Driving superior demand forecasting accuracy by incorporating customers and prospects behavior outside the firm environment
Bibliographic record
Abstract
This research study investigates the development of an empirically viable and scalable demand forecasting model, for the product line decision problem, whose specification incorporates explanatory variables from three data sources: 1) internal, 2) competitive, and 3) customers and prospects behavior outside the firm environment. This research study also aims to empirically demonstrate that incorporating explanatory variables that capture customers and prospects behavior outside the firm environment improves forecasting accuracy results. It does so by evaluating the forecasting accuracy of the proposed demand forecasting model against that of three candidate models, a benchmark model and two additional models that are representative of those employed in existing product line decision studies reviewed. This research study relies on the collection and analysis of both secondary data from Popeye’s Supplements (Popeyes), one of Canada’s leading sports nutrition retailers with over 125 locations coast to coast, and primary data. This research study offers three key contributions to theory. First, this research study demonstrates an empirically viable demand forecasting model specification that incorporates explanatory variables from three data sources: 1) internal, 2) competitive, and 3) customers and prospects behavior outside the firm environment. Doing so addresses the call for future research in the highly cited paper by Wedel and Kannan (2016) to collect data that captures customers and prospects behavior outside the firm environment to alleviate the problem that activities of (potential) customers with competitors are unobservable in internal data and may help fully determine their path to purchase. This study made use of secondary data from Popeyes, which was comprised of internal data (i.e., store-level operational data across 11 of its stores, and 12,357 products in total) and competitive data, spanning 2 years. Moreover, to complement this secondary data, this study collected primary data through a questionnaire-based survey to capture customers and prospects behavior outside the firm environment. Second, this research study empirically demonstrates that employing a demand forecasting model specification that incorporates explanatory variables about customers and prospects behavior outside the firm environment improves forecasting accuracy results. Third, this research study demonstrates a demand forecasting model that is scalable for industry size problems. A demand forecasting model is considered scalable for industry size problems when it 1) does not oversimplify the problem, and 2) is applicable at the individual product level. The proposed demand forecasting model meets both of these requirements.
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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 | 0.005 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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 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".