LSM552: Analyzing Segmentation and Targeting · PDF fileConsider the following hypothetical...

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LSM552: Analyzing Segmentation and Targeting


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


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.


Sachin Gupta , Cornell UniversityProfessor of Marketing

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.

Start Your Course


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.


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.


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 or example, they may have different needs and wants. Bases are-f

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.


Download The Tool

Download the Project Worksheet

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.

https://s3.amazonaws.com/ecornell/content/LSM/LSM552/LSM552_project-worksheet_us-en_01.doc


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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 for this course.project worksheet

Follow the instructions to complete the first part, . Use the scenario described below toExamine Bases and Descriptors

complete the examination.

your work when you are done.Save

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.

https://s3.amazonaws.com/ecornell/content/LSM/LSM552/LSM552_project-worksheet_us-en_01.doc


Watch: Exploring Data Used for Segmentation Studies


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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.


Return to the project worksheet and complete the second part, . Use the scenario describedExamine Variables

below to complete the exercise.


.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 website and browse different pages, as if you were shopping for aPrius

car. You will also need to imagine you are a marketing manager at Prius.

http://www.toyota.com/prius


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 (McGraw-Hill, 2002,Database Management Systems

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?


What are key properties of a transaction?

What's the difference between transaction processing and a data warehouse?


What is a market-basket analysis?

What is an example of a market-basket analysis?


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.


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.


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.


Watch: Choosing Clustering Algorithms

To further your understanding, download the additional reading, , that Professor Gupta has provided.Clustering Algorithms

https://s3.amazonaws.com/ecornell/content/LSM/LSM552/lsm552_clustering_algorithms_en-us.pdf


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.


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.


Watch: Identifying and Reaching Segments


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 (McGraw-Hill, 2002,Database Management Systems

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?


Can you tell us more about data preprocessing?

What do you do when you have missing values?


How do you select the right attributes?

What are representative subsets?


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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.


Return to the project worksheet and complete the third part, Use the scenario locatedWrite a Memo to Your Boss.

below. You may find it helpful to refer to Check exercises, Parts I and II, you completed earlier.


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.


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