Data Mining - Concepts 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.

Digital marketing: A framework, review and research agenda

https://www.yupingliu.com/files/papers/gensler_managing_brands.pdf

Managing brands in the social media environment

Online chatter, or user-generated content, constitutes an excellent emerging source for marketers to mine meaning at a high temporal frequency. This article posits that this meaning consists of extracting the key latent dimensions of consumer satisfaction with quality and ascertaining the valence, labels, validity, importance, dynamics, and heterogeneity of those dimensions. The authors propose a unified framework for this purpose using unsupervised latent Dirichlet allocation. The sample of user-generated content consists of rich data on product reviews across 15 firms in five markets over four years. The results suggest that a few dimensions with good face validity and external validity are enough to capture quality. Dynamic analysis enables marketers to track dimensions' importance over time and allows for dynamic mapping of competitive brand positions on those dimensions over time. For vertically differentiated markets (e.g., mobile phones, computers), objective dimensions dominate and are similar acr...

Mining Marketing Meaning from Online Chatter: Strategic Brand Analysis of Big Data Using Latent Dirichlet Allocation:

Market structure analysis is a basic pillar of marketing research. Classic challenges in marketing such as pricing, campaign management, brand positioning, and new product development are rooted in an analysis of product substitutes and complements inferred from market structure. In this article, the authors present a method to support the analysis and visualization of market structure by automatically eliciting product attributes and brand's relative positions from online customer reviews. First, the method uncovers attributes and attribute dimensions using the “voice of the consumer,” as reflected in customer reviews, rather than that of manufacturers. Second, the approach runs automatically. Third, the process supports rather than supplants managerial judgment by reinforcing or augmenting attributes and dimensions found through traditional surveys and focus groups. The authors test the approach on six years of customer reviews for digital cameras during a period of rapid market evolution. They...

Automated Marketing Research Using Online Customer Reviews

Needs-based analysis lies at the intersection of product marketing and new product development. It is the study of why consumers purchase and what they do with those purchases. In a world of mass-customization and one-to-one marketing, anticipating the customer's needs is a key competitive advantage. In this paper, we consider a new approach to supplement traditional methods for assessing rapidly changing user needs. We model the knowledgebase of online customer reviews as a matrix of reviews relating customer needs to product attributes. In a hierarchical two-stage process, we first use association rules to cluster related attributes and needs into hyper-edges. In a second application of association rule mining, we search for hyper-rules relating hyperedges. The method is demonstrated on 10,500 customer reviews over two unrelated product domains, digital cameras and vacuum cleaners.

Needs-based analysis of online customer reviews

The traditional product design process begins with the identification of user needs (Ulrich and Eppinger 2008) Traditional methods for needs identification include focus groups, surveys, interviews, and anthropological studies In this paper, we propose to augment traditional methods for identifying user needs by automatically analyzing user-generated online product reviews Specifically, we present a supervised, machine learning approach for sentential-level adaptive text extraction and mining Based upon a set of 9700+ digital camera product reviews gathered in January 2008, we evaluate the approach in three ways First, we report precision and recall using n-fold cross-validation on labeled data Second, we compare the recall of automated learning with respect to traditional measures for identifying users and their respective needs Third, we use multi-dimensional scaling (MDS) to visualize the competitive landscape by mapping existing products in terms of the user needs that they address

Automatically Learning User Needs from Online Reviews for New Product Design

The first step in product design and development involves concept generation. Concept generation involves identifying customer needs and then mapping those needs onto a set of product attributes (specifications). Traditional methods for concept generation involve focus groups, surveys, and anthropological studies to assess user needs. Techniques, like Quality Function Deployment (QFD), then guide designers in relating needs to explicit product specifications. In this paper, we propose to augment traditional methods for concept generation by automatically processing user generated online product reviews. We apply adaptive text extraction methods to automatically learn user needs and product attributes. Association rule mining is used to learn the mapping between needs and attributes. We summarize results from prior work for independently learning user needs and attribute specifications from product reviews and then discuss the application of these methods to concept generation for new product development.Copyright © 2009 by ASME

Adaptive Text Extraction for New Product Development

This abstract summarizes a propsal to use semistructured constraints as a general framework for information extraction. The proposal builds on the intuition that written documents tend to contain a great deal of redundancy: Tell them what you are going to tell them, tell them, tell them what you told them. While current methods overlook or eliminate redundancy, we seek to explicitly represent and exploit both repetition and other natural patterns in semistructured text. We summarize our early research on verification, outline current and future directions, and identify related work. Our initial work assumes knowledge of both document structure and its attendant constraints for the purpose of extraction verification. Specifically, semantic and structural constraints are used to check wrapper accuracy. For example, a structural constraint might ensure that the parties to a contract are introduced at the beginning of the document. A foreign-key constraint checks whether the parties are included on a list of approved signers. That signatories are repeated at the end of a contract is represented by an inverse constraint. A wrapper to extract seven attributes from legal arbitration decisions was applied to 200 cases. Constraint-based verification produced only ten false negatives, the majority of which were due to errors in wrapper delimiters. Further results were reported earlier [Hunter, et al., 2002]. We are currently experimenting with semistructured constraints in hierarchical extraction. Documents with the same semantic and structural constraints are placed in the same class. Class-specific routines fragment the document, extract from each fragment, and recurse on selected components. We propose the use of semistructured constraints as a framework for both fragmentation and extraction. For example, in addition to simple features like HTML-nesting, constraints can dictate whether certain values or structural properties functionally determine other fragments. A dissenting opinion must correspond to a legal decision with more than one judge.

Thomas Y. Lee

Papers

Automated Marketing Research Using Online Customer Reviews

Needs-based analysis of online customer reviews

Automatically Learning User Needs from Online Reviews for New Product Design

Adaptive Text Extraction for New Product Development

Constraint-driven hierarchical information extraction.