Category Archives: Marketing Research

Country-Level Segmentation Using K-Means Cluster Analysis

Cluster Plot

Using data from the International Monetary Fund World Economic Outlook 2009 and The Global Enabling Trade Report 2009 from the World Economic Forum, I thought that it would be interesting to develop macro-level country clusters based on three variables: percent growth in gross domestic product from 2008 to 2009 (GDPG), percent population growth from 2008 to 2009 (POPG) and an assessment of the openness of the country to business endeavors (OPEN: the Enabling Trade Index – ETI) from 2009. Complete data for 119 countries is available, attainable and used in this example.

Cluster analysis is a statistical technique that groups individuals (in this case, countries) into clusters so that the objects in the same cluster are more similar to one another, based upon the characteristics investigated, than they are to individuals in other clusters. All data was analyzed using the open source R statistical package. One of the limitations of cluster analysis is identifying the optimal number of clusters to develop. Using model-based cluster analysis resolves this problem by mathematically determining the optimal number of clusters. For this project, the optimal number of clusters is determined to be five.

On the basis of the k-means cluster analysis, with a target of five clusters as suggested by the model-based cluster analysis, distinct clusters emerge from the data with sizes of 25, 17, 17, 28 and 32. In theory, the opportunity for marketing and business in the countries contained within each cluster should be similar based on the growth (or constriction) of the overall economy, population growth and the overall business climate within the country.

Cluster Results

Cluster 1: Armenia, Austria, Bosnia and Herzegovina, Cote d’Ivoire, Cyprus, France, Gambia, Greece, Indonesia, Jamaica, Lesotho, Macedonia, Madagascar, Namibia, Paraguay, Philippines, Qatar, Senegal, Slovak Republic, Slovenia, South Africa, Switzerland, Uruguay, Venezuela, Zambia

Cluster 2: Bangladesh, Burundi, Cambodia, China, Eqypt, El Salvador, Ethiopia, Guyana, Honduras, Jordan, Malawi, Mauritius, Mozambique, Panama, Tajikistan, Tanzania, Uganda

Cluster 3: Australia, Chad, Kazakhstan, Korea, Kuwait, Mexico, Mongolia, New Zealand, Nigeria, Norway, Poland, Russia, Saudi Arabia, Sweden, Turkey, Ukraine, United Kingdom

Cluster 4: Algeria, Azerbaijan, Bahrain, Belgium, Brazil, Canada, Chile, Columbia, Croatia, Czech Republic, Denmark, Estonia, Finland, Germany, Hungary, Ireland, Italy, Latvia, Lithuania, Luxembourg, Moldova, Netherlands, Oman, Portugal, Romania, Spain, Taiwan, United Arab Emirates

Cluster 5: Argentina, Benin, Bolivia, Bulgaria, Burkina Faso, Cameroon, Costa Rica, Dominican Republic, Ecuador, Ghana, Guatemala, Hong Kong, India, Israel, Japan, Kenya, Kyrgyz Republic, Malaysia, Mali, Mauritania, Morocco, Nepal, Nicaragua, Pakistan, Peru, Singapore, Sri Lanka, Syria, Thailand, Tunisia, United States, Vietnam

What does this mean for global marketers? The results are mixed. Countries in the first cluster, on average, are growing in population, have respectable levels of openness for business but their macro-level economies shrunk by an average of 10.84 percent.

Countries in cluster 2 show growth in GDP and population, but have the lowest average score on openness for business. Countries in cluster 3 experienced the largest level of economic loss for the 2008-2009 period, on average 26.56 percent. Countries in cluster 4, on average, enjoy the highest levels of pro-international business policies (as measured by the ETI). Finally, the countries in cluster 5 have the second-best macro-level economies (on average), positive population growth but some trade policies that are edging towards protectionist, as indicated by their average ETI.

The benefits of using cluster analysis for macro-level segmentation are apparent. The more difficult task is determining how to use the results in a way that supports your market entry decisions or global business strategy. Clearly, the most telling result is that this type of blog post can only come from a self-confessed nerd with nothing better to do with his time.

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Marketing Metrics, Intelligence and Research: A Basic Overview

Marketing ResearchMarketing metrics, marketing intelligence and marketing research. Although the three are interrelated, important distinctions exist when comparing between them. Astute marketers use all three, in a coordinated effort, to guide their marketing efforts and to support the development of their strategic marketing plans. One commonality is that all three are used to support decisions that impact how firms organize and implement their marketing programs. A brief definition of the three is presented below.

Marketing Metrics

Simply defined, metrics refers to performance measures and operating statistics. Metrics are key performance indicators, allow firms to track performance over time and enable Salesforce.com dashboardgreater precision in execution of business activities. To provide practical value, metrics should identify frequency of measurement, frequency of review, source of data, rationale, and be logical. Metrics provide information about the current state of performance and operations. The metrics mantra is: “you can’t improve what you can’t measure”. Metrics serve as the firm’s dashboard.

