Colleges and universities can use statistical analysis to increase their qualified leads and conversions, reduce both AHT (average handling time on calls) and CPA (cost per acquisition), and increase start rates and graduation rates (and revenue!). Here’s how:
Colleges and universities are businesses too, and need to remain competitive by bringing new students in, ensuring enrolled students start and keeping their existing students through graduation. Analytics can greatly refine your target, and help you prioritize your efforts. A refined target market will increase your response and conversion rates, reduce your AHT and CPA, and increase the lifetime value of the student.
Many colleges are using paid search (bidding on key words people enter into search engines) and display advertising (banner ads). While some colleges may advertise in all geographies, most limit to a certain number of geographies. And even those that do advertise in all geographies must prioritize them in some way. The question, of course, is which geographies should you prioritize?
A starting point is to have some basic information on the ZIP codes in which you are paying for search and display. There are some online tools that can help with this, and surely your Google Analytics can shed a lot of insight. But you can do a lot better. Here’s what to do:
1. First we want to find out how many people are in each ZIP code. This gives you an idea of the opportunity size. Also, some ZIP codes may be too small, or may not have enough of your target market in them to be worth pursuing.
2. Determine which ZIP codes have more valuable students. This is basically their lifetime value. To get lifetime value, you should take into account enrollments, starts and graduation rates. And the acquisition costs. You can layer in other costs, such as the cost to maintain the student in your program. Take the average lifetime value for each ZIP code.
3. Use statistical analysis to predict the number of responses (inbound leads) you can expect from each ZIP code, and what the expected conversion rate will be. Predictive modeling will find key characteristics that distinguish responders from non-responders. Each lead will actually get an individual score. Now you have an average response rate for each ZIP code.
4. Do the same thing for conversions (actual sales). You will get an average conversion rate by ZIP code. A lot of times these go hand in hand. Sometime they don’t. If one ZIP code has a higher response rate, but lower conversion rate, this is an opportunity for you to move the levers on incentives or offers (e.g., maybe waive enrollment or application fees for segments that are tougher to convert).
5. Now you are in a position to look at the size of the prize (you’ll know how many prospects each ZIP code has), which ones have the most valuable students, and which ones have the higher response and conversion rates. Armed with this information, you can develop a solid acquisition strategy that effectively targets geographies, qualifies leads and uses incentives wisely to provide that extra push to leads on the cusp of converting.
Now, you are all set! At the end of the day, you can use statistical analysis to profile your “geographic hotspots” for paid search and display ads. And if you are paying for direct mail or email lists, you can use statistical analysis to rate each lead. Next week we will look at retention! Check back soon!
For more information on this topic, please contact Jay at firstname.lastname@example.org