Inbound Information Technology: Personas of Prospective Students

Posted by John H. Heinrichs

Jun 22

As the Information Systems Management (ISM) department focuses on educating “students for careers within the information profession,” an important learning outcome is to “utilize the most relevant information technologies”.  One of the information technologies available for visualizing and understanding data is data mining.  Coupling this information technology with the Inbound Information Technology techniques enables ISM to better understand their prospective students.  Using data mining technology enables ISM inbound marketers to answer questions such as:

  • What are the clusters of prospective students?
  • Can ISM better target information to those identified groups?

Data Mining

The Data Mining add-ins for Microsoft Office contains various algorithms to help the inbound team understand the ISM prospective students.  Thus, having the ability to define and identify various clusters or market segments provides a very powerful tool to help increase enrollment in ISM. 

Using these algorithms permits the identification of groups based upon many dimensions, both envisioned and unseen.  Without using these algorithms, ISM is limited to analyzing just categories or clusters that can be imagined by the inbound team, some of which may be meaningless or irrelevant.  Thus, these unseen and unimagined categories or clusters can become very powerful for analysis and insight generation.  Further, the traditional categories that are usually imagined by the team typically contain data that is not actionable.


So, what is the clustering algorithm about and what can it provide?  Clustering allows the inbound team to learn more about the prospective students in order for to target specific messages to specific groups.  Thus, “the capacity for determining the common thread that holds (prospective students) together makes clustering a popular data mining technique.” [1]


Once the clustering is complete, the inbound team can label the various categories or clusters to convey meaning.  This label or persona helps focus content creation, landing pages, and offer strategies.  The identified personas become a description of the ISM prospective graduate, undergraduate, or MBA student.  By using the clustering features, the persona can become more detailed and permits the inbound team to more easily brainstorm offers, relevant keywords, and landing pages that can appeal to the prospective applicant.  [2]

Insight Generation Process

Trying to comprehend what the various clusters mean can be quite challenging.  This is especially difficult because “each cluster cannot be considered in isolation - clusters can only be understood given their relationship to all other clusters”. [1]  Key to understanding the meaning of the various clusters is to follow a simple insight generation process.  The basic process for gaining insight into the meaning of the clusters is composed of six steps. [1]  The steps are:

  1. get a high-level overview of the population,
  2. pick a cluster and determine how it is different from the general population,
  3. determine how the cluster is different from nearby clusters,
  4. verify that assertions regarding the cluster are true,
  5. provide a label for the cluster, and then
  6. repeat the process for the other clusters. 

Prospective MBA Student Clusters

Using the ‘fictitious’ scenario that contains information from 1,000 prospective students captured since January to gain insight into using data mining principles, the clustering algorithm was run.  Based upon the ‘fictitious’ data, nine categories were uncovered.  The inbound team scanned the overall population looking at the distribution of the various states based upon the attributes.  The various variables that were included in this scenario included desired academic focus, age, gender, academic degrees earned, and specialization area among others. 



Once the overall distributions were analyzed and the various clusters compared, the clusters were labeled.  The labeling was simply associated with key attributes of the cluster.  So, for example, a category was labeled “Information Management – Undergraduate Degree” as prospective individuals in this group predominantly were interested in the Information Management (ISM) concentration and possessed an undergraduate degree.  This is contrasted with the “Under 26” group were the individuals where primarily under 26 years old and had indicated that they had no specific concentration in mind.   


Learn More

The Information Systems Management (ISM) concentration at the Wayne State University (WSU) School of Business Administration (SBA) helps prepare you to become an information professional.  Specifically, at the MBA level, ‘ISM 7505: Information Analytics: Inbound Information Technology’ focuses on the details of ‘Inbound Marketing’.   Take a look!

Learn More About ISM! 


An important use of the clustering algorithm is to understand our target prospective graduate, undergraduate, and MBA students.  Based upon the clustering algorithm, eight categories of prospective students were identified.  Now that the groups are clearly identified and characteristics are detailed, specific messages can be created.  Further, specific ads can be developed to target interested individuals. 

  • What do you think?
  • Are you working on enhancing your inbound information technology and data mining skills? 
  • Will you be competitive in tomorrow’s environment?

Please start a discussion by leaving your comments and telling us what you think!


  1. Jamie MacLennan, ZhaoHui Tang, Bogdan Crivat, Data Mining with Microsoft SQL Server 2008. John Wiley and Sons. 
  2. Better Landing Pages Start With a Marketing Persona. Jenn Yorke. (Accessed May 6, 2013)

Topics: Excel, Inbound Information Technology, Data Mining

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