Disruptive Technology in Higher Education 2014

Technology is seen as an important tool in reducing costs and improving educational outputs. Dust covered books, professors walking hallowed hallways donned in tweed jackets, and scholarly debates in the backrooms of higher educational institutions seem to be fading away into history. Perhaps someday we will talk about “traditional education” as an ancient educational system practiced relatively unchanged since the Dark Ages. New technology will adjust and transform the cost structure of higher education and provide greater access to a diverse group of individuals. Tradition may not be over but is likely to change to the Information Age. 

I was reading through an article entitled 10 Hottest Technologies in Higher Education and was impressed by the changes higher education will experience this year. Changes ranged from campus wide Wi-Fi to Small and Private Online Courses (SPOCS) that are likely to put pressure on higher education institutions, in the same way as online education has done over the past decade. 

 Strategically I view the Data Measurements and Campus Wi-Fi as some of the more interesting and influential topics this year. Data measurements gauge university performance will be debated as universities and government consider the overall benefits and detractors. Likewise, Campus Wi-Fi appears as an attempt to modernize aging campuses while providing case examples for city-wide Wi-Fi implementation. 

Data Measurements offer a promise to improve transparency of universities to government, administrators, and students. It may actually improve transparency but generally only in the realm of data it plans to collect.  In science, the type of information and data points chosen will impact the ranking of universities on this score card; it will also leave many important educational factors out of the mix. 

The difficulty in using rankings that provide broad enough measures that can fairly be applied across multiple types of universities is important. If such measurements are based in ensuring the status quo is maintained then there can be little complaint if the entire educational system becomes so expensive it collapses. We, as a society, justified our beliefs through selective data input but we ignored the necessity of higher education change. 

We can put this in a proper perspective. If the goal is to get students employed after graduation, than any old minimum wage job at your local fast food chain will be fine for fulfilling that goal. If the goal is to find employment that is sustainable and offers a living wage, then the data a researcher would select would inherently be different. Numbers are only representations that tell part of the story. Even though they seem concrete they can socially negotiated.

The other important idea is campus wide WI-FI. As more courses, resources, and lives of individuals take a virtual turn it is important to feed the need to stay connected. Offering campus wide WI-FI encourages this interconnected nature and speeds up information transference. It also forces universities to see themselves in both the physical and virtual realms. 

It is more than the physical implementation of Wi-Fi that counts. It’s a mental shift that traditional education is tied to the future of technology. People may attend a campus but may be encouraged to take more online courses, access the library online, and spend a portion of their education in the virtual world.  Why build an expensive new building when you can build an online course?

Whether we agree or disagree with the changes they are here to stay. That doesn’t mean that new technology won’t someday come to replace what is considered disruptive today but that higher education will be shifting to a new model from those used by our forefathers. Technology has always created shifts in society and will do so in government in much the same way as it is doing in higher education. Technology can only be delayed but not stopped. 

The SlideShare presentation offered is worth viewing:

Ten Hottest Disruptive Technologies in Higher Education from Vala Afshar

Afshar, V. (11/04/2014). 10 Hottest Technologies in Higher Education. Huffington Post. Retrieved  http://www.huffingtonpost.com/vala-afshar/10-hottest-technologies-i_b_6089740.html

 

Using Customer Profiles to Enhance Service and Product Marketing

Customers are the lifeblood of any business. Understanding the unique and rich data that comes from their core customer base helps in creating services that truly meet the needs of those customers as well as marketing the most relevant programs to the most interested parties. It creates a higher level of sales and satisfaction spurred by the interconnectivity of customers and the organization. The customer’s needs are better fulfilled with the offering of products and services they are actually interested in. Precisely how this is done is a process that can be learned and adapted.

With the advancement of the Internet and e-commerce the use of social research to understand customer behavior becomes possible. With the increase in customer data it is possible to create greater data mining and clustering of customer profiles to understand buying patterns and behaviors (Prasad & Malik, 2011). It is through the development of higher levels of data analysis that services can become more effective and beneficial. 

Let us look at an example. Analysis of a large database finds that customers who bought airplane tickets also purchased beach related products. Yet what if these customers were also found to purchase more outdoor gear and spent a greater amount of money on outdoor activities? It would be possible to build a customer profile based upon their exploratory and thrill seeking behavior. 

In order to understand unique social purchasing behaviors requires the categorization and analysis of profile customers. It requires a method of making meaning out of the historical data (i.e. purchases over time) being presented. Qian et. al. (2006 suggests the following:

• 1.)    Standardize profiles
• 2.)    Screen out uninteresting profiles
• 3.)    Using basic functions to categorize profiles
• 4.)    Apply algorithms to the categorizations
• 5.)    Identify unique profiles for further analysis

Once the profiles are standardized it is possible to categorize their behavior into clusters. These clusters are used for additional analysis and the determining of patterned behavior. That patterned behavior indicates that there are latent psychological functioning occurring and it would be beneficial to use multiple analysis methods to better highlight their behavioral thought processes. 

This process is fairly accurate and can lead to better marketing techniques based upon profile attributes and responses to previous marketing (i.e. previous purchases).  One simply needs to draw connections between the different sets of data and tests that were conducted over time. A study by Leung (2009) found that out of 1,500 profiles analyzed that 91.73% of customer profiles were segmented correctly. 

High levels of accuracy and a process for separating and analyzing consumer behavior is a benefit that organizations should not ignore. The use of more pin pointed marketing techniques further encourages efficient use of company resources by ensuring that products are actually of interest to the customer. Social research techniques can help identifying latent psychological functions that further enhance organizational profits.

Leung, C. (2009). An inductive learning approach to market segmentation based upon customer profile attributes. Asian Journal of Marketing, 3 (3). 

Prasad, P. & Malik, L. (2011). Generating customer profiles for retail stores using clustering techniques. International Journal on Computer Science & Engineering, 3 (6). 

Qian, Z. et. al. (2006). Churn detection via customer profile modeling. International Journal of Production Research, 44 (14).