营销
业务
产品(数学)
杠杆(统计)
新产品开发
计算机科学
机器学习
几何学
数学
作者
Lonnie Hobbs,Aleksan Shanoyan,Zelia Wiley,Greg Aldrich
出处
期刊:Agribusiness
[Wiley]
日期:2023-10-04
卷期号:40 (4): 885-907
被引量:16
摘要
Abstract Pet‐food industry growth and demand for specialized product offerings have generated new opportunities for companies to enhance their competitiveness and profitability through effective product differentiation. Recent rise in e‐commerce and technological advancements for capturing and analyzing online customer review data provide new opportunities for large‐scale assessment of customer perceptions of product attributes. This paper presents an integrated qualitative and quantitative approach for utilizing online customer review data to generate insights for informing pet‐food industry product and marketing decisions. The results from the analysis of 5632 online reviews of two pet‐food products highlight (i) product attributes that effectively differentiate (or put on par) two competing pet‐food products, (ii) identify product attributes that are most important for customer postpurchase experience, and (iii) estimate the effect of specific attributes on customers' positive (or negative) postpurchase experience. Specific pet‐food attributes examined include packaging, health/benefit, ingredient, taste, smell, appearance/form, natural/organic, processing, sourcing, small/large breed, price, and service. The advantages of this approach include its ability to leverage large amounts of easily accessible data and its generalizability to the analysis of other consumer goods that have significant online sales and customer review data [EconLit Citations: Q13, M31].
科研通智能强力驱动
Strongly Powered by AbleSci AI