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Using Multivariate Statistics

Data Mining Practical Machine Learning Tools and Techniques

MKT 6009 Marketing Internship (0 semester credit hours) Student gains experience and improves skills through appropriate developmental work assignments in a real business environment. Student must identify and submit specific business learning objectives at the beginning of the semester. The student must demonstrate exposure to the managerial perspective via involvement or observation. At semester end, student prepares an oral or poster presentation, or a written paper reflecting on the work experience. Student performance is evaluated by the work supervisor. Pass/Fail only. Prerequisites: (MAS 6102 or MBA major) and department consent required. (0-0) S MKT 6244 Digital Marketing Strategy (2 semester credit hours) Executive Education Course. The course explores three distinct areas within marketing and sales namely, digital marketing, traditional sales prospecting, and executive sales organization and strategy. The continuing convergence of the digital marketing and sales funnels has created a strategic continuum from digital lead generation to digital sales. The course identifies the current composition of this digital continuum while providing opportunities to evaluate sales and marketing digital strategies. Prerequisites: MKT 6301 and instructor consent required. (2-0) Y MKT 6301 (SYSM 6318) Marketing Management (3 semester credit hours) Overview of marketing management methods, principles and concepts including product, pricing, promotion and distribution decisions as well as segmentation, targeting and positioning. (3-0) S MKT 6309 Marketing Data Analysis and Research (3 semester credit hours) Methods employed in market research and data analysis to understand consumer behavior, customer journeys, and markets so as to enable better decision-making. Topics include understanding different sources of data, survey design, experiments, and sampling plans. The course will cover the techniques used for market sizing estimation and forecasting. In addition, the course will cover the foundational concepts and techniques used in data visualization and \"story-telling\" for clients and management. Corequisites: MKT 6301 and OPRE 6301. (3-0) Y MKT 6310 Consumer Behavior (3 semester credit hours) An exposition of the theoretical perspectives of consumer behavior along with practical marketing implication. Study of psychological, sociological and behavioral findings and frameworks with reference to consumer decision-making. Topics will include the consumer decision-making model, individual determinants of consumer behavior and environmental influences on consumer behavior and their impact on marketing. Prerequisite: MKT 6301. (3-0) Y MKT 6321 Interactive and Digital Marketing (3 semester credit hours) Introduction to the theory and practice of interactive and digital marketing. Topics covered include: online-market research, consumer behavior, conversion metrics, and segmentation considerations; ecommerce, search and display advertising, audiences, search engine marketing, email, mobile, video, social networks, and the Internet of Things. (3-0) T MKT 6322 Internet Business Models (3 semester credit hours) Topics to be covered are: consumer behavior on the Internet, advertising on the Internet, competitive strategies, market research using the Internet, brand management, managing distribution and supply chains, pricing strategies, electronic payment systems, and developing virtual organizations. Further, students learn auction theory, web content design, and clickstream analysis. Prerequisite: MKT 6301. (3-0) Y MKT 6323 Database Marketing (3 semester credit hours) Techniques to analyze, interpret, and utilize marketing databases of customers to identify a firm's best customers, understanding their needs, and targeting communications and promotions to retain such customers. Topics

Marketing for management.

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Modern Applied Statistics With S

Social Network Analysis

We propose a data mining (DM) approach to predict the success of telemarketing calls for selling bank long-term deposits. A Portuguese retail bank was addressed, with data collected from 2008 to 2013, thus including the effects of the recent financial crisis. We analyzed a large set of 150 features related with bank client, product and social-economic attributes. A semi-automatic feature selection was explored in the modeling phase, performed with the data prior to July 2012 and that allowed to select a reduced set of 22 features. We also compared four DM models: logistic regression, decision trees (DTs), neural network (NN) and support vector machine. Using two metrics, area of the receiver operating characteristic curve (AUC) and area of the LIFT cumulative curve (ALIFT), the four models were tested on an evaluation set, using the most recent data (after July 2012) and a rolling window scheme. The NN presented the best results (AUC = 0.8 and ALIFT = 0.7), allowing to reach 79% of the subscribers by selecting the half better classified clients. Also, two knowledge extraction methods, a sensitivity analysis and a DT, were applied to the NN model and revealed several key attributes (e.g., Euribor rate, direction of the call and bank agent experience). Such knowledge extraction confirmed the obtained model as credible and valuable for telemarketing campaign managers.

