The pricing opportunity: discovering what customers actually value
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
Purpose Companies cannot capture the full profit potential of their products and services until their managers understand the ideal price points and width of the price range for each product or brand given its position in the marketplace. This article describes the tools and best practices to accomplish this. Design/methodology/approach In the past year or so, Mercer Consulting has conducted 26 discrete choice modeling studies (our version of the modeling is called Strategic Choice Analysis® or SCA) with over 15,000 customers in a wide swath of industries across the U.S., Canada, Germany, and China. Findings Mercer studies show that price (17 percent out of a possible 100 percent) is nowhere near as important a selection factor as product features (65 percent); service features (11 percent), and other features (7 percent) account for the rest of decision‐making. Practical implications The article shows how all businesses can follow the lead of the exemplars in aligning pricing to customer value. Originality/value It clarifies why ideal pricing depends on discrete choice modeling and a number of best practices rather than on price optimization software alone.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| 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