LTV model in consultant sector. Case study: mental health clinic

Since the early 1980s, customer relationship management (CRM) has been important in the new competitive business environment. Today, due to development of competitive factors in the business, the enterprise's need to create and retain effective relations with customers has been highlighted more and more. With the aim of customer scoring applications, the most profitable customers can be identified. In this paper, we categorized customers by three types of values for the clinic by using logistic regression as a data-mining technique, and calculated the customer defection and future purchase probability in a mental health clinic of the university of Tehran. Model verification and validation (using lift chart) was done and customer segmentation and analysis presented with proper marketing strategies.     

A recommender system to avoid customer churn: A case study

A major concern for modern enterprises is to promote customer value, loyalty and contribution through services such as can help establish a long-term, honest relationship with customers. For purposes of better customer relationship management, data mining technology is commonly used to analyze large quantities of data about customer bargains, purchase preferences, customer churn, etc. This paper aims to propose a recommender system for wireless network companies to understand and avoid customer churn. To ensure the accuracy of the analysis, we use the decision tree algorithm to analyze data of over 60,000 transactions and of more than 4000 members, over a period of three months. The data of the first nine weeks is used as the training data, and that of the last month as the testing data. The results of the experiment are found to be very useful for making strategy recommendations to avoid customer churn.     

Mining class outliers: concepts, algorithms and applications in CRM

Outliers, or commonly referred to as exceptional cases, exist in many real-world databases. Detection of such outliers is important for many applications and has attracted much attention from the data mining research community recently. However, most existing methods are designed for mining outliers from a single dataset without considering the class labels of data objects. In this paper, we consider the class outlier detection problem ‘given a set of observations with class labels, find those that arouse suspicions, taking into account the class labels’. By generalizing two pioneer contributions [Proc WAIM02 (2002); Proc SSTD03] in this field, we develop the notion of class outlier and propose practical solutions by extending existing outlier detection algorithms to this case. Furthermore, its potential applications in CRM (customer relationship management) are also discussed. Finally, the experiments in real datasets show that our method can find interesting outliers and is of practical use.     

Rule effectiveness in rule-based systems: A credit scoring case study

Rule-based systems may sometimes grow very large, making their acceptance by users and their maintenance quite problematic. One therefore needs to make rule-bases as compact as possible. The classical definition of rule redundancy in the literature is based upon logic and graph theory. Another, complementary, view of redundancy is proposed here. The suggested approach is based on the contribution of individual rules to the overall system’s accuracy.

It is shown here, though an analysis of a real-world credit scoring rule-based system, that by taking into account system’s accuracy, one can sometimes significantly reduce the size of a rule-base; even one which is already free from logic-related abnormalities. The approach taken here is not proposed as a substitution to classical logic and graph-based methods. Rather, it complements them.     

Simulation in public sector management: a case study

We detail the development and application of a simulation model to aid decision making concerning the procedures followed in the Office of Film and Literature Classification, by forecasting the effects of possible management initiatives. The major variables are — the number of censors, the volume of publications to be classified (with special emphasis on computer technology publications) and the procedures, in particular, the number of censors with decision-making powers. A model of the system was developed using Extend; a simulation software package designed to aid decision support. This model was used to investigate the utilization of "decision-makers", and to identify and locate the bottlenecks, in the system under a number of different scenarios suggested by the client. There is also the flexibility to include additional duties that might be imposed on the Office.     

Predictive-collaborative model as recovery and validation tool. Case of study: Psychiatric emergency department decision support

There is a frequent situation in data mining where data collected must be used in real time to support decisions and they could present missing or non consistent values. The objective of this proposal consists of the recovery of missing values and verifies the consistency and integrity of the provided, in order to increase the information to support decisions. To address this, a predictive-collaborative model has been designed. It is composed of different predictive models generated by means of a training set and classifier selection algorithm. The combined suggestions of these predictive models are offered to support decisions. As case of study, the psychiatric emergency department at the Doce de Octubre Hospital in Madrid has been considered, where the response time is critical and the data are acquired in a stress situation which affects the quality of data significantly.     

