Unlock Growth with Data-Driven Methods
A new year always brings new challenges, and 2021 is no exception to that rule. Institutions of every size have had to adjust to the unfolding crises engulfing the previous year only to turn the corner and find no end in sight. Because no one can predict the future, universities are sure to find new unforeseen challenges in marketing, recruitment, and enrollment as traditional methods will be curtailed under health-safety restrictions and budgetary crunches. However, schools that want to do more than just survive must explore new methods and solutions. As I argued in my new book, The Death of Content As King: How a Data Democracy Has Revolutionized Marketing, organizations that embrace data as the new voice and vote of their audiences will dominate in this new area. In this article, I will outline three data-driven methods for finding new audiences to help institutions meet their goals and thrive.
The best way to start a data-driven marketing experiment is by using your own data to its fullest. Every institution regardless of size already has lists of students at their disposal like students that have requested information, applied and enrolled. In the modern era of student recruitment and enrollment, each of these lists is guaranteed to have two basic data points: an email address and a phone number. Now of course, depending on your school’s enrollment-cycle, some of these lists are still relevant and valid, but they do not help expand audiences. A data-driven method that does is called lookalike audiences. Available on advertising platforms across programmatic advertising and social media networks, it uses your existing list of students to find people like them. Because data-powered algorithms of major platforms already have correlated and quantified billions and billions of data points for every internet user, these data-attributes are already connected to the most basic user touchpoints: their email address and phone number. Institutions with privacy concerns should rest easy as security protocols of major platforms, as part of the matching technology, make sure your lists are never exposed or unsecured. With lookalike audiences, institutions can identify and market themselves to new students most like the students you already have today. The good news is that lookalike audiences can be segmented and deployed easily across many platforms with minimal budget and effort.
For institutions seeking deeper insight into their audiences in order to better reach them, website analytics partnered with third-party data points should be explored. Third-party data points worth including should have the demographic and psychographic attributes that can be layered against website visitation. This is one of the methods, we deployed at University College at Washington University in St Louis to have better understand our audience segments, and it resulted in increased enrollments. By plotting the path students take to enroll, from their first website visit to registration for courses, each segment can help find the who, what, where, when, and why–the depth of which is far greater than website surveys or focus groups, where undersampling can occur. Does the who of your audience analytics match or differ from expectations? Does what path students take through the website predict success or failure? Are there removable roadblocks or unforeseen short cuts students are finding on their own that surprise you? Are you considering that student location has many variables, including visitation source traffic, geographical boundaries, and demographic old backgrounds? Does the when of student visits have a pattern that corresponds to enrollment cycles or marketing campaigns, which is also a good way to measure the effectiveness of current efforts and driving results? Finally understanding the why, or the student’s intent, requires the context of all the data points before it.
Data can tell a story when you know how to listen to it about who is and who is not your audience. The danger of just listening to one side risks missing under-represented groups. People of color, people with disabilities, and veterans are just some of the groups that many institutions have made great strides over many decades to be more welcoming towards. However, enrollment across the country still shows that they are under-represented in higher education compared to the overall population. This is data that every institution accepting financial aid already has available to them. The question is what to do with it. It’s important that the institution as a whole aligns with the mission of increasing enrollment in underrepresented groups, so that resources are oriented accordingly. Still, the message has to reach them, which can be done with data-driven marketing. The message has to resonate and be consistent along the student journey, which can be done with data-driven audience segmentation and communication—the results of which have to be measured against the desired enrollment outcomes, which, of course, can be done with data analysis. Data and message aligning to find and recruit underrepresented groups is not the end of the efforts needed to increase enrollment. Retaining and providing a positive experience to the students in these groups leads to successful alumni who can then provide testimonials of the positive impact of their higher education.
Institutions don’t have to face the challenges of this new data-driven era alone. Partners are available across the entire digital ecosystem to help universities navigate their data streams, lakes, and oceans. Those who have hesitated to apply data-driven efforts should recognize that their competitors are either already employing similar methods or exploring the possibilities of applying them. And while using data to deploy lookalike audiences, gain analytics insights, and finding underrepresented groups is a good start to tackle the challenges of today, the pace of technological development suggests that new concepts are just around the corner.
Author Perspective: Administrator