LSM552: Analyzing Segmentation and Targeting

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1 LSM552: Analyzing Segmentation and Targeting Copyright 2012 ecornell. All rights reserved. All other copyrights, trademarks, trade names, and logos are the sole property of their respective owners. 1

2 This course includes: Three self-check quizzes Two discussions One tool to download and use on the job Two Ask the Expert interactives One video transcript file Completing all of the coursework should take about six to eight hours. What You'll Learn Articulate a strategic rationale for customer segmentation Articulate how segmentation fits into the process of developing marketing strategy Use data on bases and descriptor variables to group customers into segments Identify some of the most common algorithms used in segmentation Start Your Course Copyright 2012 ecornell. All rights reserved. All other copyrights, trademarks, trade names, and logos are the sole property of their respective owners. 2

3 In this course, you 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 analyze data similar to those typically provided by market research firms and answer segmentation and targeting questions; for instance, you analyze the data provided by a firm's website's browsing history and predict which segments are most attractive for a firm to target. You explore this content through a mix of input from industry experts, hands-on practical activities, and the presentation of sound principles by Cornell faculty. Your fellow students and our instructors also help broaden your understanding of the content and its impact on your organization. Meet the faculty for this course in the video below. Copyright 2012 ecornell. All rights reserved. All other copyrights, trademarks, trade names, and logos are the sole property of their respective owners. 3

Sachin Gupta Professor of Marketing, Cornell University Professor Gupta's research focuses on analytical models of marketing phenomena, including discrete choice models of consumer behavior,

4 Sachin Gupta Professor of Marketing, Cornell University 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 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. Start Your Course Copyright 2012 ecornell. All rights reserved. All other copyrights, trademarks, trade names, and logos are the sole property of their respective owners. 4

5 Module Introduction: 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 module, developed by Cornell University Professor Sachin Gupta, 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. Copyright 2012 ecornell. All rights reserved. All other copyrights, trademarks, trade names, and logos are the sole property of their respective owners. 5

6 Watch: Segmentation, Targeting, and Positioning 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. Copyright 2012 ecornell. All rights reserved. All other copyrights, trademarks, trade names, and logos are the sole property of their respective owners. 6

7 Watch: Bases and Descriptors 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. Copyright 2012 ecornell. All rights reserved. All other copyrights, trademarks, trade names, and logos are the sole property of their respective owners. 7

8 Tool: Project Worksheet At the end of this course, you will create a slide presentation to showcase how you will use the concepts from this course to address an issue in your organization. Use this worksheet throughout the course to help you gather your thoughts in preparation. Download The Tool Download the Project Worksheet Copyright 2012 ecornell. All rights reserved. All other copyrights, trademarks, trade names, and logos are the sole property of their respective owners. 8

9 Tool: Examine Bases and Descriptors Take some time now to apply what you have learned. In this exercise you will begin to design a segmentation exercise for a given product. To complete this activity: If you haven't done so already, download the project worksheet for this course. Follow the instructions to complete the first part, complete the examination. Save your work when you are done. Examine Bases and Descriptors. Use the scenario described below to This is the first part of the project worksheet. At the end of the course, you will use the completed project worksheet as you prepare the final course presentation. Scenario: Imagine that you are a manufacturer of consumer packaged goods, and a significant part of your business is laundry detergents. An important strategic initiative in your company is the development and launch of "sustainable innovations," products that have a significantly reduced environmental footprint compared with existing products. One such product under development is a laundry detergent specially formulated for use with cold water. Think about a segmentation exercise for this product in your country. Copyright 2012 ecornell. All rights reserved. All other copyrights, trademarks, trade names, and logos are the sole property of their respective owners. 9

10 Watch: Exploring Data Used for Segmentation Studies Copyright 2012 ecornell. All rights reserved. All other copyrights, trademarks, trade names, and logos are the sole property of their respective owners. 10

11 Tool: Examine Variables Take some time now to apply what you have learned about segmentation. In this exercise you will explore variables of segmentation for a given product. To complete this activity: Return to the project worksheet and complete the second part, below to complete the exercise. Save your work when you are done. Examine Variables. Use the scenario described At the end of the course, you will use your completed worksheet as you prepare the final course presentation. Scenario: Consider the following hypothetical situation facing Toyota. Sales of Toyota's small hybrid car, the Prius, have flattened because of intense and growing competition. A large number of new brands have entered since the Prius pioneered this market. Overall sales of hybrids have grown, but not as rapidly as originally predicted, in part because the retail price of gasoline has remained somewhat stable for more than 12 months. To rejuvenate Prius sales, Toyota would like to use the Prius website to identify segments of consumers in the market and to customize marketing messages to segments. Toyota would like to segment consumers based on the motivation to buy (or not buy) a hybrid vehicle, as well as their readiness to buy a hybrid vehicle (that is, at what stage of the buying process is the consumer?). Some possible motivations are environmentalism, fuel economy, value, styling and appeal, safety, and trendiness. To complete this exercise you will need to visit the Prius website and browse different pages, as if you were shopping for a car. You will also need to imagine you are a marketing manager at Prius. Copyright 2012 ecornell. All rights reserved. All other copyrights, trademarks, trade names, and logos are the sole property of their respective owners. 11

