The Italian market of sparkling wines: Latent variable models for brand positioning, customer loyalty, and transitions across brands' preferences

Abstract

The Italian market of sparkling wines has undergone a strong expansion driven by what can be defined as the "Prosecco phenomenon." It has extended consumption reaching new and more complex segments with a wide offer of appellations, brands, and prices. We aim to evaluate the Italian market of sparkling wines to figure out the competitive associations among the major brands. We propose two different analyses to disentangle distinctive groups of brands. First, using the information on scanner purchases of sparkling wines recorded by a consumer panel over a 2-year period, and appropriate specifications of the latent class model, we cluster homogeneous groups of winery brands for the product attributes they propose to the market. Then, we analyze consumers' brand preferences from a dynamic perspective by employing a hidden Markov model to identify segments of brands perceived as similar. These results shed light on loyalty behavior and its evolution over time in the market.

Understanding consumer attitudes towards ecolabeled food products: A latent class analysis regarding their purchasing motivations

“…For this purpose, latent class analysis (LCA) has been implemented, as it allows segmenting consumers into groups based on certain nonobservable characteristics derived from observable variables. In this sense, previous studies show the strengths of this approach to segment markets (Vermunt & Magidson, 2004) and its advantages over other segmentation methods (Bassi et al, 2020; Bassi, 2007, 2009).…”

Modelling the group dynamics in the wine industry

“…From a similar but different angle, Bassi et al (2020) evaluated the market of sparkling wines to identify homogeneous groups of winery brands and to analyse their competitive relations. The study showed loyalty and variety seeking by customers.…”

“…The application of this model is relatively new in the wine industry. In the Italian market, Bassi et al (2020) estimated a basic HMM to infer clusters according to a categorical response variable whose categories represented the brands of sparkling wines. Later, Bassi et al (2021) proposed a multivariate HMM by using three categorical response variables collected over time: price, sugar content and appellation.…”

Market Segmentation and Dynamic Analysis of Sparkling Wine Purchases in Italy

“…The data refer to the portion of the entire panel of 5,155 households that made at least one purchase in the reference period. Data contain detailed information on purchases: time and place of purchase, intrinsic and extrinsic wine attributes, such as the appellation, label, brand, producer location, and price (Bassi, Pennoni, and Rossetto, 2020).…”

“…The HMM is employed in marketing applications since it allows us to identify hidden types of prospects in the acquisition process and connect the dynamics across different customer profiles. As far as we know, the HMM was applied in the field of wine economics for the first time by Bassi, Pennoni, and Rossetto (2020). In this article, we use an HMM for multivariate categorical responses with a longitudinal structure, accounting for the sparsity of the purchases while considering an unbalanced panel and testing covariates' effects.…”