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In the modern financial landscape, there is an increasing interest in using unconventional data sources for predicting Small and Medium Enterprises (SME) default. SMEs, indeed, have a pivotal role in the EU economy constituting 99.8% of all enterprises in the EU-28 non-financial business sector (European Commission (2019)). So far, credit scoring models have been built using only balance sheets indicators, although it has been demonstrated that smaller sized firms do not always comply with financial transparency. In this, work we explore the usage of corporate websites as a further source of information for detection of SMEs default (Crosato, Domenech, & Liberati, 2021). Working with unconventional data raises several challenges in terms of data retrieval, selection and cleaning. On the other hand, it allows to overcome the deterioration of the balance sheet indicators, that generally are published with 2 years of delay with respect to their reference time. We relied on our study on a sample of Spanish SMEs (about 900 firms) whose websites were scraped to obtain online indicators based on the HTML code, as in as in Blazquez and Domenech (2018). The online indicators, merged with balance sheet data, were then analyzed using Machine Learning algorithms (as Random Forest and LS-SVM) and Logistic Regression (as a benchmark) having as input variables both sets of information. Results show the improvement in terms of misclassification rate when the accounting information is augmented by website features. We also provide valuable insights into companies’ websites characteristics related with firms’ financial health. Finally, we show that online indicators are able to predict the future status of some uncommon firms whose accounting indicators would have led to the wrong prediction.

Research in innovation usually builds on conventional data such as balance sheets, surveys, patents, or product catalogs. This paper intends to explore unconventional data, specifically web-scraped data, as an information source for innovation studies, proposing a careful procedure to establish the veracity of the linkage between web-based data and firm-level information retrieved from conventional sources. The study regards a sample of Italian manufacturing small and medium enterprises active in 2016, comprehending both innovative and non-innovative firms. It is based on HTML tags, whilst most of the previous literature worked on the web-pages text and related semantics. Our paper provides evidence that the way HTML language is applied to build a corporate website unveils the capabilities of the owner firm, helping to distinguish innovative from non-innovative SMEs.

As recently stated by EUROSTAT, Small and Medium Enterprises (SMEs) play a crucial role in the economy of European Union (EU). This is particularly true for Italy that has one of the largest share of SMEs in the Euro zone. In such a context, the assessment of the Italian firms creditworthiness is a priority, especially in the early stage of a SMEs life. The aim of this work is to explore the effectiveness of the Feedforward Neural Network for Italian firms defaults prediction in comparison with standard linear models (Logistic and Probit). Our analysis considers the Italian manufacturing SMEs in a short time span (2016-2017). Results, obtained via two different sampling strategies, are not clear-cut.

The objective of credit scoring is to develop accurate rule of classification that aids to distinguish between good and bad clients. In this context, also Statistical Learning (SL) techniques have been explored, for building models that estimate the clients’ probability of insolvency. Although there are some encouraging results in literature, two main issues makes this classification task very hard: (i) high imbalance ratio between the two groups in the target variable and (ii) the effect of hyperparameter settings on overall performance. In this work, Bayesian Optimization (BO) is used to optimize the hyperparameters of a cost sensitive eXtreme Gradient Boosting (XGBoost) model. Experimental results reveal that the proposed solution is a promising starting point for future development

Small and Medium Enterprises (SMEs) are the backbone of the European Union (EU). They account for the majority of the value-added within the non-financial sector and employ two-thirds of the workforce. Therefore the assessment of the SMEs' creditworthiness is a significant issue within both financial and non-financial organizations. Recent works suggest that SMEs' default prediction is more complex than in large enterprises, and canonical models may fall short. At the same time, scholars pointed out similarities with the retail credit risk industry. In this context, black-box machine learning models are the standard, and they achieved remarkable performances. However, these models are less frequently used in enterprise credit scoring as they are essentially uninterpretable. This work aims to model SMEs' defaults using uninterpretable black-box models and compare them with the canonical models. We first explore these models' effectiveness for default prediction compared to standard models (Logistic, Probit, and Binary Generalized Extreme Value). Second, we overcome the lack of interpretability by using recent model-agnostic techniques such as Accumulated Local Effects and Shapley values. We use the dataset to estimate the models and predictions containing Italian Small and Medium Enterprises' information belonging to the manufacturing sector. The results suggest that the black-box models outperform the standard models used as the benchmark.

