Year-Over-Year Growth Analysis of Restaurant Sales Patterns and Visualization
Bibliographic record
Abstract
Factors in the success of a fast food franchise involve products and marketing targeted at brand consistency, low start-up costs, franchise support, and consumer convenience (Aquino, 2023). Sales fluctuations of a restaurant depend on operational strategies and location characteristics, resulting in business failure. This study explored the sales pattern of Kenny Rogers over eight years and focused on the seasonal shift in the Trece Martires branch's financial direction. The methods applied the Microsoft Power BI Data Visualization, such as Year-Over-Year and Seasonal Analysis, to recognize patterns and contrast trends in separate years. The revenue increases every December due to consumer spending and drops every January after the holiday season. It rose yearly in three years since its first establishment and experienced a 54.93% sales drop in 2020. The profit recovered after a year and multiplied the yearly growth to 134.10% in 2022, while the sales pattern moved slowly until 2024. The researchers suggested upgrading the marketing strategy every first quarter of the year and adapting the holiday season advertising.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".