Understanding the Customer Journey to Create Excellent Customer Experiences in Bookshops
Bibliographic record
Abstract
This study investigates the customer journey and identifies the drivers of excellent customer experience in bookshops. Five research methods—in-depth interview, focus group, participant observation, Zaltman metaphors elicitation technique, and collective stereographic photo essay—were run on eleven Italian bookshops involving more than 1 100 individuals overall. The contribution of this study is twofold. First, it illustrates the process to adopt when mapping the customer journey and analyzing the customer experience. Specifically, it proposes that customer experience can be deeply understood only via a broad research design involving several different profiles of participants, that are managers and booksellers, customers of different familiarity with bookshops (infrequent, frequent and loyal customers), people that were not familiar with the investigated bookshops but that have been invited on purpose, and people that have special interactions (café and events) with the bookshops. Second, results show three key aspects of the topic: (1) The customer experience world, based on rituals not on transactions; (2) The drivers of excellent customer experience in bookshops, which are customization, integration, and participation; (3) The complex role and broad competences of the ideal bookseller.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".