Category Archives: Marketing Research

Does Academic Research Provide Practical Value for Social Media Marketers?

David Berkowitz ForresterThis post originally appeared in Social Media Marketing Magazine, Issue 1, Number 4.

Research you can use; four simple words that sum up my goal for this column. It is an honor and a pleasure to take over the responsibility for writing this column from Caroline Dangson (@CDangson). Caroline established this column as a must read. Her initial three articles, “Who Owns Social Media for Business”, “Co-Creating Value with Customers” and “Twitter is Ready for Advertisers, but are Advertisers Ready for Twitter?” presented thought provoking insight into emerging trends in social media marketing (SMM). Please join me in thanking her for developing a solid foundation on which to build.

Our goal for this column is to share with you cutting edge academic research that provides utility and assists in making your social media marketing research efforts easier. Academic research in marketing is not known for either its readability or its practicality. A few exceptions exist. Academic journals that are written with an eye towards readability and containing an applied focus include Business Horizons, California Management Review, Harvard Business Review and MIT Sloan Management Review. Interestingly, none are focused solely on marketing.

In my opinion, academic research has the potential to provide value to social media marketers IFF (if and only if) it provides instruction or insight into new research tools, methods, concepts and/or constructs. In the area of social media marketing research, in general, marketing academicians lag practitioners in understanding, developing and utilizing applied research tools and techniques. The good news is that this is changing. In addition to current marketing professors who are rapidly re-tooling, a new generation of marketing researchers is making its way through some of the top Ph.D. programs in the world and these researchers are engaged in and excited about SMM.

It is the research of this group, the denizens of SMM research, that we plan to review and share with you each quarter. The goal for this column is to add value to your SMM research skillset and tool kit. This issue, two must-read articles are highlighted and reviewed. The take-aways for each are clearly identified. Once you read this column, and the articles referenced, please come back to leave your comments/questions/suggestions because learning isn’t unidirectional.

The two, initial, journal articles for review are:

Hoffman, Donna L. and Marek Fodor (2010), “Can You Measure the ROI of Your Social Media Marketing?”, MIT Sloan Management Review, Vol. 52, No. 1 (Fall), pp. 41-49.


Hansen, Derek L. (2011), “Exploring Social Media Relationships”, On The Horizon, Vol. 19, No. 1,  pp. 43-51.

Can You Measure the ROI of Your Social Media Marketing?

Because this article has been out for a while, this is not the first review. A good overview of both the article and the importance of determining return on investment (ROI) in SMM is provided by Angela Hausman.  Hoffman and Fodor challenge SMM researchers to adopt a new approach:

“Effective social media measurement should start by turning the traditional ROI approach on its head. That is, instead of emphasizing their own marketing investments and calculating the returns in terms of customer response, managers should begin by considering consumer motivations to use social media and then measure the social media investments customers make as they engage with the marketers’ brands.” (p. 42)

They offer 4 C’s (instead of 4 P’s) to identify the key motivations for social media interaction: connections, creation, consumption and control as well as two case study examples of social media marketing failures. However, the real benefit (take-away) offered by this article is the sample metrics provided on page 44, some easy to operationalize and some more difficult to operationalize. The final gem provided by this article is a traditional 2 x 2 matrix entitled “Strategic Options for Social Media Measurement”. Using the sample metrics based on the strategic options available should provide a solid framework for developing a ROI unique to your business model.

Exploring Social Media Relationships

Hansen’s article offers immediate utility. In my opinion, he sums up the purpose of the paper brilliantly:

“This paper describes some of the techniques and tools needed to make sense of the social relationships that underlie social media sites. As relational data are increasingly made public, such techniques will enable more systematic analysis by researchers studying social phenomena and practitioners implementing social media initiatives”. (p. 44)

The paper documents the development and release of an incredible open source network analysis add-on for Excel. Hansen explains the concept of network analysis and social network analysis (SNA). The later builds on the former and is perfect for studying SMM relationships.

He justifies the study of SNA on page 45:

“Viewing the social world as a network can provide many insights not obtainable in any other way. Social network maps provide overviews of social spaces, highlighting subgroups and individuals that hold important positions within the network. Tracking changes in a network over time provides a powerful evaluation tool that measures previously hard-to-capture insights about social capital development, community formation, and marketing campaigns”.

The take-away from this article is the description of the power of the open source network analysis add-on for Excel: NodeXL. He even shares he is using NodeXL in his courses at the University of Maryland.  For those of you who don’t know me, my life revolves around open source. This add-on excited me so much that I purchased Microsoft Office just to use it (it won’t work in my beloved OpenOffice). After four days of playing with NodeXL, it is clearly a tool that you should explore in order to strengthen your SMM research skillset.

Future columns will be more succinct, but will follow the same format. Each quarter, two “must-read” academic research papers will be highlighted for your benefit. If you have any suggestions for future topics or manuscripts to review, please feel free to contact us.

Finally, since the goal of this column is to add value to your SMM research skills, we end with two questions: 1) Do you find this approach helpful? and 2) Did this column provide you with any take-aways that you can use?


New England in Fortune’s 100 Best Companies to Work For

CapeCod by laura padgettThe recent edition of Fortune Magazine (07 February) identifies the 100 Best Companies to Work For. Living in New England, it was easy to assume that the majority of firms identified were going to be local firms. For without question, we’re the best at innovation, entrepreneurship, creativity, information technology and marketing – just ask us!

The reality of the situation paints a less than optimistic picture for our region. Of the 100 Best Companies to Work For, only eight are located in New England.

New England in the Fortune Top 100 Best Comanpies to Work ForThree are in the top 50 (Boston Consulting Group, Stew Leonard’s and Bingham McCutchen). Congratulations to the Boston Consulting Group for being ranked second nationally. You really are our cash cow. Stew Leonard’s climbed the most over its 2010 ranking and Bingham McCutchen dropped the most. Four are from Massachusetts, two from Rhode Island and two from Connecticut. Missing are companies from New Hampshire, Vermont and Maine.

The eight New England companies identified employ a total of 26,488 people and have a total annual growth rate of -1 percent. Certainly we can do better. Check that – our long term sustainable economic viability requires that we do better. Make your company fun, rewarding and exciting to work for today. The next list comes out in 11 months. The time has come (to borrow an extreme sports cliche) to go big or go home.


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.