Strategic conversations with your customers helps hone the planning process
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.000 | 0.000 |
| 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 | 0.001 |
| 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 it