Nabep analytics presentation


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Nabep analytics presentation

  1. 1. Analytics Driven Recruitment<br />By: Aaron Black<br />Director of Admissions, MBU<br />
  2. 2. About me<br />
  3. 3. This isn’t about Google (analytics)<br />
  4. 4. This isn’t (just) about data<br />
  5. 5. It’s about Discovery<br />
  6. 6. Tell them what I’m going to tell them<br />Why analytics?<br />What is analytics?<br />Where does it fit?<br />How do you do it?<br />
  7. 7. The importance of Analytics (a business perspective)<br /><ul><li>Analytics trumps intuition
  8. 8. Analytics is a differentiator
  9. 9. The first responsibility of a leader is to define reality.—Max DePree, Leadership Is an Art
  10. 10. You’re here aren’t you?</li></li></ul><li>Six Sigma:Get rid of anything (any process etc.) that does not add value to the end user.<br />
  11. 11. It’s about discovering a recruitment model that results in the right number of the right students…and does it efficiently.<br />How much are you spending to recruit one student? How many more could you recruit with a more efficient model?<br />Marketing<br />Recruiting<br />COA<br />
  12. 12. Your Recruitment Model: how do you know its reaching its full potential?<br />Your Recruitment Model<br />
  13. 13. Recruitment Model<br />
  14. 14. Without analysis our recruitment model is just our best guess.<br />
  15. 15. Macro-environment & instability<br />Things can get complicated <br />Environment<br />Political<br />Competition<br />Soci-cultural<br />Demographic<br />Technology<br />Economic<br />Ethics<br />Regulation<br />
  16. 16. Analytics<br />From Wikipedia, the free encyclopedia<br />"the science of analysis". A practical definition, however, would be that analytics is the process of obtaining an optimal or realistic decision based on existing data…unless there are data involved in the process, it would not be considered analytics.<br />
  17. 17. Where does analytics fit into SEM?<br />Meeting<br />Goals<br />Tactics<br />Strategies<br />Enrollment Infrastructure<br />Structure, Staffing, Skills, Systems, Service<br />Data Collection and Analysis<br />Clear Mission and Goals<br />Typical starting point<br />Starting point for long term success<br />
  18. 18. Analytics uses for SEM<br />To improve retention <br />To build relationships with high schools and community colleges<br />To target admissions efforts and predict enrollments<br />To recommend changes to admissions policy<br />To examine issues of how best to accommodate growth<br />To improve the educational experience of students<br />To identify needs of unique student groups<br />To project and plan for student enrollment behavior<br />To determine financial aid policies<br />To assess student outcomes<br />
  19. 19. Analytics<br />Passive/Vanity metrics: Best for when you know cause and effect relationships well. Do you really know what actions you took in the past that drove those inquiries and applicants to you, and do you really know which actions to take next? <br />Actionable metrics: Imagine you add a new feature to your website, and you do it using an A/B split-test in which 50% of customers see the new feature and the other 50% don’t. A few days later, you take a look at the number of applicants from each set of visitors, noticing that group B has 20% higher application rate. Think of all the decisions you can make: obviously, roll out the feature to 100% of your customers; continue to experiment with more features like this one; and realize that you’ve probably learned something that’s particular valuable to your prospects.<br />
  20. 20. Practical Ways to use Passive Data<br /><ul><li>Capacity Study
  21. 21. Preferred New Student Profile
  22. 22. Primary Market Penetration
  23. 23. Price Elasticity
  24. 24. Un-met Need Gap
  25. 25. Student Need/Support Alignment</li></li></ul><li>Continued…<br />Practical Ways to use Passive Data<br />DFW rates<br />Travel planning<br />ACT ranking<br />FAFSA position<br />Segmented funnels<br />Predictive modeling<br />Strategic Scholarship Decisions<br />E-mail open rates<br />
  26. 26. Limitations of Passive Analytics<br />Passive: Isn’t necessarily actionable<br />Unless you know cause-effect relationships well it only allows guesses.<br />It relies on drawing conclusions from correlations<br />Many decisions in recruitment based on intuition but developing accurate intuition takes experience and time.<br />
  27. 27. “Correlation does not imply causation!”<br />-Passive Data (limitations)-your funnel is trying to tell you something<br />
  28. 28. We make plans based on guesses and passive data.<br />Accurate Intuition takes time and means we either rely on our predecessors models (outdated?) or adopt someone else’s model (not OUR perfect recruitment model).<br />
  29. 29. Data sometimes hard to obtain and accuracy can sometimes be questionable.<br />
  30. 30. The goal of your research should be to reduce waste and make current processes more effective.It’s about discovering your perfect recruitment model.<br />
  31. 31. Life (enrollment) is an experiment…but we treat it like a guess.<br />Reality<br />Plan<br />
  32. 32. “Everybody has a plan until they get hit”.-Mike-<br />
  33. 33. Analytics<br />From Wikipedia, the free encyclopedia<br />"the science of analysis". A practical definition, however, would be that analytics is the process of obtaining an optimal or realistic decision based on existing data…unless there are data involved in the process, it would not be considered analytics.<br />
  34. 34. Experiment<br />From Wikipedia, the free encyclopedia<br />An experiment is a methodical procedure carried out with the goal of verifying, falsifying, or establishing the accuracy of a hypothesis. <br />Experimentation is the step in the scientific method that helps people decide between two or more competing explanations – or hypotheses. These hypotheses suggest reasons to explain a phenomenon, or predict the results of an action.<br />
  35. 35. Aaron Black<br />Funnels are like status updates<br />They tell us we need to DO SOMETHING about something…but offer no clue about what that something is that we need to do.<br />
  36. 36. Using existing data helps us identify weak areas and generate hypotheses (guesses) about why things are that way. Further, it allows us to generate additional hypotheses (guesses) on what a solution might be. It lets us guess.<br />
  37. 37. A radical idea about recruitment analytics<br />"Thirty years from now the big university campuses will be relics….. (Residential) Universities won't survive. It's as large a change as when we first got the printed book.“ -Peter DruckerForbes, June 16, 1997<br />
  38. 38. Powering up your insight<br />Become active about experimentation<br />
  39. 39. The key isn’t data, the key is agility driven by discovery.Agility: ability to make strategic changes (quickly), based on truth.<br />
  40. 40. Agility…because what good is data if you can’t use it to make changes?<br />
  41. 41. What’s needed then is a framework for conducting research with the aim being a perfected recruitment model.<br />
  43. 43. HOW?<br />Split-tests: most actionable of all metrics, because they explicitly refute or confirm a specific hypothesis.<br />Funnel metrics & cohort analysis: Example: SPD vs Individual Visit and funnel progress<br />Keyword & web traffic metrics: What keyword entrances result in the most applications?<br />
  44. 44. Split Test<br />
  45. 45. Split Test<br />
  46. 46. Split Test<br />
  47. 47. How Obama raised $60 million by running a simple experiment<br />
  48. 48. The Winner: 2,880,000 more sign ups + avg. gift of $21 = $60 million more<br />
  49. 49. “The value of an idea lies in using it.”<br />