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In this course from the Johnson School of Management, you will use cluster analysis to divide the market based on customer needs and preferences. This helps you identify and target the segments with the greatest potential for profitability. Through dynamic activities, you will analyze data similar to those typically provided by market research firms and answer segmentation and targeting questions; for instance, you will analyze the data provided by a firm’s website browsing history and predict which segments are most attractive for a firm to target.
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|Section 1: Exploring Segmentation Bases and Descriptors|
As marketers, we want greater market share and increased profitability. To achieve these goals, we use data analysis to answer two critical questions: Who is your target customer? And what unique benefit does your product offer relative to competing products? In this course, developed by Professor Sachin Gupta of the Johnson School of Management at Cornell University, you examine the process of segmenting the market and targeting the right customers so your marketing strategies yield better returns. In the context of segmentation and targeting, you also want to develop an understanding of "big data," a popular concept that's currently attracting much attention. Analyzing big data requires specialized software, but the underlying theories of how to approach and analyze it are the same as those used here to analyze smaller data sets. You'll hear from Cornell University Tisch Professor Johannes Gehrke about why big data are important, and how an analysis of big data can help marketing professionals with segmentation and targeting.
Professor Gupta's research focuses on analytical models of marketing phenomena, including discrete choice models of consumer behavior, marketing mix models, measurement of returns on marketing investments, pricing, promotions, and advertising decisions, channel relationships, and so forth. He is especially interested in the prescription drug and consumer goods industries.
In 2008 one of Professor Gupta’s papers received the O’Dell award of the American Marketing Association. This award is given to the authors of the best article published in the Journal of Marketing Research five years before. Professor Gupta also received the Paul Green award of the American Marketing Association in 2003. In 2007, he received the Cornell Hospitality Quarterly's best paper award for his article on customer satisfaction in the restaurant industry. Professor Gupta serves on the editorial boards of Marketing Science and the Journal of Marketing Research.
At Johnson, Gupta teaches the core Marketing Management course, as well as a popular elective course called Data Driven Marketing. In 2009, he received the Stephen Russell Distinguished Teaching Award, given by the Johnson class of 2004, at their fifth reunion. The 2007 graduating MBA class selected him to receive the Apple Award for Teaching Excellence. Gupta previously taught at the Kellogg School of Management at Northwestern University, where he received the Sidney Levy Award for teaching excellence.
In this video, Professor Gupta explains that because customers vary, it is difficult to market products or services that try to be all things to all people. Instead, marketers try to see how certain groups of people are similar (homogeneous) and others are different (heterogeneous). These groups should be fairly homogeneous within each group, but heterogeneous from one group to the next. People will often pay more money for things that exactly meet their needs or that have a specific appeal (and when the additional amount that they pay is greater than the cost to find the exact match).
For existing brands, it is especially useful if the needs and benefits used for segmentation can be mapped to the product’s value proposition. For instance, if you have a drug whose primary differentiating advantage is that it works quickly, the segmentation will be useful if you use that characteristic as one of the needs or benefits that you use to segment.
In this video, Professor Gupta discusses bases variables and descriptor variables, which help define segments. You must understand these two terms to successfully complete this module. "Bases" are needs, motivations, and preferences; "descriptors" refer to demographics, psychographics, geographic locations, and other characteristics. Bases tell us why customers will respond differently to a given offering—for example, they may have different needs and wants. Bases are often hard to observe, except via market research done with a sample. With some effort, however, descriptors can be observed.
The most appropriate basis for segmentation depends on the managerial reason for the segmentation. For example, for positioning studies for existing products, appropriate bases may be benefits sought, product use, or attribute preferences; for a new-product concept, reaction to the new concept may be the appropriate basis. The general approach is to create a segmentation framework using a sample, and then apply it to the population at large by using descriptors.
|Lecture 4||5 slides|
Course Project Part I: Interpreting Clusters
|Section 2: Ask the Expert: Johannes Gehrke on Big Data and Segmentation|
Now that you've had a chance to explore segmentation studies and the information they yield, you are ready to consider how that analysis may be executed on a broader scale. What do marketers need to know to discuss big data knowledgeably in terms of segmentation? The work you're doing in this course will provide you with the foundation you need to understand how analysts measure and analyze big data. Professor Gupta invites Cornell University professor Johannes Gehrke to discuss some of the most commonly asked questions about big data and how they relate to the study of advanced marketing research.
Question for the expert: What is a transaction?
Johannes Gehrke is the Tisch University Professor in the Department of Computer Science at Cornell University. Gehrke's research interests are in the areas of database systems, data science, and data privacy. He has received a National Science Foundation Career Award, an Arthur P. Sloan Fellowship, an IBM Faculty Award, the Cornell College of Engineering James and Mary Tien Excellence in Teaching Award, the Cornell University Provost's Award for Distinguished Scholarship, a Humboldt Research Award from the Alexander von Humboldt Foundation, the 2011 IEEE Computer Society Technical Achievement Award, and the 2011 Blavatnik Award for Young Scientists (from the New York Academy of Sciences). He co-authored the undergraduate textbook Database Management Systems (McGraw-Hill, 2002, currently in its third edition), used at universities all over the world. He is also an adjunct professor at the University of Tromsø in Norway. Gehrke was co-chair of the 2003 ACM SIGKDD Cup, program co-chair of the 2004 ACM International Conference on Knowledge Discovery and Data Mining (KDD 2004), program chair of the 33rd International Conference on Very Large Data Bases (VLDB 2007), and program co-chair of the 28th IEEE International Conference on Data Engineering (ICDE 2012). From 2007 to 2008, he was chief scientist at FAST, a Microsoft subsidiary.
|Section 3: Analyzing Data to Divide the Market|
When we talk about segmenting and targeting customers, we're looking at analytical methods of classifying consumers into groups based on similar needs and preferences. How do we perform this statistical analysis? And how do we look for patterns in the data that will be meaningful from a business perspective? In this module, developed by Cornell University professor Sachin Gupta, you examine how you can divide the market meaningfully based on customer needs and preferences, so that you can identify and target the segments with the greatest potential for profitability.
You'll also hear again from Professor Gehrke, who explains how big data can be an important part of this conversation. You'll learn what to consider in terms of selecting the right attributes, what "data preprocessing" is, and what "representative subsets" are. Increasingly, marketing professionals are expected to have a well-rounded awareness of the terms being used in discussing big data.
As Professor Gupta explains in this video, cluster analysis is a statistical technique commonly used for segmentation. It classifies a set of "observations" (customers or prospects) into mutually exclusive, unknown groups based on several variables or shared properties.
In this video, Professor Gupta introduces some of the considerations in choosing a clustering method, and he describes how a hierarchy of clusters can be reached. You want to develop a well-rounded understanding of how analysts use consumer data to segment the market. You may not be the person who actually performs the computations described here, but you may be in a position to commission this analysis from a vendor. You should understand what the data can do for you and how these analyses inform segmentation and targeting decisions.
|Section 4: Ask the Expert: Johannes Gehrke on Big Data and Data Preprocessing|
|Section 5: Course Project and Wrap-up|
Course Project Part II: Profiling Clusters
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