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Shopping for Sustainability: Re-Envisioning the Secret Shopper Assessment 

Explore our experience with the Secret Shopper assessment, our plan to modify it, and our goals for achieving sustainability. Discover how we can gather more data, improve outcomes, and enhance user experiences.

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Shopping for Sustainability: Re-Envisioning the Secret Shopper Assessment 

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  1. Shopping for Sustainability: Re-Envisioning the Secret Shopper Assessment  Tricia Boucher, User Experience Librarian Jessica McClean, Subject Librarian

  2. What we’re covering today • Our experience with Secret Shopper • Our plan for modifying Secret Shopper • What we hope to achieve

  3. Secret Shopper: What is it? • Taken from the retail and hospitality sectors • Natural interactions yield realistic results • Can produce both quantitative measurements and qualitative observations

  4. Secret Shopper: How do we do it? • Began in 2014 to test new customer service models • Coordinated by two library staff members every long semester • Originally asked supervisors for input on what to test

  5. Secret Shopper: Do we like it? NO.   • Too time consuming for our narrow applications • Not using the data to its fullest potential

  6. Secret Shopper: Can we get rid of it?   Short answer: No. Why? • Need to keep the current metrics for reporting outcomes • Data is unique!

  7. Darn.  Now what do we do? Make Secret Shopper more sustainable through economies of scale. Economies of Scale give us... • Data on more types of interactions • More/different types of data • More responses for valid results

  8. Scaling up: The plan • Simplify staffing • Analyze data against outcomes • Measure additional outcomes

  9. 1. Scaling up: Simplify staffing Partner with marketing/business faculty

  10. 2. Scaling up: Analyze data against outcomes • Run a data scan • Run an outcomes scan • Check data against outcomes for gaps 

  11. Data Scan • Annual Report data • Outcomes-based data

  12. Outcomes Scan • Reported outcomes • Targeted outcomes

  13. Checking data against outcomes • Choose what we want to know • Decide best metrics for discovery

  14. Putting outcomes first

  15. Example: Outcomes-based data gathering

  16. 3. Scaling up: Measure additional outcomes What do we want to know?

  17. Scaling up: Measure additional outcomes What outcomes? • Ask for input • Journey maps • Measure non-human interactions

  18. Possible Outcomes Can modify either: • Scenarios • Reporting instrument

  19. Example: Cleanliness data before expansion

  20. Example: Cleanliness data after expansion

  21. What scaling up means for us: More work, but easier • Systematized • Scheduled

  22. Ultimately, scaling up means: Better results...create changes to improve our users' experiences!

  23. Thank you! Tricia Boucher, User Experience Librarian Squirrel@txstate.edu Jessica McClean, Subject Librarian Jlm336@txstate.edu

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