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The Use of Analytics in Higher Education

The Use of Analytics in Higher Education. JISC Project York St John University / Applied Web Analytics January 2010. Structure. What is analytics? Customer journeys Three phases of analytical development Key concepts. Definition of web analytics.

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The Use of Analytics in Higher Education

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  1. The Use of Analytics in Higher Education JISC Project York St John University / Applied Web Analytics January 2010

  2. Structure • What is analytics? • Customer journeys • Three phases of analytical development • Key concepts

  3. Definition of web analytics “The process of collection, measurement and analysis of user activity on a website to understand and help achieve the intended objective(s) of the website”

  4. JISC project - using analytics “The process of collection, measurement and analysis of interactions between the university and its various audiences, to understand these audiences and help achieve the intended objectives of the university”

  5. Possible ‘interactions’ with YSJ Benefactor Supporter Speaker to business school Employer, referrer Sole grant provider Joint research grant Lifetime Value Part-time student Donor / Alumnus Careers Advice Centre User P/G Student U/G Student Time

  6. Stages • Data collection • Gaining insight and taking action • Embedding the approach

  7. Stage 1 • Data collection and measurement • Collecting these interactions into a single database, to provide a single view of a contact • Collecting results on previous communications with that contact • Revenue (“Tangible resources”) • Costs of communication • Response rates • Surplus / profits created

  8. Replacement of disparate databases Faculty and Directorate databases Contact management database

  9. Stage 2 – Getting insight and taking action • Developing insight and taking action • Identifying patterns in the data • Developing hypotheses • Performing tests

  10. Getting Insight Insight Data • Identifying patterns in the data • Developing hypotheses • Performing tests

  11. A continuous process

  12. Stage 3 - Embedding the process • Involve stakeholders • Make one person responsible for analytics • Focus on insight / ad-hoc queries, not reporting • Have a positive attitude to testing and ‘failure’ • Set goals for improvement • Focus on tangible outcomes • Start with a small win

  13. Key concepts • Data driven decisions / statistical analysis • Events / interactions recorded in single database • Closed loop marketing – who did you interact with and who and who did not respond • Past behaviour correlates with future actions • Segmentation – different messages to people who are different in their behaviour • Lifetime value and retention rates • Return on Investment (revenue – costs / costs)

  14. Thank you Dan Croxen-John Applied Web Analytics dan@appliedwebanalytics.com 0800 990 3580 Follow me on Twitter: Dan Croxen John or ApldWebAnalytix

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