An Enriched Customer Journey Map: How to Construct and Visualize a Global Portrait of Both Lived and Perceived Users’ Experiences?
Bibliographic record
Abstract
Design is about understanding the system and its users. Although User Experience (UX) research methodologies aim to explain the benefits of a holistic measurement approach including explicit (e.g., self-reported) and implicit (e.g., automatic and unconscious biophysiological reactions) data to better understand the global user experience, most of the personas and customer journey maps (CJM) seen in the literature and practice are mainly based on perceived and self-reported users’ responses. This paper aims to answer a call for research by proposing an experimental design based on the collection of both explicit and implicit data in the context of an authentic user experience. Using an inductive clustering approach, we develop a data driven CJM that helps understand, visualize, and communicate insights based on both data typologies. This novel tool enables the design development team the possibility of acquiring a broad portrait of both experienced (implicit) and perceived (explicit) users’ experiences.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".