Customer Segmentation Analysis in Major Sporting Goods Companies and its Influence on Strategic Marketing Decisions
Bibliographic record
Abstract
This article critically reviews the use of customer segmentation in distribution channels as an analytical instrument to support strategic marketing decisions. Major sporting goods companies with a very fragmented distribution system have been selected as sampling group. With access to the top management, this article gives insight into decision processes and strategies in the sporting goods industry. The study focuses on the analysis of an existing customer portfolio as well as on the future development of individual segments. The research exceeds the reduction of customer segmentation to a “marketing strategy” by showing the impact on structural and strategic decisions of corporations. Thus the scope of this study amplifies considerably the common definitions and objectives of the customer segmentation concept and implicates the deduction of a strategic target as assumed. A qualitative exploratory research design has been chosen and the gained information has been processed with personal in-depth interviews to gather reliable primary data. The findings illustrate that converting customer segmentation into a set of mid-term marketing decisions offers potential for improvement. Marketing and global strategic decisions as well as change management requirements in the sporting goods industry may benefit from this research in adapting their focus.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".