Mapping headphone brand positioning from e-commerce reviews with large language models

Online consumer reviews encode rich signals about brands, yet converting this unstructured text into actionable positioning insight remains challenging. This study proposes an LLM-driven framework that extracts structured brand perception variables from e-commerce reviews and demonstrates it in the headphone category. Using an analytic file of 5,895 review-level observations covering Sony, Bose, JBL, and Beats, we employ a structured GPT-5.4 prompt to extract multidimensional brand perception indicators from each review, capturing associations, judgments, emotional attachment, advocacy, and attribute focus. A manual validation exercise based on 400 stratified reviews showed meaningful agreement between the LLM-coded outputs and an adjudicated human benchmark. The results reveal clear brand separation. Bose leads on perceived quality (4.20/5), recommendation rate (78.3%), and strong emotional connection (39.9%), whereas Sony achieves the highest value perception (3.97/5). Beats and JBL occupy weaker quality positions, though Beats retains a comparatively stronger emotional profile. Patterns in attribute emphasis also help distinguish the brands: Bose reviews are distributed across sound quality, active noise cancellation, and comfort; Sony reviews emphasize sound quality, comfort, and value; JBL reviews are heavily concentrated around sound quality; and Beats reviews emphasize sound quality, value, and design. Negative reviews provide additional diagnostic insight, revealing durability and fit issues for JBL and perceived value concerns for Beats. The study contributes primarily to LLM-assisted marketing analytics and applied brand diagnosis by showing how constrained and human-validated LLM coding can transform review text into a scalable, multidimensional brand monitoring instrument. Brand equity and positioning theory are used as conceptual foundations for organizing the extracted perception variables rather than as targets of theoretical extension.

Authors

Institutions

Publication Details

Journal
PLoS ONE
Published
2026-11-11
DOI
https://doi.org/10.1371/journal.pone.0361055
Primary Topic
Digital Marketing and Social Media
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
NOV
article

Mapping headphone brand positioning from e-commerce reviews with large language models

Hengshan Cao, Jian Li
PLoS ONE
Digital Marketing and Social Media
article

Mapping headphone brand positioning from e-commerce reviews with large language models

Hengshan Cao, Jian Li
article en

Abstract

Online consumer reviews encode rich signals about brands, yet converting this unstructured text into actionable positioning insight remains challenging. This study proposes an LLM-driven framework that extracts structured brand perception variables from e-commerce reviews and demonstrates it in the headphone category. Using an analytic file of 5,895 review-level observations covering Sony, Bose, JBL, and Beats, we employ a structured GPT-5.4 prompt to extract multidimensional brand perception indicators from each review, capturing associations, judgments, emotional attachment, advocacy, and attribute focus. A manual validation exercise based on 400 stratified reviews showed meaningful agreement between the LLM-coded outputs and an adjudicated human benchmark. The results reveal clear brand separation. Bose leads on perceived quality (4.20/5), recommendation rate (78.3%), and strong emotional connection (39.9%), whereas Sony achieves the highest value perception (3.97/5). Beats and JBL occupy weaker quality positions, though Beats retains a comparatively stronger emotional profile. Patterns in attribute emphasis also help distinguish the brands: Bose reviews are distributed across sound quality, active noise cancellation, and comfort; Sony reviews emphasize sound quality, comfort, and value; JBL reviews are heavily concentrated around sound quality; and Beats reviews emphasize sound quality, value, and design. Negative reviews provide additional diagnostic insight, revealing durability and fit issues for JBL and perceived value concerns for Beats. The study contributes primarily to LLM-assisted marketing analytics and applied brand diagnosis by showing how constrained and human-validated LLM coding can transform review text into a scalable, multidimensional brand monitoring instrument. Brand equity and positioning theory are used as conceptual foundations for organizing the extracted perception variables rather than as targets of theoretical extension.

PLoS ONEVol. 21(11)
Wuxi Taihu University (CN)
Digital Marketing and Social Media
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.

Laika access coming soon