In interactive data analysis processes, the dialogue between the human and the computer is the enabling mechanism that can lead to actionable observations about the phenomena being investigated. It is of paramount importance that this dialogue is not interrupted by slow computational mechanisms that do not consider any known temporal human-computer interaction characteristics that prioritize the perceptual and cognitive capabilities of the users. In cases where the analysis involves an integrated computational method, for instance to reduce the dimensionality of the data or to perform clustering, such non-optimal processes are often likely. To remedy this, progressive computations, where results are iteratively improved, are getting increasing interest in visual analytics. In this paper, we present techniques and design considerations to incorporate progressive methods within interactive analysis processes that involve high-dimensional data. We define methodologies to facilitate processes that adhere to the perceptual characteristics of users and describe how online algorithms can be incorporated within these. A set of design recommendations and according methods to support analysts in accomplishing high-dimensional data analysis tasks are then presented. Our arguments and decisions here are informed by observations gathered over a series of analysis sessions with analysts from finance. We document observations and recommendations from this study and present evidence on how our approach contribute to the efficiency and productivity of interactive visual analysis sessions involving high-dimensional data.
Customer retention is crucial in a variety of businesses as acquiring new customers is often more costly than keeping the current ones. As a consequence, churn prediction has attracted great attention from both the business and academic worlds. Traditional efforts in the financial domain mainly focus on domain specific variables such as product ownership or service usage aggregation, however, without considering dynamic behavioral patterns of customers' financial transactions. In this paper, we attempt to fill in this gap by investigating the spatio-temporal patterns and entropy of choices underlying the customers' financial decisions, and their relations to customer churning activities. Inspired by previous works in the emerging field of computational social science, we built a prediction model based on spatio-temporal and choice behavioral traits using individual transaction records. Our results show that proposed dynamic behavioral models could predict churn decisions significantly better than traditionally considered factors such as demographic-based features, and that this effect remains consistent across multiple data sets and various churn definitions. We further study the relative importance of the various behavioral features in churn prediction, and how the predictive power varies across different demographic groups. More generally, the proposed features can also be applied to churn prediction in other domains where spatio-temporal behavioral data are available.
Experiences from various industries show that companies may have problems collecting customer invoice payments. Studies report that almost half of the small-and medium-sized enterprise and business-to-business invoices in the United States and United Kingdom are paid late. In this study, our aim is to understand customer behavior regarding invoice payments, and propose an analytical approach to learning and predicting payment behavior. Our logic can then be embedded into a decision support system where decision makers can make predictions regarding future payments, and take actions as necessary toward the collection of potentially unpaid debt, or adjust their financial plans based on the expected invoice-to-cash amount. In our analysis, we utilize a large data set with more than 1.6 million customers and their invoice and payment history, as well as various actions (e.g., e-mail, short message service, phone call) performed by the invoice-issuing company toward customers to encourage payment. We use supervised and unsupervised learning techniques to help predict whether a customer will pay the invoice or outstanding balance by the next due date based on the actions generated by the company and the customer's response. We propose a novel behavioral scoring model used as an input variable to our predictive models. Among the three machine learning approaches tested, we report the results of logistic regression that provides up to 97% accuracy with or without preclustering of customers. Such a model has a high potential to help decision makers in generating actions that contribute to the financial stability of the company in terms of cash flow management and avoiding unnecessary corporate lines of credit.
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