In recent studies, there has been a growing interest toward tolerance and its implications in social and economic systems. So far, the openness of people to homosexuals or to foreign-born people has been considered the best indicator of tolerant attitudes. In this paper, we consider tolerance as a multi-faced phenomenon involving several different social domains. The aim is to provide some recommen- dations on how to develop a multidimensional index for tolerance in the case of survey’s items measured by Likert-scale. Our discussion relies on a case study about a student survey carried out at Milan-Bicocca University.

Customer Satisfaction management has been long studied as a cross-sectional phenomenon. Today, a shift in emphasis from a static to a dynamic approach appears to be a worthwhile change, although the design and monitoring of panel data is not simple to achieve. In our paper, we propose the usage of pseudo panels, which are less costly and easy to build with the available data for the firms. Our approach is based on a model-free technique: Dual Multiple Factor Analysis. The synthesis of the multivariate table is visualised on a common space that sheds light on customers' trajectories of satisfaction. A real case study of an Italian bank is illustrated. Results related to the dynamic profiling of the clients highlights highly different behaviours that warn the management of the future trends of the services.

Traditionally, customer’s ‘character’ was used as one of the main criteria in credit granting process (Three ‘C’s of Credit). Credit scoring (risk models targeted at screening individuals) replaced it with hard information, such as credit history and demographics. Recent studies reignited the interest in character/ personality by showing its relevance in modelling consumer indebtedness, and more general financial behaviour. Unfortunately, most literature in this field is mainly based on self-reported financial measures. We overcome this limitation by analysing a unique dataset of Italian bank customers that contains hard information about credit and investment performance, in addition to soft measures of personality, financial knowledge and preferences for different levels of customer support. The purpose of this work is twofold: first, we aim at detecting if there are different psychographic segments of banking customers based on personality traits and other soft information. We experiment with clustering and different kernels in Kernel Principal Components Analysis. The comparison indicates the presence of different clustered structures in the data but some partitions may be difficult to interpret from a managerial perspective. Second, we analyse the investment and credit performance of the different customer typologies. We comment on the model fit and predictive accuracy of models for different segments.

Credit scoring models are generally trained using customers credit history and demographics. Recent works in interdisciplinary studies have showed that alternative source of information, such as psychological traits or behavioural attitudes, can aid to improve default prediction. In our work, we would like to verify if an explorative segmentation based on financial knowledge, preferences and personality traits is able to detect different customer typologies also in terms of financial credit performance. This could be the case when the bank management has to deal with new clients or with those without a long banking history. The segmentation has been derived employing Hierarchical clustering on Factors coming from non-linear Principal Component Analysis (PCA). The Kernel-PCA allowed to map data indirectly in a very-high-dimensional space F where is simple to construct a hyperplane that divides the points into arbitrary clusters. The choice of the kernel functions and its parameters provided different kernel factors which produced, in turn, alternative clusters solutions. All the partitions have been ranked using alternative criteria that measure aspects of clusters validity.

L’analisi dell’evoluzione della soddisfazione nel contesto di business, è di solito basato su studi pseudo-panel, perchè sono meno costosi e facili da costruire con i dati disponibili. Come in casi cross-section, informazioni dettagliate sui clienti vengono raccolte in ogni istante temporale, ma il confronto dinamico avviene generalmente considerando soli pochi ritardi temporali (a causa del breve ciclo di vita di prodotti e servizi). Di conseguenza, nel nostro contributo proponiamo l’utilizzo dell’analisi fattoriale multipla duale. Tale tecnica permette la sintesi delle tabelle multivariate e la visualizzazione delle stesse su uno spazio comune che fa luce su traiettorie di soddisfazione dei clienti. I vantaggi della tecnica vengono illustrati in un caso di studio relativo ad una banca italiana The analysis of the evolution of satisfaction in business context is usually based on pseudo panels studies, because they are less costly and easy to build with the available data. As in the cross-section case, detailed information about customers are collected at each time point, but the dynamic comparison generally involves few temporal lags (due to short life time of products and services). Accordingly, in our paper we apply the Dual Multiple Factor Analysis. Such a technique allows the synthesis of the multivariate tables and their visualization on a common space that sheds light on customers’ trajectories of satisfaction. A real case study of an Italian bank is illustrated

