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Big Data are hard to capture, store, search, share, analyze, and visualize. Without any doubts, Big Data represent the new frontier of data analysis, although their manipulation is far to be realized by standard computing machines. In this paper, we present a strategy to process and extract knowledge from Facebook data, in order to address marketing actions of a pharmaceutical company. The case study relies on a large Italians sample, interested in wellness and health care. The results of the study are very sturdy and can be easily replicated in different contexts.

Customer satisfaction for banking services is, arguably, a construct that develops and changes over time for a number of different endogenous and exogenous factors (modification of customers contract terms, transparency of banking transactions and financial services, bank charges, customer relationships, changes of market conditions an so on). Measuring change requires a longitudinal perspective: it can be carried out collecting measurements on the same individuals across multiple time points. The aim of this paper is to analyze the dynamic customer satisfaction of particular sub-groups of clients estimating a Latent Growth Curve Model. Results show interesting behavior differences to address strategic management decisions.

The most important company asset seems to be Customer Satisfaction (CS), which banks, in the recent years, have frequently analyzed. For reaching such target, a dynamic Factor Analysis offers an effective way of merging information about clients and their preferences evolution. In our work we performed a dynamic Customer Satisfaction study, by means of a three-way factorial analysis, and we also introduced a new index of shift and shape (SSI), to synthesize information about every customer, cluster or typology. We considered a national bank case, with spread network, evaluating results provided by a questionnaire framed according to the SERVQUAL model. The information employed was obtained via a Customer Satisfaction survey repeated three times (waves). We performed the dynamic factorial model and we illustrated the usage of SSI as a new measure of trajectories’ dissimilarity. Finally, we showed our results which highlight promising performances of our index.

The growth of Internet and the information technology has generated big changes in subjects communication, that, nowadays, oc- curs through social media or via thematic forums. This produced a surge of information that is freely available: it offers the possibility to com- panies to evaluate their credibility and to monitor the ”mood” of their markets. The application of Sentiment Analysis (SA) has been proposed in order to extract, via objective rules, positive or negative opinions from (unstructured) texts. Communication literature, instead, highlights how such polarization derives from a subjective evaluations of the texts by the receivers. In business applications the receiver (i.e. marketing manager) is leaded by the values and the mission of the company. In our paper we propose a strategy to fit brand image and company values with a subjective SA, a probabilistic Kernel classifier has been employed to get discrimination rule and to rank classification results.

Today, the most important asset for a bank is its customer and therefore, the main targets to achieve by management are: knowledge of his needs, anticipation of his concerns and to distinguish itself in his eyes. The awareness that a satisfied customer is a highly profitable asset effort to provide a satisfactory service to the customer by diversifying its services. This paper aims to analyze the customer evaluation evolution of the main attributes of banking services to catch differences among the clusters and time lags through a dynamic factorial model. We propose a new system of weights by which assessing the dynamic factor reduction that is not optimal for all the instances considered across different waves. An empirical study will be illustrated: it is based on customer satisfaction data coming from a national bank with a spread network throughout Italy which wanted to analyze its reduced competitiveness in retail services, probably due to low customer satisfaction.

An empirical study, based on a sample of 27.000 retail customers, has been curried out: The management of a national bank with a spread network across Italian regions wanted to analyze the loss in competition of its retail services, probably due to a loss in customer satisfaction. The survey has the aim to analyze weaknesses of retail services, individuate possible recovery actions and evaluate their effectiveness across different waves (3 time lags). Such issues head our study towards a definition of a new path measure which exploits a dimension reduction obtained with Dynamic Principal Component Analysis (DPCA). Results which shown customer satisfaction configurations are discussed in the light of the possible marketing actions. © Springer International Publishing Switzerland 2013.