How to identify line extensions that survive
Bibliographic record
Abstract
Purpose Launching a new product is costly, and failure is likely. Although brands continue to launch new products, there is no clear guidance about when or how to detect the survivors. The purpose of this study is to help brands identify earlier which launches are at risk of failure and which are likely to survive. Design/methodology/approach This study compares the performance of 7,195 survivor (reported sales three years after launch) and 5,294 failed (reported no sales after the first year) line extensions (LEs) to detect patterns in how survival is linked to early indicators. Findings This study shows that the “average” survivor and failed LE has a similar repeat-buyer rate in each quarter over their launch year. Although the repeat-buyer rate is similar for survivor and failed LEs, there is a larger difference in the penetration the “average” survivor and failed LEs achieve over launch. The penetration differences manifest after the first quarter from launch and the divide continues to widen over the launch year. The descriptive and model results suggest penetration is a more sensitive measure to identify survival early on. Practical implications This research provides guidance about when and how to identify likely failure and survivor LEs. The results suggest investing in activities to bolster repeat buying still has a role, but it is more productive for marketers to prioritize activities with a greater opportunity to build trial. The findings from this study give marketers the tools to identify the LEs to keep supporting, and those to remedy or delist. Originality/value This study advances existing knowledge by uncovering the role of trial and repeat. The study uses practitioner-relevant data and performance indicators that are widely adopted in practice to measure new product success.
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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 | 0.004 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 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".