Bibliographic record
Abstract
Purpose The purpose of this paper is to examine commonly relied upon product sampling strategies, direct‐to‐consumer sampling and event sampling, to determine which method can deliver the greatest return on investment in a variety of situations. Design/methodology/approach The paper explores information and data collected via case studies in actual programs presented to brands. The trial, sample, and purchase numbers were actual averages from sampling effectiveness studies for these types of programs. Findings The paper identifies and segments different types of products and the method by which the products are most effectively implemented into trial and sampling programs. ICOM reveals hard statistics on the return on investment of programs utilizing multiple methods of sampling including point‐of‐use, direct mail and event sampling. Practical implications Marketers should follow the STEPS outlined in this study to apply the best brand sampling strategy for a given product. Knowledge of targeted consumer base along with careful pre‐event analysis will deliver the best return on investment for a trial campaign. Originality/value The paper reveals reasons for a growing shift in corporate budgeted marketing dollars from event marketing to direct consumer product sampling. While event sampling is not always an ineffective or inferior marketing method,the reader can discover methods for a pre‐event return on investment analysis that will reveal the sampling strategy sure to deliver the most “bang” for the marketing “buck”.
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 | 0.075 | 0.175 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.052 | 0.007 |
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".