Early adoption of advanced analytics strategies and applications have occurred in the private sector for over a decade. The most extreme examples are “digitally native companies” like Amazon and Google. Both of these companies have emerged in and in many ways defined the digital age. They have built businesses around the idea of using data to better understand customer experience and customer intent. Advanced applications of analytics have been the catalyst for them to formulate this understanding and, most importantly, to act upon it.

Over time, we have seen tangible evidence to support the idea that analytics maturity correlates with better performance. Simply stated, advanced analytics creates a competitive advantage because it enables customer-centricity. These performance benefits have a multiplier effect over time as the more mature organizations invest and get better at it.

Revenue Chart

Revenue and Operating Income Growth by Analytics Maturity Stage (4 being most Mature)

The benefits are well known in general industry. According to a 2014 Accenture study:

  • 87% of organizations surveyed said they believe data analytics will redefine the competitive landscape of their industries.
  • 89% believed organizations that fail to adopt a data analytics strategy could lose both market share and momentum.

But why is that? The reason that organizations that are more analytically mature perform better is not simply because they invest more in analytics. It is because analytics builds real insight into what matters to the customer. Customer wants, needs and desires are better understood and anticipated.

The most prominent example in the private sector is seen when comparing Amazon and Sears. Two organizations that are going in different directions. One built for speed, digitally native and analytically savvy. The other is saddled by an antiquated business model and is constantly playing from behind.

Amazon LogoSears Logo
Interestingly, few organizations are leveraging data analytics to its fullest extent. The same 2014 Accenture study found that while many organizations invest in data analysis, just 13% are using big data analytics to predict outcomes, and only 16% are using analytics applications to prescribe what to do to alter those outcomes. The data suggests that a higher education institution could stand out from its competition and gain market share by using data analytics to make decisions.

Predictive Analytics for Higher Education

So why can’t higher education institutions see some of these same benefits? This question fuels Othot’s team on a daily basis.

Think about the higher education enrollment process. It begins with an institution acquiring a list of prospects, buying thousands of names from multiple sources, as well as implementing a variety of outreach programs. The dollars, effort and resources required to determine which students have a sincere interest in the school and then to convince them to enroll can be dramatically reduced by implementing a predictive analytics solution.

The concept of predictive analytics and big data is not new to the higher education market. Just a few years ago, some of the industry’s biggest buzzwords were data science, predictive analytics and big data.

Now colleges and universities are exploring how to use advanced analytics to gain a more nuanced understanding of each individual student and what motivates him to make a decision. They are using advanced analytics to focus on what they know about their individual students in a more advanced and integrated way so they can answer questions that will impact the future of their schools. Perhaps most importantly, schools that have embraced these advancements are seeing tangible results and an increasing number of these schools are Othot customers.

Think Amazon. Not Sears.

Tangible Proof Points from the 2017-2018 School Year

Examples of Customer Impact