How to identify line extensions that survive

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.

Authors

Institutions

Publication Details

Journal
Journal of Product & Brand Management
Published
2025-09-19
DOI
https://doi.org/10.1108/jpbm-10-2024-5527
Primary Topic
Evolutionary Algorithms and Applications
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

How to identify line extensions that survive

Jenni Romaniuk, Kirsten Victory, John Dawes, Arry Tanusondjaja et al.
Journal of Product & Brand Management
Evolutionary Algorithms and Applications
article

How to identify line extensions that survive

Jenni Romaniuk, Kirsten Victory, John Dawes, Arry Tanusondjaja, Magda Nenycz‐Thiel
article en

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.

Journal of Product & Brand Management
University of South Australia (AU)
Openalex Percentile: Top 87%
Evolutionary Algorithms and Applications
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.