The current study is designed as a part of the proposal entitled: Initiative for Low-input Apple Production in Illinois. Major difficulties currently facing apple production in Illinois are competition from lower cost producers in more favorable climates and the use of traditional production technologies which require intensive use of pesticides. Recently, the successful use of biotechnology has resulted in the development of new disease-resistant apples varieties. The objectives of the study are to learn about consumer preferences for such apple attributes as place of production (locally grown versus non-locally grown), method of production (genetically modified versus conventionally produced) with respect to different pricing, and to determine whether locally grown genetically modified apples would be appealing to the consumer. Consumers responding to the online survey were asked to rate hypothetical apples and later choose a preferred apple alternative from multiple sets, each consisting of three choices. Using the conjoint analysis technique, the study analyzes two versions of the survey: one referred to the choice of genetically modified apples as "GM", while the other version referred to that choice as "REI" (reduced environmental impact). The study results conclusively prove that consumer preferences for apples are influenced by place and method of production attributes. Although price remains one of the dominant attributes, it may play a different role for consumers who are willing to pay a premium for locally grown apples with combination of benefits provided by the method of genetic modification. The study also contributes to the literature on the issues related to the labeling language of GM food. The study results suggest that labeling language has to be carefully worded. When apples are labeled as "GM", respondents value this attribute negatively, while labeling apples as "REP" results in a positive valuation of the same attribute. It can be concluded that labeling should reflect both: the method (GM) and the specific benefits it could provide. As a result, consumers would have more information to make a desirable and affordable choice among products.

Conjoint analysis is probably the most significant development in marketing research in the past few decades. It can be described as a set of techniques ideally suited to studying customers’ decision-making processes and determining tradeoffs. Though this book is oriented towards methods and applications of conjoint analysis in marketing, conjoint methods are also applicable for other business and social sciences. After an introduction to the basic ideas of conjoint analysis the book describes the steps involved in designing a ratings-based conjoint study, it covers various methods for estimating partworth functions from preference ratings data, and dedicates a chapter on methods of design and analysis of conjoint-based choice experiments, where choice is measured directly. Chapter 5 describes several methods for handling a large number of attributes. Chapters 6 through 8 discuss the use of conjoint analysis for specific applications like product and service design or product line decisions, product positioning and market segmentation decisions, and pricing decisions. Chapter 9 collates miscellaneous applications of marketing mix including marketing resource allocation or store location decisions. Finally, Chapter 10 reviews more recent developments in experimental design and data analysis and presents an assessment of future developments.

Seminar paper from the year 2011 in the subject Communications - Public Relations, Advertising, Marketing, Social Media, grade: 1,3, AKAD University of Applied Sciences Stuttgart, language: English, abstract: "Conjoint analysis has become one of today’s most widely used marketing research tools. It goes beyond simple surveys, providing a more realistic approach to understanding customers attitudes, opinions, and behaviors." (Orme, 2010, p. 7) The author Orme (2010, p. 7) emphasises in his book the growing popularity of conjoint analysis in marketing research. According to Orme (2010, p. 1) the consumer preferences are changing constantly with an increasing speed. Therefore many marketing managers ask themselves, how they could asses client preferences? Which product characteristics are most important to the customer and what price brings the maximum profit? From Wilcox’s (2003, p. 1) point of view conjoint analysis gives answers to these most critical questions of marketing research. The conjoint analysis is a marketing research technique designed to help managers determine the value system of clients and potential customers (Wilcox, 2003, p. 1). Introduced as a fundamental measurement method by the mathematical psychologists Luce and Tukey (1964, p. 1) more than forty years ago, conjoint analysis presents combination of features in product profiles and ask people to rank or make choice among of them. Finally, the results can be used for new product design, targeting, pricing and market segmentation (Dolan, 1990, p. 1). However, there arises the question what is conjoint analysis really and why it has become so popular in contrast to other marketing research techniques? In order to answer these questions, section 2.1 defines the terms marketing and market research and outlines the contrast between them. Section 2.2 gives an overview of different preference measurement techniques before it deals with the conjoint analysis itself. Chapter 3 presents the main chapter of this assignment. At first, it gives a brief overview of the role of conjoint analysis in the marketing concept. The next two sections illustrate an exemplary conjoint analysis survey and show the usage of conjoint analysis for the design of marketing strategies. Moreover chapter 4 discusses the advantages and disadvantages of conjoint analysis. Finally chapter 5 summarizes the basic insights and gives a short perspective.

Now, a leader of Northwestern University's prestigious analytics program presents a fully-integrated treatment of both the business and academic elements of marketing applications in predictive analytics. Writing for both managers and students, Thomas W. Miller explains essential concepts, principles, and theory in the context of real-world applications. Building on Miller's pioneering program, Marketing Data Science thoroughly addresses segmentation, target marketing, brand and product positioning, new product development, choice modeling, recommender systems, pricing research, retail site selection, demand estimation, sales forecasting, customer retention, and lifetime value analysis. Starting where Miller's widely-praised Modeling Techniques in Predictive Analytics left off, he integrates crucial information and insights that were previously segregated in texts on web analytics, network science, information technology, and programming. Coverage includes: The role of analytics in delivering effective messages on the web Understanding the web by understanding its hidden structures Being recognized on the web – and watching your own competitors Visualizing networks and understanding communities within them Measuring sentiment and making recommendations Leveraging key data science methods: databases/data preparation, classical/Bayesian statistics, regression/classification, machine learning, and text analytics Six complete case studies address exceptionally relevant issues such as: separating legitimate email from spam; identifying legally-relevant information for lawsuit discovery; gleaning insights from anonymous web surfing data, and more. This text's extensive set of web and network problems draw on rich public-domain data sources; many are accompanied by solutions in Python and/or R. Marketing Data Science will be an invaluable resource for all students, faculty, and professional marketers who want to use business analytics to improve marketing performance.