Analytics continue to become more popular as technology and other resources continue to advance. Increasingly, more and more information is becoming available to help marketers be more effective.
This week, we’ll cover the three types of analytics and how you can leverage them to help your university/college’s marketing efforts more effective.
Descriptive Analytics
These types of analytics are great for showcasing information that’s already been collected to get a good understanding of where you are now. Unfortunately, these analytics pretty much stop there. They won’t provide the qualitative data you may be searching, and they won’t help you plan for the future.
A great example of descriptive analytics is a dashboard. Below is a screenshot of an example of a dashboard for Florida International University’s College of Business.
If you’re struggling to see the information above, you can click the image to see a larger version of it.
Predictive Analytics
These analytics can be more useful that descriptive analytics. Predictive analytics uses past data to attempt to predict future outcomes. These analytics could provide useful insights on how customers may respond to marketing promotions or how sales could be affected.
In a university/college setting, predictive analytics can help predict a customer’s journey through graduation to identify revenue generating opportunities. In the example below, we know that students once students finish their final semester and after graduating, many of them will be traveling (the exact amount depends on your individual institution). Since we know XX amount will be traveling, it’s an opportunity to market merchandise in bundles identified as essentials for travel.
Prescriptive Analytics
These are analytics that leverage optimization techniques to advise marketing teams on what would work best for their target audience and the organization’s end goal. A great example of this would be something we’ve discussed in the past: A/B testing.
Conducting optimization experiments, like A/B testing, provide marketers with a statistical analysis to help them determine the best way to market.
Imagine you’re crafting an e-mail encouraging students to participate in a survey. Here, after conducting an A/B test with, let’s say, different subject lines, prescriptive analytics can help you gauge which tactic is most effective.
In the example below, it appears that Version A of our A/B test garnered the better results. You should still run test to determine statistical significance.
Do you currently implement all three of these types of analytics? Have a fun story to share about how these analytics have supported your marketing recommendations? Let me know why in the comments!
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