Kai prekės parduodamos telefonu, įprasti pirkinių krepšelio modeliai netinka, nes ne visos prekės būna pasiūlytos visiems klientams. Šiame magistro darbe keli žinomi modeliai adaptuoti tokiai situacijai. Be to, sukurtas naujas modelis, kuriame atsižvelgiama į kliento pirkimų apimtis ir laiką, praėjusį nuo paskutinio pirkimo. Visi modeliai pritaikyti realiems pardavimų duomenims. / When items are sold via phone, common market basket models can not be applied, because not every item has been proposed to every customer. In this master thesis some known models are adapted to this particular case. Moreover, the new model takes the amount of customer purchases' and the time since his last purchase into consideration. All models are applied to real trade data.

Master of Agribusiness / Department of Agricultural Economics / Hikaru Hanawa Peterson / This thesis focuses on the question of the effect of commodity pricing and Federal programs on the cost of food in the United States. For many decades the debate around subsidy payments has been argued in the halls of Congress and in farm fields across the country. Corn, wheat, and soybeans are the three largest crops subsidized in the United States today; arguably, the prices of these crops are influenced by subsidy payments. The goal of this thesis is to determine the effects of the prices of the top three subsidized crops on the thrifty market basket for families for four published by the USDA, factoring in transportation costs, market spread, agricultural technology advancements, and market value share. Previous studies have focused on direct subsidy payments as a whole and their aggregate influence on the price of food. This paper builds on the past studies by evaluating the effects of crop-specific programs on the cost of food. Econometric regression analysis was used to analyze the data gathered to support or refute the hypothesis that commodity prices and Federal payments do influence the cost of food. Initially data were gathered from January 1960 to December 2012. The data were adjusted for inflation using the Producer Price Index and Consumer Price Index where appropriate. After multiple attempts of modeling it was discovered that data from 1960 to 1970 needed to be discarded due to the difference in the market basket price calculations from the rest of the series. Furthermore, the model was adjusted based on the presence of multicollinearity, and the Hildreth-Lu Method was utilized to correct for the autocorrelation in error. The regression results illustrated that the only commodity of the three considered in the study that had a positive and statistically significant impact on the cost of food over the sample period was corn (p-value = 0.005). The coefficients on wheat and soybean prices were statistically insignificant. The historical fuel price had the expected positive sign and was statistically significant. The agricultural technology factor was not significant. The results also suggested that the cereal grains supply chain has significantly increased the cost of food. Both the cereal grain farm value share and the retail-to-farm spread for cereal grains were statistically significant (p-value < 0.000) with positive coefficients. The price spread of fruit was statistically significant, (p-value = 0.000), but the farm value was not. The regression results were initially surprising for the crop price variables. The overall analysis supports previous studies that crop subsidies alone may not have impacted food prices per se, but biofuel policies may have had unintended consequences. Crop-specific results provide more information to consider when discussing The Farm Bill and the implications of such a complicated and omnibus piece of legislation.

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.

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.

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.

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.

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"

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)

The term electronic commerce (e-commerce) refers to a business model that allows companies and individuals to buy and sell goods and services over the internet. The focus of this thesis is on online pharmacies, a segment of the ecommerce market. Even though internet pharmacies are still subject to the same stringent rules imposed on pharmacies that limit the scope for their market growth, it has shown a notable increase in the past decades. The main goal of this thesis is to develop a data-driven marketing strategy based on a Swedish based online pharmacy’s daily sales data. The methodology of the data analysis includes exploratory data analysis (EDA) and market basket analysis (MBA) using the Apriori algorithm and the application of marketing frameworks and theories from a data-driven standpoint. In addition to the data analysis, this paper proposes a conceptual framework of a digital marketing strategy based on the RACE framework (reach, act, convert, and engage). The result of the analysis has led to the following data-driven marketing strategy: Special attention should be paid to association rules with a high lift ration value; high gross profit margin percentile (GPMP) products should have a volume-based marketing strategy that focuses on lower prices on subsequent items; and price bundling is the best marketing strategy for low GPMP products. Some of the practical ideas mentioned in this thesis paper include optimizing keyword search for a high GPMP product type and sending reminder emails and push alerts to avoid cart abandonment. The findings and recommendations presented in this thesis can be used by online pharmacies to extract knowledge that may support several decisions ranging from raising overall order size, marketing campaigns, to increasing the sales of products with a high gross profit margin.

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.