Marketing Intelligence (aka Market Research)

The information that firms collect and analyze about the market(s) in which they operate, their current and potential customers and their competitors is collectively known as marketing intelligence. This information provides support for decisions regarding the development of appropriate marketing metrics, market opportunities and marketing strategy. Marketing intelligence provides a map of the marketing environment and identifies the landmarks and hazards. Marketing intelligence subsumes marketing metrics.

Marketing Research

Marketing research involves the systematic, objective collection and statistical analysis of data to turn it into actionable information. Marketing research serves as the GPS by providing information that guides the direction of the firm. Marketing research subsumes marketing intelligence. Using the scientific method as its foundation, marketing research consists of a series of steps that include:

 

  1. Problem definition
  2. Statement of the objectives
  3. Creation of the research design
  4. Choice of research method
  5. Sampling selection/plan
  6. Data collection
  7. Data analysis
  8. Interpretation of the results
  9. Develop the research report
  10. Follow-up/clarification

Accurately defining the problem is the key to conducting relevant marketing research. This task is more difficult than one might think given that symptoms, rather than problems, are the evidence provided by the metrics. For instance, a decline in gross sales volume is a symptom rather than a problem. Before beginning the marketing research process, one must identify the potential underlying cause(s) of the symptom. Researching symptoms rather than problems provides information that possesses limited utility.

Failure to conduct marketing research is one of the top reasons for business failure, especially when considering market entry alternatives. The key is to structure the research so that the benefits received outweigh the costs associated with the research process.

In summary, the three are not mutually exclusive decisions or tools in the marketing arsenal: they are interrelated. By utilizing all three in a cohesive manner, marketers can manage their marketing efforts to ensure maximum impact. Coordinating metrics with marketing intelligence and marketing research provides marketers with the dashboard, map and GPS needed to drive success.

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Price, Add-ons and Graphics: In My Opinion the Statistical Program R Kicks SPSS’ SAS

Tired of paying the initial cost, annual licensing fee and for each add-on package for your statistical analysis software? It’s time for you to switch to R, an incredible open-source program for statistical analysis and graphics. R was developed by Ross Ihaka and Robert Gentleman at the University of Aukland, New Zealand in 1993. It has become the statistical analysis software of choice for statisticians, financial analysts and economists. Marketing researchers, and business schools in general, have been slow to adopt R. Thankfully, this is changing.

As an avid user of open source (Linux Mint operating system, OpenOffice, Firefox, etc.), I made the switch to R two years ago when I started teaching marketing research again. Both my undergraduate and graduate marketing research classes utilize the R software package for data analysis. Five reasons for you to make the switch to R are presented below.

Reason 1: Price

R is free. I know that my students will be able to afford to use the software after they graduate. In addition, each add-on module for specialized statistical analysis is free. To date, there are more than 2,437 add-on packages available, including structural equation modelling, model-based cluster analysis and lattice graphics.

Reason 2: Multi-Platform Usability

R works in Windows, Mac and Linux. This removes any excuses that students can offer about software compatibility.

Reason 3: Graphics

R’s capability for generating graphics is unparalleled. The ability to incorporate colors, graph in three dimensions and in some packages, grab and rotate the graphic for different views makes R the king of the statistical analysis software in this category. Two examples are provided: a three dimensional rabbit (in color) with individual data points highlighted and a three-dimensional graph from a model-based cluster analysis.

Reason 4: Ease of Data Import and Export

R makes it simple to import data in multiple formats. My students enter data in Microsoft Excel or in OpenOffice Spreadsheet and import what they need by using the copy and paste functions. My preference is to import data from a spreadsheet as a .csv file. R allows you to import SPSS or SAS datasets and export to these same formats.

Reason 5: As Part of the Open Source Community, R is Continuously Improving and Expanding

Since 1997, updates to R are managed by the R Core Development Team, working collaboratively from all over the globe to contribute code, debug, provide documentation and develop add-ons.

It is this open source approach, managed by some of the top statistical and scientific talent available, that makes R so robust and so appealing.

Summary

No mistake about it, R is old-school cool. Users have to learn to utilize command lines in their statistical analysis. Seasoned marketing researchers, like me, were taught how to do this in SAS, BMDP and SPSS before they became menu-driven packages. New users face a steep learning curve, but the effort pays off in the end. For once you understand the commands in R, switching to SPSS or SAS is a walk in the park. And the python extension in SPSS allows users to run the plethora of statistical add-ons available in R.

So how do you get started? Visit the R-Project website and learn as much as you can about the R statistical software. Then go to the Comprehensive R Archive Network to download the latest release (currently R 2.11.1). Install R on your computer and begin the relationship. In my opinion, the best source of information for adapting to R as a former SPSS or SAS user is the Quick-R website by Robert I. Kabakoff.

No excuses remain. Join us in using R as your statistical analysis platform or become obsolete in marketing research. The choice is yours.

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