Numeric Big Data Analytics for Successful Decision Making: A Case Study from the Retail Industry in Canada
Bibliographic record
Abstract
This paper proposes that the successful analysis of big data in the retail industry is the key to successful business practice, and is an essential ingredient for successful decision making in many industries. In this paper, we present a case analysis of the use of numeric big data analytics to discover the decline in profits while sales figures are rising during promotion weeks at a Toronto-based retail chain store. We also discuss why big data analytics should be seen as a key practice in the retail industry due to the massive amounts of data that is generated in a daily basis, especially, in the retail food industry. The paper sheds light on the ability of analysts to transform the massive amounts of data into information and knowledge to discover hidden problems and make wise management decisions. If properly analyzed, big data can produce the needed information to uncover internal strengths and weaknesses, and external opportunities and threats, and to make data-informed management decisions.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".