Key account teams: success factors for implementing strategy
Bibliographic record
Abstract
Purpose The purpose of this paper is to explore a contributing factor – communication – within a team as well as with a client. Organizations that rely on key account teams for strategy implementation may find their “best laid plans” thwarted by communication problems associated with such teams. Design/methodology/approach This paper is based on depth interviews and content analysis. The authors analyze what team members and team leaders say and count positive/negative terms about communication within teams and with clients. These counts of such terms as a proportion of interview length are compared to the actual team success. Findings Negative comments about communication within the team focus on difficulty and positive comments focus on support. Interestingly, however, the best indicator of whether a team has succeeded in selling its key account is the extent of negative expressions about communication from key account managers. Presumably, the structure of key account teams gives them an extra leadership burden, and the authors’ see a relationship between their perception of communication shortcomings and success or failure. Research limitations/implications The authors recommend investigating communication issues when strategy implementation depends on key account teams, but because this study is conducted using a qualitative method with one company, its results cannot be projected. The authors simply demonstrate what a company could learn from conducting its own study and comparing results to its sales success. Originality/value Little research has examined communication in key account teams or linked it to sales success.
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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 | 0.006 | 0.047 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".