on marketing. To prevent new product become shooting star and with short life span, corporate decision makers should advance the performance on new product by manipulating the capability of market orientation, product innovation and well allocate resources on R&D and marketing.

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Bako, Abdulaziz Tijjani. "The Role of Social Workers in Addressing Patients' Unmet Social Needs in the Primary Care Setting." Diss., 2021. http://hdl.handle.net/1805/25986.

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Indiana University-Purdue University Indianapolis (IUPUI) Unmet social needs pose significant risk to both patients and healthcare organizations by increasing morbidity, mortality, utilization, and costs. Health care delivery organizations are increasingly employing social workers to address social needs, given the growing number of policies mandating them to identify and address their patients’ social needs. However, social workers largely document their activities using unstructured or semi-structured textual descriptions, which may not provide information that is useful for modeling, decision-making, and evaluation. Therefore, without the ability to convert these social work documentations into usable information, the utility of these textual descriptions may be limited. While manual reviews are costly, time-consuming, and require technical skills, text mining algorithms such as natural language processing (NLP) and machine learning (ML) offer cheap and scalable solutions to extracting meaningful information from large text data. Moreover, the ability to extract information on social needs and social work interventions from free-text data within electronic health records (EHR) offers the opportunity to comprehensively evaluate the outcomes specific social work interventions. However, the use of text mining tools to convert these text data into usable information has not been well explored. Furthermore, only few studies sought to comprehensively investigate the outcomes of specific social work interventions in a safety-net population. To investigate the role of social workers in addressing patients’ social needs, this dissertation: 1) utilizes NLP, to extract and categorize the social needs that lead to referral to social workers, and market basket analysis (MBA), to investigate the co-occurrence of these social needs; 2) applies NLP, ML, and deep learning techniques to extract and categorize the interventions instituted by social workers to address patients’ social needs; and 3) measures the effects of receiving a specific social work intervention type on healthcare utilization outcomes.

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March, Nicolas. "Building a Data Mining Framework for Target Marketing." Thesis, 2011. http://epub.wu.ac.at/3242/1/diss_epub_nicolas_march_20111002.pdf.

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Most retailers and scientists agree that supporting the buying decisions of individual customers or groups of customers with specific product recommendations holds great promise. Target-oriented promotional campaigns are more profitable in comparison to uniform methods of sale promotion such as discount pricing campaigns. This seems to be particulary true if the promoted products are well matched to the preferences of the customers or customer groups. But how can retailers identify customer groups and determine which products to offer them? To answer this question, this dissertation describes an algorithmic procedure which identifies customer groups with similar preferences for specific product combinations in recorded transaction data. In addition, for each customer group it recommends products which promise higher sales through cross-selling if appropriate promotion techniques are applied. To illustrate the application of this algorithmic approach, an analysis is performed on the transaction database of a supermarket. The identified customer groups are used for a simulation. The results show that appropriate promotional campaigns which implement this algorithmic approach can achieve an increase in profit from 15% to as much as 191% in contrast to uniform discounts on the purchase price of bestsellers. (author's abstract)

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Dissertations / Theses on the topic 'MBA- Market Basket Analysis'

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