Higher education marketing has two important components that define its returns – ‘user acquisition’ and ‘demand generation’.
Most of the people I have met place their bets into user acquisition, without any importance for the amount of demand generation that will eventually allow for a successful year. So we are looking at a sub-linear return in marketing expenditure if the strategies are based only on user acquisition.
Super-linear returns happen when marketing and admission teams build their plans based on large demand generation for their undergraduate/graduate/MBA programs separately for every geography, and follow through with effective user acquisition channels.
I have asked many admission teams from international Universities this simple question – “What’s your recruitment demand in (say) China or India or South-East Asia?“. I have never received any answer from these folks.
That is primarily because of the way they approach their plans each year. But, they remained interested to understand how this number can actually be calculated, correlated and then included it in their design every year.
So what is this number and what’s its correlation to results?
The recruitment demand is the cumulative measure of every engagement for a particular institution/program across every relevant channel available to aspirants in a particular geography, at a given time “t”. “Engagement” here refers to any action, such as – reading, asking a question, sharing information, meeting, visiting, calling in, etc – that can be done by the aspirants across all available channels relevant to his/her aspiration to join a program/institution.
Every engagement gives a specific number (for example – in the digital parlance, a “page-view” for a specific institution generated due to engagement contributes to the demand number). Add these numbers together across all the relevant channels, and you would have a “recruitment demand” score for yourself.
Such numbers can then be collated, and a scalable system can be designed to provide institutions a ranking of sorts. For example, imagine institutions in Singapore getting to know “demand-wise” which is the top 10 in a certain geography (like India). Such a scale allows, for the first time, an idea of how much engagement needs to be put in by individual institutions.
Such recruitment demand numbers correlate well with the improvement in quality or quantity of aspirants for a certain program/university. Statistical analysis shows that a positive correlation exists between the recruitment demand and the quality/quantity of aspirants, collected over a time period of 3-5 years.
A simple explanation also explains this phenomenon:
Admits = d/dx(Applications) = d/dx(Enquiries) = d/dx(Recruitment Demand)
So next time, if you are sitting with your team to figure out expenditure in (say) the Indian market, please ask them to provide the “recruitment demand” numbers for your university/program in India for the past 2 years at least. Without any data on such user engagement, it would like flying an aircraft without any radar or wind measurement instruments; one will fly guessing that the air speed is fine, which can actually be quite disastrous.
This also means that your vendors now need to provide more relevant data as per your recruitment needs, rather than what shows their medium in good light. To be very frank, things like – Banner impressions, share of voice, CTRs, readership numbers, search engine marketing, etc – wouldn’t make any sense exclusively once you see through the lens of recruitment.
These are advertising terms/jargons that may make you feel like you belong there as a marketer, but at the end of the day, they don’t really contribute to what your end objectives are.
“Recruitment demand-based” marketing and admission plans will change the game for you in the coming years. You would know exactly what to measure, how to measure it, and how much money is truly enough. Start working on such demand generation frameworks at your end and enjoy the fruits of your smart work.