Customer value propositions in business markets.
Bibliographic record
Abstract
Examples of consumer value propositions that resonate with customers are exceptionally difficult to find. When properly constructed, value propositions force suppliers to focus on what their offerings are really worth. Once companies become disciplined about understanding their customers, they can make smarter choices about where to allocate scarce resources. The authors illuminate the pitfalls of current approaches, then present a systematic method for developing value propositions that are meaningful to target customers and that focus suppliers' efforts on creating superior value. When managers construct a customer value proposition, they often simply list all the benefits their offering might deliver. But the relative simplicity of this all-benefits approach may have a major drawback: benefit assertion. In other words, managers may claim advantages for features their customers don't care about in the least. Other suppliers try to answer the question, Why should our firm purchase your offering instead of your competitor's? But without a detailed understanding of the customer's requirements and preferences, suppliers can end up stressing points of difference that deliver relatively little value to the target customer. The pitfall with this approach is value presumption: assuming that any favorable points of difference must be valuable for the customer. Drawing on the best practices of a handful of suppliers in business markets, the authors advocate a resonating focus approach. Suppliers can provide simple, yet powerfully captivating, consumer value propositions by making their offerings superior on the few elements that matter most to target customers, demonstrating and documenting the value of this superior performance, and communicating it in a way that conveys a sophisticated understanding of the customer's business priorities.
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 | 0.009 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.005 | 0.018 |
| Scholarly communication | 0.018 | 0.022 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".