Select Sidearea

Populate the sidearea with useful widgets. It’s simple to add images, categories, latest post, social media icon links, tag clouds, and more.

[email protected]

Alternatives Evaluation

Alternatives Evaluation

The concepts in this article will be further discussed in an enroll ml webinar on Tuesday, April 25, at 3:00 (eastern). 


We call it yield season.  McKinsey’s Consumer Decision Journey would call it alternatives evaluation.

I’m unsure which label is more lackluster and unexciting to people who don’t work in our profession, but I digress.


Step three – alternatives evaluation


The beginning of the journey was problem recognition.  That’s the moment a student (and their family) decides that they should consider looking for a college.  The start of the college search process, if you will.


Then came information search, where students can explore new institutions, majors, and experiences.  


Once they’ve done that, they’ve identified the list of colleges they’re considering or, more likely, applying to. 


This is their evaluation set.


If a college still needs to be added to a student’s list, it will take an act of Congress (or perhaps a recruiting call from a coach) to get in there.


This is when colleges can (must?) draw a picture of comparisons between their institution and the alternatives, directly or indirectly.


When a student has moved on to alternatives evaluation, you are no longer introducing your institution to them.  Instead, you are making the case shamelessly that your institution is the better choice for them.


This is where a college can increase enrollment by doing yield better.  There’s a deceptively simple two-part formula to increase numbers at this stage in the consumer decision journey:


1 – Learn what each student values in their college search process

2 – Deliver relevant, accurate, distinguishing information to them, personally


Small, private institutions may be able to follow the formula precisely as written.  Larger institutions or understaffed smaller institutions will need to find the signals deep within their data set to deliver this type of recruitment communication that meets those two criteria.


This, and other aspects of this topic, will be discussed on April 25 at our next enroll ml webinar, with a recording available afterward.

Teege Mettille
No Comments

Post a Comment