In this paper we explore the relationship between the Industrial Production Index (IPI), the confidence index for the manufacturing sector and its sub-indexes and Google searches for several words linked to the economic situation, for the period January 2004 - September 2016 on Italian data. Significant correlations between the selected indicators point to probable comovements of same. Adding one observation at a time since the first forewarning signs of the 2008 crisis, we find that a few Google searches and the IPI cointegrate, particularly during the strong downward trend leading to January 2009, while no confidence indicators cointegrate with the IPI. These findings suggest that concern about economic conditions expressed through searches in google and the IPI or the confidence indexes are influenced by common circumstances. Recursive forecasts of the IPI through VECM models suggest that the evolution of the IPI can be well mimicked using the real time Gtrends selected variables.

We study frictions in trading patterns in the Euro money market. We characterize the structure of lending relations during the period of recent financial turmoil. We use a network-topology method on data from overnight transactions in the Electronic Market for Interbank Deposits (e-MID) to investigate two main issues. First, we characterize the roles of borrowers and lenders in long-run relationships by providing evidence on network formation at a 3-month frequency. Second, we identify the “key players” in the marketplace and study their behavior. In our formalization, key players are “locally-central banks” within a network that lend (or borrow) large volumes to (from) several counterparties, while borrowing (or lending) small volumes from (to) a small number of institutions. Our results are twofold. We show that the aggregate trading patterns in e-MID are characterized by largely asymmetric relations. This implies a clear difference in the roles of lenders and borrowers, with market positions changing only gradually over time. We also find that the large net lenders exploit their positions as network leaders by imposing aggressive pricing policies on their counterparties.

Tail events, due to their nature, occur very rarely and are therefore often regarded as an element of surprise. However, business continuity management (BCM) as a part of enterprise risk management (ERM) should protect businesses from disruption in case of such events. In this chapter, we investigate whether businesses of different sizes were ready to withstand the consequences of the outbreak of the global SARS-CoV-2 pandemic in 2020. To this aim, we analyse the results of a global survey conducted during a major lockdown period (April–May 2020) by applying multiple correspondence analysis (MCA) and clustering. Our findings indicate that the financial sector was the best prepared for tail events, which may be attributed to regulations enforced for most financial institutions. Although around a half of businesses did not have a business continuity plan (BCP), having one helped adjust to the new conditions efficiently.

Kernel-based methods such as SVMs and LS-SVMs have been successfully used for solving various supervised classi cation and pattern recognition problems in machine learning. Unfortunately, they are heavily dependent on the choice of the optimal kernel function and from tuning parameters. Their solutions, in fact, su er of complete lack of interpretation in terms of input variables. That is not a banal problem, especially when the learning task is related with a critical asset of a business, like credit scoring, where deriving a classification rule has to respect an international regulation. The following strategy is proposed for solving problems using categorical predictors: replace the predictors by components issued from MCA, choice of the best kernel among several ones (linear ,RBF, Laplace, Cauchy, etc.), approximation of the classifi er through a linear model. The loss of performance due to such approximation is balanced by better interpretability for the end user, employed in order to understand and to rank the influence of each category of the variables set in the prediction. This strategy has been applied to real risk-credit data of small enterprises. Cauchy kernel was found the best and leads to a score much more e fficient than classical ones, even after approximation.

This paper addresses the issue of measuring tolerance, viewed as a multifaceted phenomenon involving several different social domains. We develop a multidimensional index for Likert-scale data, characterized by the following features: (i) it reflects the individual’s intensity of tolerant attitudes towards each social domain; (ii) the index can be broken down by dimension in order to determine the contribution of each dimension to overall tolerance; (iii) the index combines the different dimensions of tolerance using a weighted scheme that reflects the importance of each dimension in determining the overall level of tolerance. To show how this new measure of tolerance works in practice, we carry out a case study using an Italian recent survey asking the opinion of university students about different subjects, such as interreligious dialog, women/religion relationship, religion/death relationship, homosexuality, and multicultural society.