A fuzzy DEA model with undesirable fuzzy outputs and its application to the banking sector in India

Data envelopment analysis (DEA) is a widely used technique for measuring the relative efficiencies of decision making units (DMUs) with multiple inputs and multiple outputs. However, in real life applications, undesirable outputs may be present in the production process which needs to be minimized. The present study endeavors to propose a DEA model with undesirable outputs and further to extend it in fuzzy environment in view of the fact that input/output data are not always available in exact form in real life problems. We propose a fuzzy DEA model with undesirable fuzzy outputs which can be solved as crisp linear program for each α in (0, 1] using α-cut approach. Further, cross-efficiency technique is applied to increase the discrimination power of the proposed models and to rank the efficient DMUs at every α in (0, 1]. Moreover, for better understanding of the proposed methodology, we present a numerical illustration followed by an application to the banking sector in India. This is the first study which attempts to measure the performance of public sector banks (PuSBs) in India using fuzzy input/output data for the period 2009–2011. The results obtained from the proposed methodology not only depict the impact of undesirable output on the performance of PuSBs but also analyze efficiently the influence of the presence of uncertainty in the data over the efficiency results. The findings show that the efficiency results of many PuSBs vary with the variation in α during the selected period.     

Assessing safety culture in public sector organisations: A cross-national study

An appropriate safety culture helps in enhancing safety performance in organisations. This study aims to investigate safety culture prevalence, assess individual sociodemographic parameters and accident experience effects on this culture and explore ways to enhance this culture in public sector organisations. A specially designed questionnaire was randomly distributed to 805 public sector employees in Dubai and Kuwait. Respondents were asked to rate their agreement with 24 statements representing seven safety culture dimensions. Student t-test and non-parametric tests were used to analyse the responses. Results revealed that employees in both governments reported experiencing a reasonably strong safety culture in their workplaces with safety attitude and teamwork receiving the highest while safety rules and workload receiving the lowest ranks among the seven safety culture dimensions. Moreover, male employees reported experiencing more accidents and scoring higher on most safety culture dimensions than female employees. Finally, employees who experienced accidents in the last five years reported a higher safety culture score than others. Accordingly, recommendations are put forward to enhance safety culture in public sector organisations.     

A scoring model to detect abusive billing patterns in health insurance claims

We propose a scoring model that detects outpatient clinics with abusive utilization patterns based on profiling information extracted from electronic insurance claims. The model consists of (1) scoring to quantify the degree of abusiveness and (2) segmentation to categorize the problematic providers with similar utilization patterns. We performed the modeling for 3705 Korean internal medicine clinics. We applied data from practitioner claims submitted to the National Health Insurance Corporation for outpatient care during the 3rd quarter of 2007 and used 4th quarter data to validate the model. We considered the Health Insurance Review and Assessment Services decisions on interventions to be accurate for model validation. We compared the conditional probability distributions of the composite degree of anomaly (CDA) score formulated for intervention and non-intervention groups. To assess the validity of the model, we examined confusion matrices by intervention history and group as defined by the CDA score. The CDA aggregated 38 indicators of abusiveness for individual clinics, which were grouped based on the CDAs, and we used the decision tree to further segment them into homogeneous clusters based on their utilization patterns. The validation indicated that the proposed model was largely consistent with the manual detection techniques currently used to identify potential abusers. The proposed model, which can be used to automate abuse detection, is flexible and easy to update. It may present an opportunity to fight escalating healthcare costs in the era of increasing availability of electronic healthcare information.     

The significance of attitudes towards evidence-based practice in information technology use in the health sector: an empirical investigation

The article investigates the relationship between attitudes towards evidence-based practice (EBP) and the use of information and communication technology (ICT) in practice and demonstrates that the poor correlation reported in the literature is a methodological artifact rather than a substantive fact. Results are based on structured surveying of 1015 medical and nursing staff, drawn from 15 Greek hospitals. We used unfolding item response theory models to demonstrate that by placing the statements assessing attitude towards EBP and ICT self-reported use on a single attitude–behaviour continuum, behaviour statements have a systematically different location on the attitude–behaviour continuum from the attitude statements. Based on the latent probabilistic relation among attitudes towards EBP and ICT use, the practical implications of the study are discussed.     

