Bibliographic record
Abstract
While the principles of needs or benefit-based market segmentation have been long established, its potential value as a route to a stronger market understanding and ultimately competitive advantage has been largely untapped in pharmaceutical marketing research, with internal process rather than market focus driving market understanding. Many of the tensions around the use of geodemographics for market segmentation in the consumer work are mirrored in the use of classification systems and diagnosis in the pharmaceutical environment. This paper presents the application of needs-based segmentation - market segmentation based on understanding how physicians use perceptions of patient needs to group patients and then use this understanding to make appropriate treatment decisions specific to each patient group. The need to include patient needs in market segmentation is taken into account by considering the consequences of not segmenting the market strategically. The approach is illustrated to show how valuable outputs are generated and how direction may be provided across the brand development process. The potential impact and application of this novel thinking within pharmaceutical companies is reviewed. This paper shows how a benefit-based or needs-based segmentation of the market provides a more potent view of the market, and argue that market segmentation should therefore be fashioned to reflect this.
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.
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 | 0.038 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.005 | 0.065 |
| Scholarly communication | 0.012 | 0.015 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.009 | 0.009 |
| Insufficient payload (model declined to judge) | 0.005 | 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".