I'm a Recruiter, Not a Data Scientist!
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Easy ways to make sense of higher ed admissions data.
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I'm a Recruiter, Not a Data Scientist!
- 1. Easy Ways to Make Sense of Admissions Data I’m a Recruiter, Not a Data Scientist!
- 2. Before We Begin We are using WebEx. Please enter questions in the Q&A box. We are monitoring the discussion and will try to bring the comments and questions into the conversation. We are recording the webinar; the webinar archive and slides will be provided after the event via email. Join the conversation on Twitter: @CollegisEdu 2Copyright Collegis, LLC - Proprietary and Confidential
- 3. About Collegis Education A strategic operational partner. Collegis Education is a strategic partner for institutions that face barriers to long-term success. Guided by our deep-rooted experience in higher education, our distinctive combination of marketing, technology and content expertise helps us craft holistic, institutionally aware solutions that help optimize and expand your reach. We do it all while preserving what matters most — the heart of your institutional mission and academic identity. 3Copyright Collegis, LLC – Proprietary and Confidential
- 4. Our presenter 4 Dan Antonson Manager, Marketing Technology Collegis Education
- 5. Benefits of using data Barriers to using data Solutions Focus Repeatability Accessibility Context Bring it all together Copyright Collegis, LLC - Proprietary and Confidential 5 What we’ll cover today:
- 6. July 2017 webinar: “How to Increase the Value of Data Captured by Admissions” If you missed it, that’s ok Links to the July webinar will be available at the end of this presentation. Copyright Collegis, LLC - Proprietary and Confidential 6 Re: Data Quality
- 7. Data in higher ed admissions: You probably know more about data than you realize. 7
- 8. Copyright Collegis, LLC - Proprietary and Confidential 8 There is plenty of data to draw from in higher ed
- 9. Copyright Collegis, LLC - Proprietary and Confidential 9 There is plenty of data to draw from in higher ed
- 10. Number of names on search lists Number of applications received Total enrollments Copyright Collegis, LLC - Proprietary and Confidential 10 Admissions Data We All Know
- 11. Helps your team meet enrollment goals Helps with expense management Helps with managing workloads Confirms assumptions (or points you in a better direction) Copyright Collegis, LLC - Proprietary and Confidential 11 Benefits of Using Data
- 12. Focus Accessibility Repeatability Context Copyright Collegis, LLC - Proprietary and Confidential 12 Barriers to Using Data
- 13. Copyright Collegis, LLC - Proprietary and Confidential 13 Data will never be perfect.
- 14. Copyright Collegis, LLC - Proprietary and Confidential 14 Don’t obsess over tools.
- 15. Copyright Collegis, LLC - Proprietary and Confidential 15 Focus on Accessibility and Repeatability.
- 16. Copyright Collegis, LLC - Proprietary and Confidential 16 Better Questions = Better Answers
- 17. Questions bring focus to data Questions give meaning to data Questions help direct priority Questions contain context Questions drive tool choices Copyright Collegis, LLC - Proprietary and Confidential 17 Why Questions Matter
- 18. Copyright Collegis, LLC - Proprietary and Confidential 18 We find and use data every single day
- 19. Copyright Collegis, LLC - Proprietary and Confidential 19
- 20. Your institution’s data may be more accessible than you thought. Copyright Collegis, LLC - Proprietary and Confidential 20 What if you could search higher ed data?
- 21. Copyright Collegis, LLC - Proprietary and Confidential 21 How data answers the big question: What is really driving enrollment?
- 22. Copyright Collegis, LLC - Proprietary and Confidential 22 Let’s dive in…
- 23. Copyright Collegis, LLC - Proprietary and Confidential 23
- 24. The following 12 slides capture the types of data visualizations that were covered in the live presentation that used the Collegis Discovery Engine tool, but are not exact duplicates. While slides can be made from Collegis Discovery Engine, we opted to give you a more spontaneous look at how questions can guide a data visualization process. The graphs below are snapshots of data that we have used to answer questions similar to the ones we covered in our presentation. Copyright Collegis, LLC - Proprietary and Confidential 24 Note:
- 25. Quickly view where your inquiries are coming from and when. Copyright Collegis, LLC - Proprietary and Confidential 25 1. Track and monitor inquiries by source over time.
- 26. Understand your reach in terms of geography. Copyright Collegis, LLC - Proprietary and Confidential 26 2. Map inquiries and starts by location.
- 27. See how inquiries flow into – and out of – your admissions process. Copyright Collegis, LLC - Proprietary and Confidential 27 3. Compare inquiries and starts over time.
