The Case For and Against Generalization in Higher Ed Marketing
For much of my early career, I was engaged by clients to research and consult on the best way to communicate with millennials. Typically, this meant identifying a set of characteristics and preferences that afforded our clients the opportunity to better communicate with this target audience. It started with Neil Howe’s Millennials Rising, and then citing whether or not various consumer data points validated or conflicted with the documented tropes. With our more sophisticated clients we would build a set of personae that paired generational characteristics with certain institutional values. These strategies are now commonplace, and rightfully so, but they could stand some scrutiny given the prevalent technical advances currently used to collect and manage prospect data in higher ed.
The case for generalization.
Generalizing, or persona-building, works particularly well when we know little about our audiences. Simply put, generalizing strategies in higher ed recruitment and marketing can be distilled into three advantages: personification, assurance, economy and knowledge transfer.
If we can agree that the aim of higher ed marketing is to speak with prospective families personally, individually even, then we must create some method of generalizing traits and communication preferences for instances where we don’t have granular knowledge about whom we communicate with. As these personas are built, we frequently give them names like Agnes DuMonde or Larry Lawrence (The New York Times famously compared Gen Y with Gen Z as Hannah Horvath v. Alex Dunphy). The hope is that if we can supply enough data about the group we can amalgamate that into an imaginary person sitting across the table or on the other end of an email. Currently we’re seeing a tendency to rely on cultural markers to build personas. An example of this is to say “Gen Z is concerned about the environment” or “Gen Z is entrepreneurial.” When you couple these cultural traits with data points such as “Latinx families in certain states attend colleges within 70 mile radius” or “students from low SES zip codes yield 5% less than the rest of the pool,” you can start to pair a persona with strategies and tactics for communication.
At NACAC in Louisville, our team presented a set of eight personas for a beloved client. Their basis were actual matriculants with the generalized and anonymized data points that existed in the CRM. With those data as a foundation, we wrote fictional accounts of paths to that institution to be distributed to the wider team.
In higher ed we use personas to inform the way we compile a search buy, develop content for an outbound strategy or prepare a site for stealth visitors. In these cases, personas allow us to make informed guesses about the types of audiences we will encounter. When used properly, personas give some assurance that those who we don’t know personally will be communicated with in a manner that they will be receptive to. For instance, data tell us that, for this generation, homepages need to load in less than three seconds, and that Gen Z prefers to be productive and creative during their free time. You can start to see how these generalizations might inform strategy for your communication. You can plan for these mindsets and tendencies without being on a first-name basis with your prospects.
It’s both less expensive (certainly during early stage recruitment) and takes less time to implement persona-based communication tactics. It absolves you from having to acquire more intensive data (more on this later) and the resources (financial or otherwise) required to do so.
As times has passed, I’ve come to believe that creating shorthand to define groups of people has another, perhaps more essential premise. Personas are a transference of knowledge about customer segments in a digestible manner to those with less experience interacting with defined customers. In short, it’s a way to briefcase a bunch of study about a group and hand it to your teammates.
The case against generalization.
So what about the case against generalization? Where does this practice falter and why? I think the answer falls into two main categories:
You’re being too general, and generality leads to vagueness.
I see this often. Clients will want to generalize because it’s more economical OR they don’t have confidence in those data they do have. Commonly a client will say something like, “we need to move to Snapchat,” (I’m waiting for the request for TikTok) because data tell them that the demo has more penetration in this app. But this isn’t sufficient rationale to move to Snapchat. A new channel is not a strategy for reaching audiences.
In my work with both millennials and Gen Z, I’ve found that there are considerable pitfalls with treating the generations like a monolith. Blanket statements, even the most flattering, tend to do two things: a) exclude portions of the audience (do all of Gen Z really want to create a start-up?), or b) do nothing to differentiate from anyone else (Gen Z is really tech savvy, but is that really different from millennials or even a subset of Gen X?).
You don’t have to generalize.
Let’s be honest, you have too much information about prospects already to rely on generalization for your marketing tactics. You can buy too much information and deploy it in a way that is tailored to your target audience and economical. The most recent list purchase we advised on had more than 30 columns of data; that gives a pretty precise picture of the student. If the aim of using a persona is to progressively build a richer profile of an individual student, then you may be starting from a profile that is already more comprehensive than a simple name and an email address. And if that’s the case, any generalization would be diluting the information, not clarifying it.
The intent of this post isn’t to be prescriptive; it’s merely a reminder that, while generalization has its place, it should be applied as an intentional practice, not something done by default or constraint. With the current advancements in data and technology, it’s no longer a “must.” Knowing the scenarios when it helps or hinders your marketing goals will spare you from missed opportunities or wasted resources.