OK, I'll Do It Myself! Data Mining, Reporting, and Analytics on a Shoestring
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This document outlines how Phil Melita, coordinator of marketing and communications at the University of Richmond SPCS, took on data mining, reporting, and analytics independently with limited resources. It describes determining the scope of data available from their customer relationship management system and Google Analytics. It discusses assessing goals around enrollment, attendance, and growth. Key performance metrics were selected around inquiries, applications, and attendance. Data was pulled from the systems and presented visually in dashboards, graphs, and regular reports to track progress towards goals and understand trends. The document emphasizes asserting one's influence within an organization to identify what data adds value and creating impactful reports on a regular schedule.
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OK, I'll Do It Myself! Data Mining, Reporting, and Analytics on a Shoestring
- 1. OK, I’ll Do It Myself! Data Mining, Reporting, and Analytics on a Shoestring Phil Melita Coordinator, Marketing & Communications University of Richmond SPCS July 28, 2015
- 2. Agenda • Setting the Scene • Determining the Scale • Finding your Space • Assessing the Challenge • Picking the Criteria • Pulling the Information • Presenting the Data
- 3. Setting the Scene Who/What/Where is SPCS • Private, Liberal-Arts University in Virginia • One of 5 autonomous Schools with distinct Dean, tuition, admissions, marketing, etc. • Degree, Non-Credit, OLLI, Summer • Almost exclusively classroom-based • Started with Intelliworks/Radius in 2008
- 4. Setting the Scene Knee-deep in data • Facebook, Fitbit, Apple WATCH, Statcast • Google Analytics • How are we doing? • How is what you’re doing doing?
- 5. Determining the Scale SPCS parameters • 130 inquiries per month • 70 applications per month • 47,000 contacts in Radius • 200 campaigns per year (+800 from comm plans) • 230 info session attendees per year
- 6. Determining the Scale SPCS history • Rollout September 2008 • Initially 5 users, now 10 • Began with degree-program inquiry capture • Me, Myself, and I
- 8. Finding your Space Who are you? What do you do? • What is your role in the organization? • What data do you influence? • Where can you add value?
- 10. Assessing the Challenge The Goal • Money? (Revenue/Profit/Gross margin) • Reach? (Attendance/Enrollments/Web visits) • Growth? (Doing better than last year/typical term)
- 11. Picking the Criteria S.O.S. (Shiny Object Syndrome)
- 13. Picking the Criteria Progress toward The Goal • Measuring interest/responsiveness • Seats in seats/Counting noses (attendees, registrants, etc.) • Conversion from stage to stage • Determining trends • Key Performance Metrics (KPMs) • Measureable • Actionable • Predictive
- 14. Picking the Criteria What does Radius let us see? • Inquiries • Applications • Interactions • Reservations • Interest (open rates, click-throughs) • Cumulative data or date-range analysis
- 16. Picking the Criteria Decisions, decisions. . . • Web visits/users • Inquiries • Applications • Attendees
- 17. Picking the Criteria Google Sheet
- 18. Pulling the Information Where to find What you Want • List Views • Targets • Campaign Results
- 19. Pulling the Information Create data interactions • Attendee throughput • Started-to-Submitted window • Comm Plan success • Application time analysis • Applicant analysis by term (Fall/Spring/Summer) • Contact creation date and Campaign opens • Conversion (inquiry-to-applicant) • Correlations: e.g. Inquiries to Applications
- 20. Presenting the Data Getting your point across • Dashboards • Infographics • Graphs • Regularly-scheduled programming
- 22. Presenting the Data Applicant Analysis
- 23. Presenting the Data Info Session Campaign Opening y = -‐329.5ln(x) + 1401.2 R² = 0.97271 0 100 200 300 400 500 600 700 800 900 1000 1100 1200 1300 1400 1500 1600 1700 1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51 53 55 57 59 61 63 65 67 69 71 73 75 77 79 81 Frequency Months Months a0er Ini3al Contact Crea3on
- 24. Presenting the Data Attendee Throughput 25.0% 30.0% 35.0% 40.0% 45.0% 50.0% 55.0% Cumulative Info Session Rates Attendees Applicants
- 25. Presenting the Data Conversion Prospects, 1000 Prospects, 1099 Prospects, 1250 Prospects, 864 Prospects, 874 Prospects, 1003 Prospects, 870 Prospects, 1023 Stealth, 117 Stealth, 106 Stealth, 93 Stealth, 189 Stealth, 136 Stealth, 121 Stealth, 232 Stealth, 149 Incomplete, 57 Incomplete, 82 Incomplete, 60 Incomplete, 140 Incomplete, 103 Incomplete, 81 Incomplete, 133 Incomplete, 97Closed, 104 Closed, 50 Closed, 48 Closed, 83 Closed, 52 Closed, 39 Closed, 107 Closed, 70 Admitted, 168 Admitted, 113 Admitted, 64 Admitted, 128 Admitted, 100 Admitted, 100 Admitted, 196 Admitted, 117 0 200 400 600 800 1000 1200 1400 1600 1800 Fa 2012 Sp 2013 Su 2013 Fa 2013 Sp 2014 Su 2014 Fa 2014 Sp 2015 1446 1450 1515 1404 1265 1344 1538 1456 Prospects Stealth Incomplete Closed Admitted
- 26. Presenting the Data Inquiry-Applicant Correlation
- 27. Presenting the Data Applications by Month 0 50 100 150 200 250 300 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec APPLICATIONS STARTED Fall Spring Summer
- 28. Summing it Up Take it to Make it • Yield to no one: assert self and your influence • Observe your environment • Uncover institutional goals • Recognize what KPMs matter • Optimize data extraction/gathering • Create reports with impact and meaning • Keep to a regular reporting schedule Y O U R O C K !
- 29. Questions?