Sales Channel as a Strategic Choice - SME Managers Seeking Profitability
Bibliographic record
Abstract
The development of various types of sales channels has increased interaction opportunities between corporations and end users. Furthermore, customers have gained increasing influence in business due to the development of performance measures as customers’ ability to influence corporate profitability has gained importance in economic governance. This affects today’s business leaders’ strategic decisions because they need to decide which marketing channels they should use to reach out to end users and how they should proceed in order to increase customer profitability. The purpose of this study is to identify the driving forces behind the strategic choices of the sales channel(s) and to describe the managers’ views of the customers behind the marketing channels and how satisfied, loyal and profitable these customers are. The strategic choices and motivations behind the choice of sales channels are characterized partly by managers’ personal experiences and views on the channel opportunities, coincidences and the fear that some channels will compete with each other. The majority of the managers believe that customers behind sales channels have different preferences, but they use no analysis of how satisfied and loyal their customers are. Satisfaction and loyalty are factors that should be analyzed because they are assumed to lead to customer profitability.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".