As we navigate 2026, administrators and marketing professionals at higher education institutions across the U.S. are up against immense challenges.
Colleges and universities are facing stiffer competition than ever before. There is the threat of an “enrollment cliff” of significantly reduced college-aged prospects that will mean more schools vying for fewer students.
There is also a concerning sentiment of diminished public trust in higher education, making a degree a tougher sell for some consumers and their children.
A 2024 Gallup poll found that nearly as many U.S. adults have little or no confidence as have high confidence.
Virtually every industry has also been impacted by the rapid evolution of AI, and education is no exception. From usage in the classroom to new degree programs, AI has become a mainstay on campuses across the country.
Thankfully, advanced analytics research can help fill the information gaps left by these issues and set higher education institutions up for success.
Advanced analytics in higher education go beyond the surface with sophisticated approaches to support the research needs of an institution.
An online survey is typically leveraged to generate proprietary results for the institution. Once the data has been accumulated, a professional researcher or data scientist will use the appropriate software or tools to transform the data into actionable insights.
Methods like conjoint analysis, segmentation, MaxDiff, key driver analysis, TURF analysis, and pricing research are common examples of advanced analytics you might see. Each of these approaches are designed to collect particular data as inputs for the analyses.
Market research is a common tool to help colleges and universities, but advanced analytics can elevate decision-making even higher. These are not simple surveys that only ask about satisfaction or directly what is most important to students.
With the more advanced methods, the research can go further and be more meaningful. Here’s how:
A key difference is capturing the derived importance of something versus the stated importance. Several advanced analytics approaches do this so you are not only relying on what students say, but also seeing which underlying factors are actually influencing their responses.
Another advantage of advanced analytics in higher education is the use of trade-off decision making. Methods like conjoint analysis and MaxDiff force respondents to think about what they are willing to give up to gain something else. This approach more closely mimics real-life decision making. In turn, you achieve more reliable and actionable results.
In general, advanced analytics also lets you view data from multiple angles. Traditional research allows you to cut the data with crosstabulations, but this only gets you so far. Advanced studies may equip you with simulators, data models, and calculators to go deeper. These dynamic outputs can help colleges and universities make decisions based on one study for years.prototype, taste a food product, or test a complex hardware interface.
You might be wondering what you gain by going the extra mile with advanced analytics in higher education. Below are some of the key benefits a college or university could experience by investing in a study.
By sponsoring your own advanced analytics study, your institution will be able to rely on primary data versus hunches, anecdotes, or outdated information.
The knowledge achieved through an advanced analytics project will often improve the bottom line for a school. Research outcomes are directly related to business objectives so that organizational leaders can make more effective decisions.
Instead of just waiting for year-end reviews for institutional processes and results, an advanced analytics study can be conducted in under a semester for more agile decision-making. Colleges and universities can implement changes for the following semester or year depending on their needs.
The other timing benefit to advanced analytics projects is the sheer volume of insights from a single study. You often walk away with a treasure trove of data to aid decisions throughout the year or beyond. Instead of having to run another study, you can just run queries within the results already in hand.
Many advanced studies support a higher education institution in reducing waste of school resources.The findings can inform decision-makers which program and campus offerings will generate the most return from an investment standpoint.
Advanced analytics may also contribute to the creation of a sustainable business model for long-term growth with optimal pricing recommendations.
With students as the lifeblood of higher education institutions, consider advanced analytics to understand what they truly think and find important about their experience. Insights from an advanced study can directly impact retention rates for existing students.
This type of research can also improve applicant conversion rates of prospective students. You can uncover which offerings would be the most enticing or which messaging is most likely to resonate with prospects.
A wide array of data can be utilized in an advanced analytics study in higher education.
Decision-making factors for choosing a school are tried and true inputs. For example, a key driver analysis can take this data and help you understand which aspects of the student experience have the greatest impact on an enrollment decision or student satisfaction.
The degrees offered by an institution are also commonly used in advanced research. A TURF analysis (Total Unduplicated Reach and Frequency) can be used to determine the program offering mixes with the most reach among prospects. This may identify the obvious popular degrees as well as the niche degrees that best complement them.
Pricing is another essential objective and input. Advanced pricing research helps an institution understand the optimal degree price points for interest and revenue. Approaches such as the Gabor-Granger method or Van Westendorp method are powerful tools.
The finer details of a program also matter a great deal. A conjoint analysis can be leveraged to see which programs would generate the most interest.
This method presents survey respondents with hypothetical programs assembled from key attributes like price, location, field of study, and time commitment.
Researchers can then simulate interest in different combinations of programs offered by the institution and its competitors.
To narrow down which program features are most important, you might consider a MaxDiff analysis. This method challenges respondents to select which features they like most and least from a list. The analysis ultimately produces a prioritized list of program features to guide decision making.
Psychographic and demographic data are extremely valuable for painting a full picture of students. A segmentation study uses this data to develop distinct student profiles and personas. The output for each segment helps an institution understand how to reach them, how to message them, and how to meet their needs.
Drive Research is a market research company with extensive experience in the higher education space. Our team brings the high level of service and technical expertise to execute a successful advanced analytics for any college or university.
Interested in discussing some research objectives for your higher education institution? Contact us today.
