Be Direct! Restaurant Social Media Posts to Drive Customer Engagement in Times of Crisis and Beyond
Bibliographic record
Abstract
Restaurants were significantly shaken by the COVID-19 pandemic, which forced them to intensify their use of social media to communicate with customers. Our objective was to identify which digital marketing strategies generated higher customer engagement during the pandemic, according to variations in the intensity of sanitary restrictions. We manually extracted 639 Facebook posts by 16 restaurants in two Canadian cities (one in a metropolitan area, one in a peripheral region), and coded them according to type of verbal move, format (image, text), and emoji use. The engagement rate was two times higher for restaurants in the metropolitan area, which also used three times more emojis per post on average. The engagement rate was also five times higher for nationally branded restaurants than for independent restaurants. When the pandemic hit, restaurants started to use more text and more directive verbal moves to convey crucial and precise information to customers, notably about sanitary restrictions. Emojis and expressive verbal moves also helped increase customer engagement. While being direct was more efficient in times of crisis, directive verbal moves continued to be used after most sanitary restrictions were lifted. Being direct, thus, appears to be a good digital marketing strategy in the “new normal”.
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How this classification was reachedexpand
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".