- 28. Monitor prospective students through various enrollment decisions. Copyright Collegis, LLC - Proprietary and Confidential 28 4. Monitor leads in terms of sales stage.
- 29. Track where prospects are spending their time in the process. Copyright Collegis, LLC - Proprietary and Confidential 29 5. Calculate the time between stages such as inquiry to app.
- 30. Verify which institutional offerings appeal to your prospects. Copyright Collegis, LLC - Proprietary and Confidential 30 6. Measure interest progression in a term or program.
- 31. Copyright Collegis, LLC - Proprietary and Confidential 31
- 32. Copyright Collegis, LLC - Proprietary and Confidential 32
- 33. Copyright Collegis, LLC - Proprietary and Confidential 33
- 34. Copyright Collegis, LLC - Proprietary and Confidential 34
- 35. Copyright Collegis, LLC - Proprietary and Confidential 35
- 36. Copyright Collegis, LLC - Proprietary and Confidential 36
- 37. What marketing sources are driving inquiries? Where are the bottlenecks in our admissions pipeline? Where are our most interested prospects locally, regionally and nationally? Copyright Collegis, LLC - Proprietary and Confidential 37 New Questions We’re Hearing
- 38. To see four more examples, check our blog article titled: “The Top 10 Dashboards You Need to Grow Enrollment” Copyright Collegis, LLC - Proprietary and Confidential 38 More available
- 39. Barriers to recognize: focus, repeatability, accessibility and context Questions are key - keep asking questions until you get useful answers Dashboards and visualizations help offset the barriers (focus, repeatability, accessibility and context) Copyright Collegis, LLC - Proprietary and Confidential 39 Key Points from Today
- 40. Questions? Copyright Collegis, LLC - Proprietary and Confidential 40
- 41. To access our July 2017 webinar, “How to Increase the Value of Data Captured by Admissions,” click here for the recording. The slides are available on SlideShare. Copyright Collegis, LLC - Proprietary and Confidential 41 More Data Basics
- 42. If you are interested in the data visualization tool we used today, contact info@collegiseducation.com and ask about: Collegis Discovery Engine Copyright Collegis, LLC - Proprietary and Confidential 42 About Our Data Tool
Editor's Notes
- Hi everyone, thanks so much for your time today. Today, we’ll be talking about “data” in the real-world. There is a gap in higher ed data. There are those pinnacle case studies people love to talk about “Through predictive data science, we were able to identify an at-risk group, and we introduced interactive interventions that we were able to proactively reach out in a personalized way to improve outcomes by some %” - but come on. That isn’t realistic for most institutions. Today is the opposite of that, I want to talk about real-data, the real benefits, the real challenges, and ways to approach and look at your admissions data. Data is not glamourous for most in higher ed – and honestly, I don’t think it will be. Sure data can be fun and interesting, but usually it’s messy, unorganized. And we have plenty of it. We’ve all got data laying around in various states all of the place.
- More granularly, I want to talk with you about the real benefits of data . Why data doesn’t have to be as complicated as we think. How you find meaning in the data. And how that all comes together…
- Many of you attended our July 2017 webinar on how to improve the value of data captured by admissions. If you missed it, or if you’d like to review it again, links to the recording and slides will be available at the end of this presentation.
- And the reality is… you can do things with it – even as an admissions professional. It’ll require work. But I want to show you some simple ways to approach it with some real world case studies. This webinar is going to be a little different than most, it’ll have some slides (wouldn’t be a webinar without those) but what I really want to focus on is the data, so I’ll be jumping out of the power point, and we’ll be doing some real world exploring of some higher education data.
- Firstly, Higher Education is absolutely filled with data… there is NO shortage of data sysstems and sources… web analytics, clicks, links, pageviews, sessions, users, emails sent out, click rates, advertising data – impressions clicks,social media data, mentions, tweets. A lot of content for future webinars : )
- But today, we’re going to talk primarily about the data within a CRM or SIS. Customer Relationship Manager (targetx , Ellucian Recruit, Slate, EnrollmentRX, SalesForce) Student Information Systems (Banner, Campus Management, Jenzabar)
- you’re likely familiar with this type of data already – the data comes from a CRM or SIS. How many enrollments did you have last year, how many applications did you receive (whether from your own internal system or the CommonApp). And maybe you have some way to get at your prospective students – whether it’s a list of names or individual visitors.
