Brushing up on time-honored sales skills to excel in tomorrow’s environment
Bibliographic record
Abstract
Purpose Determining the skills required for salespersons to maximize their effectiveness was the main driver for conducting the present study. In order to identify those necessary skills, this study aims to review various research techniques drawn from multiple disciplines and applied that knowledge to salespersons. Design/methodology/approach This study used a mixed-method methodology. This study began by conducting a literature review and then interviewed experienced salespersons with varied backgrounds to develop a comprehensive list of sales skills and themes and categorize them into competency categories. This study then conducted a quantitative analysis to determine the respective importance of the skills and themes by surveying a sample of internal stakeholders of a multinational company. Finally, this study calculated the reliability and validity of the themes. Findings A total of 206 relevant skills (later reduced to 110) and 28 themes were identified and grouped into three competency categories: conceptual, human/interpersonal and technical. Survey respondents rated the skills and themes higher than the “somewhat important” score of 3 out of 5, with the overall mean importance for skills being in the “important” range (score of 4.27 out of 5). All identified skills were believed to be important to a salesperson’s success. Originality/value This study’s expanded list of sales skills will improve employability, reduce turnover among employees and build better groundwork for fostering learning through work, resulting in better performance. These skills represent a 2020 updated list that could be used for future academic research and training and research in the business world.
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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 | 0.005 |
| 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 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".