Every university buys the same prospect lists, then uses mostly the same marketing medium (email), and largely uses the same language and similar pitches. Universities spend a lot to differentiate themselves, but these efforts often break down in their marketing. Brand projection and differentiation are vital, but a university can’t use the same lists, medium, tactics and language and expect to break through the wall of marketing that nearly every prospect receives. Agentic AI can be deployed as goal-seeking texting agents to contact prospects, have personal conversations about your college and their goals, and move them through the funnel to an application. It’s very cost-effective and is substantially more productive than emailing prospects. Prospect conversations can be launched within days and can even be integrated into your current prospect database.
Email has two core problems: it’s likely the wrong medium for your target audience and it returns limited data. For the first, most university’s primary audience does not typically interact with email; real-world open-rates are shockingly low (reported open rates are artificial). University prospect and student comms should use a primary medium that is native to the demographic. For the second, email data - including something as simple as open-rates - tend to be highly inaccurate.
In order to engage in brand differentiation, universities must know how their peers are marketing to prospects. This seems obvious, but from our research, it appears as though many universities are not aware of their peer-group marketing. The potential impact of recruitment efforts should not be assessed on the marketing created but rather the marketing received. This received marketing experience often demonstrates an incredible lack of differentiation. From our data, it appears that most university marketing communicates in the same ’voice’. This homogenization of voice will likely become worse as some marketing moves to chatbots. While branding efforts often focus on design language, colleges should pay more attention to ’voice’ so that their communications stand out. Chatbots will present even greater problems, as most university chatbots are simply wrappers (for example, context wrapped around ChatGPT), the ’voice’ of the chatbot is consistently the same as their peers and competitors (and reflects a standard corporate voice). All automated services, from email to chatbots, should be thoroughly benchmarked not only to confirm that it answers questions correctly but also so that it cannot drastically divert from your brand. Further, chatbots should be benchmarked for security issues.
The massive quantity of emails that colleges send usually gathers very little feedback data to answer basic questions (such as: is this acquired list worth acquiring again)? And colleges target very broadly - often emailing 100k+ acquired prospects - precisely because of this limited data. What exactly are prospects saying about your university? Every prospect campaign should be a focus group, relaying valuable information back to the university to inform its future marketing. Improving response rates for pieces intra-campaign should be the norm. This rapid iteration enables marketers to impact returns in the current campaign without having to wait until the next cycle (which, for many universities, is 6-12 months).
Agentic AI can be deployed as goal-seeking texting agents to contact prospects, have personal conversations about your college and their goals, and move them through the funnel to an application. It’s very cost-effective and is substantially more productive than emailing prospects. Prospect conversations can be launched within days and can even be integrated into your current prospect database.
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