Customer Intelligence in the Cultural Sector: The Case of a Quebec Museum
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
\nThe COVID-19 pandemic has heightened the importance of digital strategies and data use in museums, transforming how they deliver services and engage with audiences. As a result, museums have adapted to new audience profiles and digital methods of organizing and accessing collections to thrive in the post-pandemic era. These organizations have thus generated more and more data without the human and technological resources required to perform the analyses. In addition, the lack of consensus regarding an analytical framework in the academic literature complicates the implementation of customer intelligence among Small and medium-sized enterprises (SMEs) and non-profit organizations. To respond to this challenge, this study proposes a customer intelligence process for implementing customer intelligence around four stages: Acquisition - Commitment - Experience - Lifetime Value, associated with three states: Data - Analysis - Key Performance Indicators. The POP Museum, in the Province of Québec, Canada, which has developed online exhibitions and currently uses social media to better get to know its customers, follow their customer journey and ultimately develop customer intelligence, is presented as a case study.\n
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it