Bibliographic record
Abstract
Every sales cycle has some degree of inherent volatility. A big customer could, for instance, go bankrupt or a major deal could fall through. But there?s one type of volatility that many executives seem to think is a kind of natural law: At the beginning of every quarter, sales tend to falter; at the end, they often surge. This roller coaster can be a huge problem when major deals fail to materialize at the end of the quarter, leaving a shortfall. According to the author, such kinks in the sales cycles can be smoothed out, but doing so requires a fundamental change in how sales activities are prioritized. The typical sales process is like a funnel: At the bottom are the deals that are nearest to being closed; in the middle are other prospects in the works; and above are numerous promising leads. Companies typically work their funnels from the bottom up. After all, why not concentrate on the surest opportunities first and leave the less certain ones for last? But that prioritization strategy is the fundamental cause of the sales roller coaster. The author of this article argues that for a more continuous ? and predictable ? revenue stream, firms should prioritize the three areas of the funnel in the following way: bottom, above and then middle.
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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".