Small and medium enterprises (SMEs) contribution to the European Union economy has always been relevant, for both value added and the creation of jobs. On the same time, SMEs are more fragile and likely to default as compared to larger firms so to induce public policies targeted on them. Specific default predic- tion models, accounting for SMEs idyiosincratic traits, are based on several types of data, mainly accounting indicators. We explore the possibility of complementing accounting information with data scraped from the firms’ websites.

[EN] Small and Medium Enterprises (SMEs) contribution to the European Union economy has always been relevant, for both value added and the creation of jobs. That is why the prediction of their survival is considered one of the economic pillars UE keeps under observation. Default prediction models, accounting for SMEs idiosyncratic traits, are based on several types of data, mainly accounting indicators. Balance sheet data, indeed, are considered the standard predictors for classification models in this field, although they do not allow to completely overcome the information opacity that is one of the main barriers preventing these firms from accessing credit. In our work, we explore the possibility of complementing accounting information with data scraped from the firms’ websites. We modeled the data using a nonlinear discriminant analysis and we benchmarked the results with the Logistic Regression. The evidence of our study is promising although the combination of online and offline data shows better results in case of survival firms than for defaulted companies., This work was partially supported by grants PID2019-107765RB-I00 and funded by MCIN/AEI/10.13039/501100011033.

Differently from traditional transaction data, social media data are difficult to investigate due to their volume, variety and velocity. Indeed, the knowledge extraction from social media data raises several issues especially for what concerns statistical exploration and synthesis of complex information. Our work aims to study users' preferences, stated on a social media platform, in order to aid businesses to make their marketing communication decisions. We rely our analysis upon 5685 Italian Facebook users interested in pharmaceutical products and health. The data have been collected at the end of 2014 and are focused on Likes actively expressed by the subjects on specific categories of interests (TV Channels and Magazines). Through a factorial analysis we uncovered significant associations between marketing communication Media and users' profiles. This allows sketching out a marketing strategy in twofold actions: first, identifying the target group to reach and, then, the nearest suitable channel where to develop the marketing communication.

[EN] We propose the use of online indicators, scraped from the firms¿ websites, to predict default risk for a sample of Spanish firms via nonlinear discriminant analysis and the logistic regression model., This work was partially supported by the Ca' Foscari University of Venice, Italy and by Agencia Estatal de Investigacion, Spain under grant PID2019107765RBI00. We also acknowledge helpful comments by an anonymous referee.

[EN] The Small Business Act of the European Commission in 2008 acknowledge s the key role of Small and Medium Enterprises (SMEs) in the EU economy. Th is is particularly relevant for Italy, which has the largest share of SMEs in Europe, as well as for other countries such as Portugal, Spain and Greece. On the other hand, SMEs experience more difficulties in their early stages mainly due to high market competition and credit constraints, as highlighted by Fritsch and Weyh (2006). For these reasons, the study of SMEs default risk is always relevant. There are several papers studying firm default factors in a single country (see Ciampi, 2015, Fantazzini and Figini, 2009, Flix and dos Santos, 2018). The literature concentrates mainly on financial indicators built on businesses’ balance sheets, which are available about two years late wi th respect to their reference period. This diminishes the significance of the results, both for credit risk and policy aims, and particularly in a forecasting perspective. The purpose of this paper is to provide a preliminary study on a sample of spanish firms selected from the SABI, Sistema de Análisis de Balances Ibéricos, which is listed among Bureau van Dijk databases. The analysis will be carried out according to both parametric and non-parametric discrimination techniques, with the standard construction of a training set on which to build a model and a validation set to test the validity and robustness of the results, and, in the end, the reliability of the model in predicting default. Finally we present a new proposal: a scheme to understand to what ext ent firms’ default can be predicted by substituting the traditional data sour ces (offline information) with data collected from their corporate websites (onli ne information) in order to exploit more up-to-date information.