12 Watch: 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. Simply click the questions below for Gehrke's videorecorded answers. Johannes Gehrke 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. What is a transaction? Copyright 2012 ecornell. All rights reserved. All other copyrights, trademarks, trade names, and logos are the sole property of their respective owners. 12

13 What are key properties of a transaction? What's the difference between transaction processing and a data warehouse? Copyright 2012 ecornell. All rights reserved. All other copyrights, trademarks, trade names, and logos are the sole property of their respective owners. 13

14 What is a market-basket analysis? What is an example of a market-basket analysis? Copyright 2012 ecornell. All rights reserved. All other copyrights, trademarks, trade names, and logos are the sole property of their respective owners. 14

16 Module Introduction: 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. Copyright 2012 ecornell. All rights reserved. All other copyrights, trademarks, trade names, and logos are the sole property of their respective owners. 16

17 Watch: Cluster Analysis 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. Copyright 2012 ecornell. All rights reserved. All other copyrights, trademarks, trade names, and logos are the sole property of their respective owners. 17

18 Watch: Measures of Dissimilarity Measuring the Euclidean distance could be thought of as measuring the distance between two points on a graph as if you placed a ruler between them; it is computed by the square root of the sum of shared differences. Copyright 2012 ecornell. All rights reserved. All other copyrights, trademarks, trade names, and logos are the sole property of their respective owners. 18

19 Watch: Choosing Clustering Algorithms To further your understanding, download the additional reading, Clustering Algorithms, that Professor Gupta has provided. Copyright 2012 ecornell. All rights reserved. All other copyrights, trademarks, trade names, and logos are the sole property of their respective owners. 19

20 Watch: Examining Agglomerative Hierarchical Clustering Hierarchical clustering seeks to build a hierarchy of clusters. "Agglomerative" hierarchical clustering can be thought of as a "bottom-up" approach; pairs of clusters are merged as we move further up the hierarchy. Copyright 2012 ecornell. All rights reserved. All other copyrights, trademarks, trade names, and logos are the sole property of their respective owners. 20

21 Watch: Exploring Advantages and Disadvantages of Types of Data 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. Copyright 2012 ecornell. All rights reserved. All other copyrights, trademarks, trade names, and logos are the sole property of their respective owners. 21

23 Watch: Ask the Expert: Johannes Gehrke on Big Data and Data Preprocessing As we've seen, quality decisions come from quality data. Now that you've had a chance to explore segmentation and targeting and how this analysis helps marketers make strategic decisions, you are ready to consider how this work can be executed on a much broader scale. 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. Now you'll learn what data scientists mean by terms like "data preprocessing" and "representative subsets," and how these areas of big data analysis inform marketing decisions. Professor Gupta invites 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. Simply click the questions below for Gehrke's videorecorded answers. Johannes Gehrke 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. What's data preprocessing? Copyright 2012 ecornell. All rights reserved. All other copyrights, trademarks, trade names, and logos are the sole property of their respective owners. 23

24 Can you tell us more about data preprocessing? What do you do when you have missing values? Copyright 2012 ecornell. All rights reserved. All other copyrights, trademarks, trade names, and logos are the sole property of their respective owners. 24

25 How do you select the right attributes? What are representative subsets? Copyright 2012 ecornell. All rights reserved. All other copyrights, trademarks, trade names, and logos are the sole property of their respective owners. 25

27 Tool: Write a Memo to Your Boss Pull together what you have learned into a final practice exercise. This time, you will recommend a segment of buyers for a given product. To complete this activity: Return to the project worksheet and complete the third part, Write a Memo to Your Boss. Use the scenario located below. You may find it helpful to refer to Check exercises, Parts I and II, you completed earlier. Save your work when you are done. Review the completed worksheet and make any updates you desire. Save your work. This is the last part of the project worksheet. You will use the completed worksheet at the end of the course as you prepare the final course presentation. Scenario You are a marketing manager for the car manufacturer, Mini. Your task is to write a memo to your boss recommending which segment of car buyers Mini should target for the Mini Cooper. Copyright 2012 ecornell. All rights reserved. All other copyrights, trademarks, trade names, and logos are the sole property of their respective owners. 27

LSM554: Predicting and Managing Customers Lifetime Value

LSM554: Predicting and Managing Customers Lifetime Value Copyright 2012 ecornell. All rights reserved. All other copyrights, trademarks, trade names, and logos are the sole property of their respective