Evaluating Customer Experience through Customer Journey Mapping and Service Blueprinting at Edmonton Public Library: An Exploratory Study
Bibliographic record
Abstract
This paper presents an overview of the design, implementation, and findings of an exploratory project to evaluate customer experience at Edmonton Public Library (EPL). The EPL Intern Librarian Project had three objectives: to establish the current state of customer experience at EPL, identify pain points, and develop recommendations for improvement. The study used the ethnographic methods of Customer Journey Mapping and Service Blueprinting to directly engage with customers and staff to produce visual documents reflecting respondents’ customer experience at EPL. In order to gather data for Customer Journey Maps, participants were simultaneously observed and interviewed as they completed different activities in the library. During the creation of the Journey Maps, pain points were identified. Interactive focus groups and interviews with EPL staff members unpacked pain points and informed the creation of corresponding Service Blueprints. Based on the findings, a number of recommendations were proposed to improve the customer experience including enhanced digital wayfinding,clearly identifiable catalogue stations, and revised website FAQs. Suggestions for applying these methods include the use of multiple techniques for participant recruitment, focusing on specific library activities, and actively promoting the project internally.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".