The objective of quantitative credit scoring is to develop accurate models of classification. Most attention has been devoted to deliver new classifiers based on variables commonly used in the economic literature. Several interdisciplinary studies have found that personality traits are related to financial behaviour; therefore, psychological traits could be used to lower credit risk in scoring models. In our paper, we considered financial histories and psychological traits of customers of an Italian bank. We compared the performance of kernel-based classifiers with those of standard ones. We found very promising results in terms of misclassification error reduction when personality attitudes are included in models, with both linear and non-linear discriminants. We also measured the contribution of each variable to risk prediction in order to assess importance of each predictor.

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.

The growth of Internet and the information technology has generated big changes in subjects' communication, which, nowadays, occurs through social media or via thematic forums. This challenges the traditional notion of Customer Relationship Management (CRM) and pushes businesses to prompt and accurate understanding of sentiments expressed, in order to address their marketing actions. In this paper, the authors propose a combined application of a supervised Sentiment Analysis (SA) with a probabilistic kernel discriminant to provide a robust classifier of texts polarization. The partition obtained is also described by means of a statistical characterization of the texts. Such an approach is very promising, not only in terms of classification accuracy, but also in terms of knowledge extraction. A real case study is illustrated in order to test and show the effectiveness of the proposed strategy.

Due to the recent financial turmoil, a discussion in the banking sector about how to accomplish long term success, and how to follow an exhaustive and powerful strategy in credit scoring is being raised up. Recently, the significant theoretical advances in machine learning algorithms have pushed the application of kernel-based classifiers, producing very effective results. Unfortunately, such tools have an inability to provide an explanation, or comprehensible justification, for the solutions they supply. In this paper, we propose a new strategy to model credit scoring data, which exploits, indirectly, the classification power of the kernel machines into an operative field. A reconstruction process of the kernel classifier is performed via linear regression, if all predictors are numerical, or via a general linear model, if some or all predictors are categorical. The loss of performance, due to such approximation, is balanced by better interpretability for the end user, which is able to order, understand and to rank the influence of each category of the variables set in the prediction. An Italian bank case study has been illustrated and discussed; empirical results reveal a promising performance of the introduced strategy.

The objective of quantitative credit scoring is to develop accurate models that can distinguish between good and bad applicants (Baesens et al, 2003). Statistical research has focused on delivering new classification methodologies based on neural networks or support vector machines that provide better predictions respect to standard classifiers but poor interpretation. A step forward in such task, it is represented by linear reconstruction of kernel discriminant proposed by Liberati et al (2015) which combines the effective classification results, due to application of no-linear functions, with an easy interpretability of the data. In reality, insolvency, is not always a negative occurrence but the opportunity to generate more profit for a financial institution. Indeed, it can be considered a marketing leverage to reinforce customers’ loyalty. In this paper, we approach credit risk modeling into this perspective. We compare the classification solution that focuses on prediction performance with an explanatory regression as Tobit that models both default probability and duration. Results will be illustrated in a double perspective (risk management and customers' segmentation), in order to identify which covariates affect the insolvent behaviour the most.

[EN] The aim of this paper is to propose a method to explore and synthesize social media data in order to aid businesses to make their communication decisions. The research was conducted at the end of 2014 on 5607 Italian Facebook subjects interested in drugs and health. In this study, we refer to the pharmaceutical market that is characterized by strict legal constraints, which prevent any promotional activities (such as advertising) of companies on prescription drugs. Thus, pharmaceutical businesses tend to promote their corporate brand instead of a single product brand. In such context, social media offer the opportunity to gather customers’ information about their attitudes and preferences, helpful to address marketing activities. Through a multivariate statistical approach on Facebook data, we have highlighted the associations existing between TV channels and users’ profiles. Therefore, depending on the value proposition to promote, every business could choose, first, the target group to reach and, then, the nearest suitable channel where to develop the corporate brand communication.

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.

