Market Segmentation and Dynamic Analysis of Sparkling Wine Purchases in Italy

Francesca Bassi,Luca Rossetto,Fulvia Pennoni

doi:10.1017/jwe.2021.20

Francesca Bassi, Luca Rossetto + Show 1 more

Open Access

https://doi.org/10.1017/jwe.2021.20

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Abstract

AbstractThe Italian market of sparkling wines increases as volume and assortment (such as brands, appellations, typologies) mainly because of sparkling Prosecco consumption. We investigate the repeated purchase behavior of sparkling wines in two years within the supermarket channel through scanner data collected from a consumer panel. We propose a Hidden Markov Model to analyze these data, assuming an unobservable process to capture consumers’ preferences and allowing us to consider purchases sparsity over time. We consider multivariate responses defining types of purchases, namely price, appellation, and sugar content. Customers’ covariates influence the initial and transition probabilities of the latent process. We identify five market segments, and we track their evolution over time. One segment includes Prosecco-oriented consumers, and we show that loyalty to Prosecco changes strongly over time according to the region of residence, income, and family type. The findings improve the understanding of the market and may provide evidence to design successful marketing strategies. (JEL Classifications: C33, C51, D12, L66)

Highlights

In the last decade, the vigorous growth in the sparkling wine market has been coupled by substantial increase growth in brands, appellations, price range, as well as other attributes to catch consumers’ attention
We propose to fill this gap by considering the market dynamics of repeated purchases and the socio-demographic factors that may favor them
We propose a multivariate statistical model, namely a Hidden Markov Model (HMM) (Bartolucci, Farcomeni, and Pennoni, 2013)

Summary

Introduction

The vigorous growth in the sparkling wine market has been coupled by substantial increase growth in brands, appellations, price range, as well as other attributes (e.g., packaging) to catch consumers’ attention. Some recent contributions deal with spatial differences in wine and alcohol markets (Hart and Alston, 2020), while less attention is devoted to studying changes in purchase behavior over time. We evaluate how consumers and their family features affect the probability to belong to a certain category or profile at the initial period and retaining or changing this category over time. To analyze these data, we propose a multivariate statistical model, namely a Hidden Markov Model (HMM) (Bartolucci, Farcomeni, and Pennoni, 2013). Compared to the currently existing models in the literature, the HMM does not require strong parametric assumptions, and it is robust for model misspecification

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