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Article
Publication date: 17 August 2021

Kathleen Kelley, Marielle Todd, Helene Hopfer and Michela Centinari

This study aims to characterize several wine consumer segments who were “likely” to sample (i.e. taste before purchasing) wine from vineyards using cover crops, a…

Abstract

Purpose

This study aims to characterize several wine consumer segments who were “likely” to sample (i.e. taste before purchasing) wine from vineyards using cover crops, a sustainable production practice that reduces herbicide applications, and identify those with a greater probability of being a viable target market based on survey responses.

Design/methodology/approach

A total of 956 wine consumers from the Mid-Atlantic and boarding US states were separated into segments based on an ECHAID (exhaustive Chi-square automatic interaction detector) classification tree from internet survey responses.

Findings

Out of the 12 created segments, 6 (n = 530, 72% of training data) contained participants who were at least 1.02 times (index score =102%) more “likely” to try the wine compared to the overall sample and were willing to pay $18.99 for a 750-mL bottle of the wine, which included a $1 surcharge to cover associated production costs. Of these, three (n = 195, 26%) had the greatest potential for which a marketing plan could be developed (index scores of 109%–121%), with over half in each segment willing to pay $20.99 for the bottle of wine, which could motivate growers to consider implementing this sustainable strategy.

Originality/value

Although several segments of participants were “likely” to sample the sustainably produced wine, an ECHAID classification tree allowed us to identify participants who would not pay $18.99 for a 750-mL bottle of wine, even after learning about the use of cover crops and the trade-off ($1 bottle surcharge). By narrowing the number of potential “likely” segments to those with a greater potential of sampling the wine, more purposeful marketing strategies can be developed based on demographics, attitudes, and behaviors defined in the model.

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