competitive intelligence tool emcit tm recruitment goals
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COMPETITIVE INTELLIGENCE TOOL (EMCIT TM ) Recruitment Goals: Sample - PowerPoint PPT Presentation

Using Segment Analysis Service Data to Recruit, Enroll, and Retain the Right Students COMPETITIVE INTELLIGENCE TOOL (EMCIT TM ) Recruitment Goals: Sample University Goal 1: Build the prospect pool Goal 2: Build the low/no discount pool


  1. Using Segment Analysis Service Data to Recruit, Enroll, and Retain the Right Students COMPETITIVE INTELLIGENCE TOOL (EMCIT TM )

  2. Recruitment Goals: Sample University • Goal 1: Build the prospect pool • Goal 2: Build the low/no discount pool • Goal 3: Build the pool of academically strong students

  3. Competitive Intelligence and SAS • Start with top 5 feeder clusters (both HS and EN) • Look at cluster descriptions to narrow down to a few Priority Clusters • Focus EPS reports on Priority Clusters in order to address goals • Identify 10 new, promising recruitment markets

  4. Sample University: Cluster focus EN Cluster Defining Characteristics Wealthy; Financial aid is not a priority; Choose good schools, a number of which are private and religiously-affiliated; Median income is $134K 78 Relatively high income; Students tend to take AP and honors courses and have above-average test scores; Median income is $95K 51 HS Cluste r Defining Characteristics Predominantly Christian affiliated schools; Serve upper middle class communities; Students exposed to above average curricula; Strong test 72 scores

  5. EN Cluster 78 Neighborhoods • These neighborhoods contain the types of students who are most likely to be interested in Sample University and vice versa.

  6. Priority Cluster Focus: EN 78 Geomarket Total Test Sample U Geomarket Name Code Takers Score Senders Montgomery Metropolitan MD02 3447 0 Fairfax Co VA02 3303 30 Fairfield Co CT03 3226 20 Westchester Co NY15 3079 3 Essex Co MA04 1408 0 Fulton Co GA02 1289 35 • GA02 has a large number of 2015 test takers living in EN 78 • Sample U has had some success there • Sample U has a strong alumni base in Georgia and could build on their prior success

  7. GA 02 Focus by Zip Code 2014 Total Test % Living in % Living in ZIP Code Takers EN 78 EN 51 30350 55 64% 10% 30328 1140 37% 5% 30342 73 34% 13% 30345 831 24% 19% 30338 609 21% 7%

  8. GA02: Focus on HS 72 Sample U Geomarket Total Test Score HS Name State Country Code Takers Senders Landmark Christian School GA US GA02 72 0 Fellowship Christian School GA US GA02 61 12 Mount Pisgah Christian School GA US GA02 56 3 Loganville Christian Academy GA US GA02 40 0 What is Sample U doing differently at Fellowship Christian? Have they been recruiting at Landmark Christian?

  9. Competitor success in EN 78 • Pick top 7 competitors (SAT overlap / NSC) • Look at their success in the Priority Cluster • Compare that to Sample U’s success

  10. Competitor success in EN 78 2014 Score Senders Geomarke Geomarket Name t Code Sample U Comp1 Comp2 Comp3 Comp4 Austin & Central Texas TX06 4 30 47 25 87 Fairfax Co VA02 30 28 38 48 17 Fairfield Co CT03 20 12 38 49 9 Fulton Co GA02 35 96 389 96 4 Lowell, Concord, & Wellesley MA08 0 5 14 18 3 Montgomery Metropolitan MD02 0 28 79 70 7 Southwest Houston Metro Area TX16 3 20 50 37 127

  11. TX16 Drill-down 2014 % Out-of- Total Test % SAT > state % Living HS Name Takers 1200 Mobility in EN 78 Duchesne Academy of the Sacred Heart 60 50% 63% 30% Emery Weiner School 63 27% 67% 76% Episcopal High School 171 27% 68% 51% Kinkaid School 143 58% 80% 55% Memorial Senior High School 632 37% 54% 51% Saint John's School 148 79% 93% 40% Saint Stephen's Episcopal School Houston 7 29% 29% 29% Saint Thomas Episcopal School 38 66% 55% 39%

  12. Be More Data-Driven with SAS • Be more targeted with name buys = Save Money! • Build presence in new markets wisely • Spend precious travel dollars in the right places to find the right students for you • Segment Analysis data can help at all stages of the funnel / student cycle

  13. Full-cycle use of SAS data • We incorporate these data into: – Inquiry-to-applicant conversion model (EMPOWR TM ) • Combine SAS factor scores with institutional data • Can greatly expand what we know about inquiries – Predictive yield model (EMFASYS TM ) • Especially helpful with non-aid filers – Predictive model of retention • Incorporate SAS into admit strategy to choose students most likely to succeed

  14. Emily Coleman Vice President for Enrollment Management Services Maguire Associates, Inc. 978.371.1775 Ext. 258 ecoleman@maguireassoc.com www.maguireassoc.com

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