Strategic conversations with your customers helps hone the planning process
Bibliographic record
Abstract
This paper makes the case that customer feedback is a valuable input to a company’s strategy development. The paper also suggests a process for capturing and using this input. By following this process, a company is more likely to identify a strategy in sync with customer and market demands. Closer relationships will also grow between the supplier and customer as a result of the consultative approach to collecting feedback from the customer. The article is based on the author’s experience working with companies to develop fresh information about their businesses before undertaking strategy development. Customers of a company are involved in the market every day and have a different point of view than the company itself. Their observations on issues including new technologies, offerings by competitors, and market demands can help a company prepare for the next threat, or exploit a developing opportunity. The article describes in steps the path to follow if management decides to seek out customers’ views to obtain fresh information for strategic development. By introducing the concept and benefits of strategic customer conversations, and by outlining the steps to take to implement such a system, the reader can now embark on a process of extracting fresh information from customers in order to build or fine‐tune their own strategy. This will help the CEO, VP strategy, head of marketing, or business development head as they plan product, market or service strategies.
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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 | 0.028 | 0.063 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.016 | 0.008 |
| Scholarly communication | 0.019 | 0.023 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.006 | 0.013 |
| Insufficient payload (model declined to judge) | 0.028 | 0.021 |
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".