Optimizing Commercial Teams and Territory Design Using a Mathematical Model Based on Clients’ Values: A Case Study in Canada
Bibliographic record
Abstract
This study, set in Nors Construction Equipment ST in Canada, addresses logistical challenges by enhancing commercial team evaluation and market sectorization. Traditional performance assessments relied only on sales, lacking other efficiency measures. This research proposes a mathematical function to combine diverse Key Performance Indicators (KPIs) to better evaluate team effectiveness. Additionally, it aims to optimize the sales territory assignment, improving resource allocation across Canada’s expansive, sparsely populated regions. Customer segmentation was conducted using the RFM model, classifying clients into Low-, Mid-, and High-Value groups based on purchasing behavior. For incorporating multiple KPIs in the evaluation of commercial teams’ performance, the Analytic Hierarchy Process (AHP) was used. Sectorization was modeled as a linear programming problem to minimize travel distances while ensuring compact sales territories. Constraints included balancing sales opportunities and customer types across assigned territories. As a result, the proposed optimization model significantly improves operational efficiency through better-balanced sales territories and reduced travel. Improved sectorization enhances market penetration and customer coverage, which is expected to lead to increased sales and support the company’s growth objectives. The mathematical models developed in this study allowed for a deeper understanding of the performance and provided management with tools to refine sales strategies and allocate resources more effectively. The article ends with a discussion on the possibility of ChatGPT being used to replace a mathematician in performing this analysis for the company. It was observed that ChatGPT (version GPT-4o) provided an extremely incomplete solution, evaluating the commercial teams solely based on profit and sales and not addressing the sectorization problem at hand.
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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 | 0.001 | 0.000 |
| 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".