Restaurant Daily Deals: The Operator Experience
Bibliographic record
Abstract
A survey of 273 restaurateurs who have offered daily deals presents a mixed picture regarding the deals? success and the operators? attitudes on past and future deals. About half of the restaurateurs, a substantial majority of which were independents, had offered at least one deal, most of them through Groupon or LivingSocial. These operators reported that about 40 percent of the deal purchasers were new customers, meaning that there was substantial cannibalization of existing customers. However, 35 percent of the first-time deal customers returned to the restaurant without a further incentive. Although many restaurant operators said revenues increased, the number that said they made money on the deal was about the same as those who reported losing money. Additionally, a comparison of deal buyers? attitudes with the restaurateurs? view of the deal buyers found that the operators misjudged their guests in some regards but were correct on others. The operators underestimated deal buyers? likelihood of returning to the restaurant without a subsequent deal, and missed a key characteristic of the deal buyers, which is that they want to be opinion leaders, or ?market mavens.? This aspect of deal buyers can be important for future sales. Given the consumers? attitudes, the best strategy to bring back deal buyers is to offer them a first-class experience so that they can see a restaurant?s full value proposition.
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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 | 0.002 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".