JOURNAL ARTICLE
Strategic Visual Merchandising of New and Open-Box Products: Evidence from Experiment and Retail Data.
Published In: Management Science (INFORMS), 2024, v. 70, n. 4. P. 2047 1 of 3
Database: Business Source Ultimate 2 of 3
Authored By: Ertekin, Necati; Ding, Yuanyuan; Donohue, Karen 3 of 3
Abstract
This article investigates the impact of two visual merchandising (VM) strategies—side-by-side (SBS) and separate (SEP)—used by retailers to sell returned products as discounted open-box items alongside new products. Through a controlled online experiment and empirical analysis of proprietary data from a North American jewelry retailer, the study finds that the SEP strategy increases new product sales by 3.6% but also raises return rates by 3.5 percentage points compared to the SBS strategy, which lowers returns but results in lower sales. The research identifies cannibalization of new product sales by open-box products as a key mechanism underlying these effects and shows that the SEP strategy decreases open-box product sales by 2.7% relative to SBS. Profitability analysis reveals that selling open-box products enhances overall retailer profit by about 3.3%, with the optimal VM strategy varying by product profile: SBS suits low-priced, low-demand, high-uncertainty products, while SEP benefits medium-priced, high-demand, low-uncertainty products. The study suggests that customizing VM strategies based on product mix can improve profitability, potentially increasing retailer profit by 3.2%.
Additional Information
- Source:Management Science (INFORMS). 2024/04, Vol. 70, Issue 4, p2047
- Document Type:Article
- Subject Area:Business and Management
- Publication Date:2024
- ISSN:0025-1909
- DOI:10.1287/mnsc.2023.4786
- Accession Number:176632999
- Copyright Statement:Copyright of Management Science (INFORMS) is the property of INFORMS: Institute for Operations Research & the Management Sciences and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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