Customer Experience across Touchpoints along the Customer Journey. A Text Mining Analysis
Bibliographic record
Abstract
To extract data from Foursquare, we wrote a Python script that uses the Foursquare Developers API to collect relevant data. The Foursquare API refers to physical stores in a city as “venues” and the user-written reviews of the experience in the stores as “tips”. To extract the tips for the venues, we used the “venues search” functionality of the API (Foursquare, 2020), which allowed us to search for "venues" within a certain radius (The maximum supported radius is currently 100,000 meters) from the center of a specific city. We selected Alpha and Beta, cities from the United States (New York, Chicago, Los Angeles, Washington, Boston, and San Francisco), the UK (London), Canada (Toronto), and Australia (Melbourne, Sydney) based on the “Global City Index”. Using the Twitter Search API through a Python script with the Tweepy library, we initially collected 3,114,924 anonymized tweets, which mentioned 75 retail brands between September 27th, 2018, and April 10th, 2019. This data represents a broad cross-section of English-written tweets on the retail brand’s categories in the United States.
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.
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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.012 |
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".