In healthcare literature, several papers have demonstrated that providing patients with detailed, continuous, and diversified information on their pathology - in a broad sense - has a significantly positive effect on patient empowerment and on care continuum effectiveness. In this respect, besides healthcare professionals, indirect communication (printed material, website, etc.) plays an increasingly important role. Web communication, in particular, has stated to be crucial: it is expected to be accessible to everyone and cost-saving for healthcare systems, to facilitate two-way interaction, and to promote patient-doctor-advocacy groups collaboration. Web communication, however, seems to suffer from two main limitations: 1. it can be unreliable if unsupervised; 2. it can be used to diffuse only some kinds of information. This work aims to address the extent of these limitations, examining patient propensity toward using web communication and identifying the patient needs that it could contribute to satisfy. For this purpose, we run an exploratory empirical study on a sample of rare cancer patients treated in a highly specialized research center in Northern Italy. We conduct semi-structured interviews on patient propensity and needs and analyze their transcriptions by means of text mining techniques. We clean and normalize the texts with automatic techniques for lexical- grammatical analysis and, finally, we synthesize the text contents with correspondence analysis (shortly, CA). CA allows us to identify the main concepts expressed by patients and to represent geometrically, in a reduced factorial space, the patients and the concepts. This simultaneous graphic representation of concepts and individuals allows visual representation of the association between patients and their information needs.

Social Network Analysis has been widely applied to detect individual drivers of counterparty selection within a number of inter-individual and inter-organizational settings. Longitudinal methods, like Stochastic Actor-Oriented Models, Separable Temporal Exponential Random Graph Models and Relational Event History Models have been proposed to identify the evolution of actors’ positioning within a network of relationships. They provide a detailed description of the general tendencies shaping the network dynamics, but do not allow to isolate groups of similar behaviors. Standard cluster analysis has been used to accomplish this purpose, but with a main focus on cross-sectional data. Our proposal is to combine Social Network Analysis with a Multi-way Factor Analysis (MFA). Such an approach allows to represent the evolutions of the actors’ behaviors according to the time structure of the data; it also facilitates the visual inspection of the actors' trajectories onto the compromise plan. A clustering of actors based on Multi-way factors is also applied, in order to identify similar patterns and to provide an interpretable solution. In the first phase, ego-network measures are computed to identify actors’ market behaviors in their neighborhood. Then, according to Structuration des Tableaux A Trois Indices de la Statistique (STATIS) approach, a Three-way Factor Analysis is performed, in order to represent actors configurations according to their average distribution. Such solution can be reviewed as the space spanned by a linear combination of multiple factor analysis and it can be consider a “virtual” space where similar actors route paths can be highlighted. An application to the Euro Electronic Market for Interbank Deposits (e-Mid) during the recent turmoil period will be shown, in order to provide insights into the rationale and benefits of the proposed approach.

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.

As management of a national bank wanted to analyze its retail service competition loss probably due to low customer satisfaction, we carried out an empirical study based on a sample of 27,000 retail customers. The survey aimed to analyze retail service weaknesses and to individuate possible recovery actions measuring their effectiveness across different waves (three time lags). We studied a definition of a new dissimilarity measure exploiting a dimension reduction obtained by a three-way factor analysis (TFA). We had previously focused our attention on the limits of this approach related to the geometrical properties of the TFA applied. We introduced a reassessment of the points to adjust the three-way solution according to the quality of representation of the points. This transformation only rescaled the factor scores producing a local adjustment of the point configuration. We then performed a trajectory analysis of the different waves. The results showed the effectiveness of our approach. Therefore, further study of the derivation of a synthetic measure of cluster routes seems appropriate.

The global imbalance (GI) measure is a way for checking balance of baseline covariates that confound eorts to draw valid conclusions about treatment eects on outcomes of interest. In addition, GI is tested by means of a multivariate test. The GI measure and its test overcome some limitations of the common way for assessing the presence of imbalance in observed covariates that were discussed in D'Attoma and Camillo (2011). A user written SAS macro called %GI, to simultaneously measure and test global imbalance of baseline covariates is described. Furthermore, %GI also assesses global imbalance by subgroups obtained through several matching or classication methods (e.g., cluster analysis, propensity score subclassication, Rosenbaum and Rubin 1984), no matter how many groups are examined. %GI works with mixed categorical, ordinal and continuous covariates. Continuous baseline covariates need to be split into categories. It also works in the multi-treatment case. The use of the %GI macro will be illustrated using two articial examples.

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 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 an empirical study: the management of a national bank with a spread network throughout Italy wanted to analyze its reduced competitiveness in retail services, probably due to low customer satisfaction. The survey aims to analyze weaknesses in its retail services, propose possible recovery actions and measure their effectiveness across different “waves” (time lags).