Dynamic Segmentation of Financial Markets: A Mixture Latent Class Markov Approach

The topic of market segmentation is still one of the most pervasive in marketing. Among clustering techniques, finite mixture models have gained recognition as a method of segmentation with several advantages over traditional methods; one variant of finite mixture models – the latent class (LC) model – is probably the most popular. The LC approach is innovative and flexible, and can provide suitable solutions to several problems regarding the definition and development of marketing strategies, because it takes into account specific features of the collected data, such as their scale of measure (often ordinal or categorical, rather than continuous), their hierarchical structure and their longitudinal component. Dynamic segmentation is of key importance in many markets where it is unrealistic to assume stationary segments due to the dynamics in consumers' needs and product choices. In this paper, a mixture latent class Markov model is proposed to dynamically segment Italian households with reference to financial products ownership. The mixture approach is compared with the standard one in terms of its ability to forecast customers' behaviour in the reference market.

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Longitudinal models for dynamic segmentation in financial markets

International Journal of Bank Marketing ◽

10.1108/ijbm-05-2016-0068 ◽

2017 ◽

Vol 35 (3) ◽

pp. 431-446 ◽

Cited By ~ 3

Author(s):

Francesca Bassi

Keyword(s):

Financial Markets ◽

Market Segmentation ◽

Statistical Models ◽

Latent Variable ◽

Latent Class ◽

Transition Probabilities ◽

Initial Point ◽

Market Segments ◽

Content Type ◽

Over Time

Purpose Dynamic market segmentation is a very important topic in many businesses where it is interesting to gain knowledge on the reference market and on its evolution over time. Various papers in the reference literature are devoted to the topic and different statistical models are proposed. The purpose of this paper is to compare two statistical approaches to model categorical longitudinal data to perform dynamic market segmentation. Design/methodology/approach The latent class Markov model identifies a latent variable whose states represent market segments at an initial point in time, customers can switch to one segment to another between consecutive measurement occasions and a regression structure models the effects of covariates, describing customers’ characteristics, on segments belonging and transition probabilities. The latent class growth approach models individual trajectories, describing a behaviour over time. Customers’ characteristics may be inserted in the model to affect trajectories that may vary across latent groups, in the author’s case, market segments. Findings The two approaches revealed both suitable for dynamic market segmentation. The advice to marketer analysts is to explore both solutions to dynamically segment the reference market. The best approach will be then judged in terms of fit, substantial results and assumptions on the reference market. Originality/value The proposed statistical models are new in the field of financial markets.

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