/pdf/a-data-driven-approach-to-predict-the-success-of-bank-3cihscyqfh.pdf

A data-driven approach to predict the success of bank telemarketing

– Arguing that increasing use of information and communication technologies (ICT) is shifting market power from suppliers to consumers, the ensuing consumer empowerment is presented as an unintended consequence of marketing. Marketing implications arising from this consumer empowerment are examined in terms of a process where control and management by suppliers over consumer access and enablement are increasingly difficult., – Consumer empowerment is examined historically, using quality gap analysis to capture an ongoing power struggle between consumers and suppliers. This draws out the limitations of current marketing and management strategies. The different forms of marketing challenges in this new environment are discussed., – The role of marketing strategies in fostering controlled consumer empowerment is reflected in the development of information‐based consumer‐centric marketing strategies that seek to enable and control delegation. In designing such strategies, consumers' familiarity with and use of ICT are both strengthened and widened, emphasising the uncontrolled nature of the consumer empowerment process., – The approach is literature‐based, focussing on the ICT enabled process. It does not address the psychology of empowerment. Since, consumer empowerment may imply switching suppliers in search of better value propositions, business cannot afford to ignore it, justifying the need for further research of both elements., – Marketing strategy rests on a control premise and the analysis of the consumer empowerment process implies that current customer‐centric strategies are operating under a false premise. There is a need to regain control over the marketing process, that is, to either manage the technological empowerment of consumers, or to devise new strategies cognisant of the possibility that such technological empowerment cannot be managed. The valuation of consumer loyalty in this environment rises significantly., – An historical perspective to consumer empowerment exposes the tensions between suppliers and consumers arising from ICT usage. A separation of consumer access and enablement from control and management by suppliers is shown to have important marketing strategy design implications.

The internet, consumer empowerment and marketing strategies

https://run.unl.pt/bitstream/10362/86404/1/Impact_e_service_quality_customer_satisfaction.pdf

The impact of e-service quality and customer satisfaction on customer behavior in online shopping.

https://scholarscompass.vcu.edu/cgi/viewcontent.cgi?article=1002&context=info_pubs

Modelling and testing consumer trust dimensions in e-commerce

A recent review on the application of business intelligence to the banking domain.Coverage of the last twelve years of scientific literature on those subjects.Usage of text mining and the latent Dirichlet allocation to analyze articles.Provide new insights and future research trends which may benefit banking business. This paper analyzes recent literature in the search for trends in business intelligence applications for the banking industry. Searches were performed in relevant journals resulting in 219 articles published between 2002 and 2013. To analyze such a large number of manuscripts, text mining techniques were used in pursuit for relevant terms on both business intelligence and banking domains. Moreover, the latent Dirichlet allocation modeling was used in order to group articles in several relevant topics. The analysis was conducted using a dictionary of terms belonging to both banking and business intelligence domains. Such procedure allowed for the identification of relationships between terms and topics grouping articles, enabling to emerge hypotheses regarding research directions. To confirm such hypotheses, relevant articles were collected and scrutinized, allowing to validate the text mining procedure. The results show that credit in banking is clearly the main application trend, particularly predicting risk and thus supporting credit approval or denial. There is also a relevant interest in bankruptcy and fraud prediction. Customer retention seems to be associated, although weakly, with targeting, justifying bank offers to reduce churn. In addition, a large number of articles focused more on business intelligence techniques and its applications, using the banking industry just for evaluation, thus, not clearly acclaiming for benefits in the banking business. By identifying these current research topics, this study also highlights opportunities for future research.

https://repositorium.sdum.uminho.pt/bitstream/1822/38304/1/BI_in_banking-review.pdf

Paulo Rita

Papers

A data-driven approach to predict the success of bank telemarketing

The internet, consumer empowerment and marketing strategies

The impact of e-service quality and customer satisfaction on customer behavior in online shopping.

Modelling and testing consumer trust dimensions in e-commerce

Business intelligence in banking