- And naturally, this type of data can be used for a variety of “analysis / reporting” which we’ll be going into details about. By looking at the data you suddenly have accountability And who has too many tasks And most importantly, data can be a form of “truth” And these are just some of the benefits… but… the irony is… if the benefits are so clear… why aren’t colleges doing data better????? Welll……
- And that’s because there are a lot of barriers… Data is hard. I hate to say it, but it is. Sure there are ways and things to make data easier… which we’ll talk about today. But here are the BIG barriers we typically see… and I’d put them in these 4 categories. Focus, accessibility, repeatability and Context
- Focus: too often the focus of data is on the wrong thing – it’s one thing to focus on making your data better, but often times, the pursuit of perfection derails the project. Accept your data won’t be perfect (ever) and focus on what you CAN do now. You should be interested in PROPORTIONS, SWINGS, TRENDS NOT decimal places. All you should care about is am I 1/3 there, halfway? All the way? Data analysis is about comparison, comparing .1 to .2 isn’t going to get you far.
- Along with the same lines of focus: the focus should never be on tools themselves. If your goal is to build a better house, very rarely does someone suggest buying a better hammer. The data world has a problem with shiny object syndrome, all these cool tools – we’ll definitely be talking about tools, but this should NEVER be the focus.
- The focus should be around Data Accessibility and Repeatability – Accessibility: can you access the data you need? Often times, I’ve found that the problem isn’t with the data itself, it’s how you GET the data – if data is hard to get to, that’s a problem. How long does it take you to get data? And along with that, repeatability. How can you automate findings, alerts, data. Can you regularly get the answer you need? If I give you an answer once but it takes 6 months to get the answer again: that’s a problem.
- And the absolute most important thing… Focus on the questions!! - What can we answer, what can we DO with that answer? If you’re answering questions with data, and you can’t do anything with the answers, I’d suggest asking better questions. Better questions always leads to better answers. Now… this is the part where I get on a soapbox… over and over again, I see colleges failing to identify the QUESTION. What is it you’re trying to answer. It turns into “Well, if we had Tableau, or this shiny object, it’d allow us to connect this. Blah blah” those things may all be true. But start with the question – what is the QUESTION that might drive change. If we had this answer, we could do X… AND THEN focus on the barriers, the accessibility, repeatability, tools, etc. No tool is going to make up the questions for you… Questions can also provide CONTEXT – “why are you asking the question?” - If I ask you “What is our enrollment pipeline?” From what angle? Marketing? Enrollment? One exercise that I absolutely love to do with admissions teams – take out a pad of post it notes and right down all the questions you’d like to know about your students from the data… and sort them… which questions are the most important – are there ways to ask better questions?
- So today, I’m going to do all the demos in a tool called the Collegis Discovery Engine – but I can show you the excel equivalents pretty easily with a couple exceptions. If you can’t tell, we’re pretty obsessed with questions here… and that’s what the CDE is built for, answering lots of questions. And the inspiration for it, came from a tool, you actually use every day.
- Whether you realize it or not, you have access to a crazy amount of data from Google. Google is really really good at questions – and answering them (and also giving you easily actionable results)
- I was at a conference last month… Context is in the search The answer comes fast And then I get different ways to build on the question. Think about the barriers The data isn’t perfect – it’s coming from sooo many places. The process is super repeatable and accessible. And if you aren’t get an actionable answer, ask a better question (or search). CONTEXT
- So we asked ourselves… what would it look like if you could search higher ed data?
- But the big question for admissions is “What is Driving Enrollment?”
- So today, one of our partner – University of NORTH Alabama, has offered to let us use a small sample / subset of their data. So it’s a single program, but I think you can see how we would cut and look at that if you had a lot more data. UNA has been a collaborator with us, for the Collegis Discovery Engine – so they’re actual daily users of the tool and the platform. But again, the tool isn’t the focus…
- You could do all of this in Excel if you really wanted to… The data we’re looking at is from a simple flat file of data (single table). A lot of the analysis we’re going to be doing could be done with pivot tables for instance. You may have tools available like Tableau already… But don’t worry about that today, focus on the questions at hand. And identify the important answers. but chances are, someone on your team does know Excel– or a simple google search can find you tutorials to replicate what I’m going to show you in real-time. So again… data is from a CRM or SIS, it’s a big flat file from… Customer Relationship Manager (targetx , Ellucian Recruit, Slate, EnrollmentRX, SalesForce) Student Information Systems (Banner, Campus Management, Jenzabar)
- Talk about the new types of questions we’re getting from our partners… and there will ALWAYS be new questions.
- Next steps…. - benefits of data Barriers We explored some data, and hopefully you’ve got some new inspiration on ways to approach your own data. Again, the focus here is questions… and when you think about Dashboards, dashboard answers questions. And good dashboards answer the most important ones. If you’re interested, we have an article the Top 10 Dashboards you need to Grow Enrollment, that outlines the important questions.
- Soft CTA – if you are interested in this tool, of course, we’d be glad to talk with you about it.