Segmenting with Big Data Analytics and Python. A Quantitative Analysis on the Household Savings

Despite a recent sharp decline in per-capita net wealth, according to the national balance sheets of the most advanced economies, Italian private households present higher rate among the wealthiest and least indebted in Europe (Acciari and Morelli, 2021). Italian personal precautionary saving bent represents a cultural trait of individual financial behaviour at a national level (Fuchs‐Schündeln et al., 2020). Nevertheless, recent demographic changes (i.e., households with multiple earners, increase in longevity, decline in fertility and so on) had a deep effect on household saving and its dynamic over time. Recently, the COVID-19 outbreak gave a new jump to the households’ savings worldwide, especially in the advanced economies (Romei, 2021) and also in Italy (Ercolani et al., 2021). This circumstance might depend on the unstable economic situation that leads consumers to adopt a more precautionary behaviour, which evolve in a high saving rate (Statista, 2021). Therefore, the research on this topic is crucial to predict future behaviours and finds a new, very relevant fellow in the support provided by digital transformation (Reis et al., 2018). On this stream, this study underlines as by virtue of advanced analytics tools, household saving behaviours information and big data analytics may support data-driven decision approaches addressed the managing of complex relationships in the financial arena. More punctually, by means of an exploratory and predictive analysis based on big data analytics and the use of machine learning, the aim of this study is to provide an extensive customer profiling in the household saving sector in Italy, supporting a data-driven decision-making approach.