Spreading good ideas: a case study of the adoption of an innovation in the construction sector

A health and safety association collaborated with two research centres to examine the dissemination of knowledge of an ergonomic intervention by opinion leaders in the construction sector. The intervention was a hydraulic ladder lift that aided with loading and unloading of ladders off van roofs. Thirteen companies, with five to 900 employees, were involved. The van operators informed workmates not employed by their companies but who worked on the same site as them about the intervention. The opinion leaders informed decision makers within their companies which led to commitments to purchase similar units. They also gave presentations at prearranged health and safety meetings, where attendees indicated that they thought the intervention sounded like a good idea. In this way, knowledge of the innovation reached at least 32 more companies and potentially several thousand other employees. The study showed the potential for workplace change to be exponential.     

Data mining in course management systems: Moodle case study and tutorial

Educational data mining is an emerging discipline, concerned with developing methods for exploring the unique types of data that come from the educational context. This work is a survey of the specific application of data mining in learning management systems and a case study tutorial with the Moodle system. Our objective is to introduce it both theoretically and practically to all users interested in this new research area, and in particular to online instructors and e-learning administrators. We describe the full process for mining e-learning data step by step as well as how to apply the main data mining techniques used, such as statistics, visualization, classification, clustering and association rule mining of Moodle data. We have used free data mining tools so that any user can immediately begin to apply data mining without having to purchase a commercial tool or program a specific personalized tool.     

Re-mining item associations: Methodology and a case study in apparel retailing

Association mining is the conventional data mining technique for analyzing market basket data and it reveals the positive and negative associations between items. While being an integral part of transaction data, pricing and time information have not been integrated into market basket analysis in earlier studies. This paper proposes a new approach to mine price, time and domain related attributes through re-mining of association mining results. The underlying factors behind positive and negative relationships can be characterized and described through this second data mining stage. The applicability of the methodology is demonstrated through the analysis of data coming from a large apparel retail chain, and its algorithmic complexity is analyzed in comparison to the existing techniques.     

Deep learning for pedestrian collective behavior analysis in smart cities: A model of group trajectory outlier detection

This paper introduces a new model to identify collective abnormal human behaviors from large pedestrian data in smart cities. To accurately solve the problem, several algorithms have been proposed in this paper. These can be split into two categories. First, algorithms based on data mining and knowledge discovery, which study the different correlation among human behavioral data, and identify the collective abnormal human behavior from knowledge extracted. Secondly, algorithms exploring convolution deep neural networks, which learn different features of historical data to determine the collective abnormal human behaviors. Experiments on an actual human behaviors database have been carried out to demonstrate the usefulness of the proposed algorithms. The results show that the deep learning solution outperforms both data mining as well as the state-of-the-art solutions in terms of runtime and accuracy performance. In particular, for large datasets, the accuracy of the deep learning solution reaches 88%, however other solutions do not exceed 81%. Additionally, the runtime of the deep learning solution is below 50 seconds, whereas other solutions need more than 80 seconds for analyzing the same database.     

Towards healthcare business intelligence in long-term care: An explorative case study in the Netherlands

This research contributes to the domain of long-term care by exploring knowledge discovery techniques based on a large dataset and guided by representative information needs to better manage both quality of care and financial spendings, as a next step towards more mature healthcare business intelligence in long-term care. We structure this exploratory research according to the steps of the CRoss Industry Standard Process for Data Mining (CRISP-DM) process. Firstly, we interview 22 experts to determine the information needs in long-term care which we, secondly, translate into 25 data mining goals. Thirdly, we perform a single case study at a Dutch long-term care institution with around 850 clients in five locations. We analyze the institution’s database which contains information from April 2008 to April 2012 to identify patterns in incident information, patterns in risk assessment information, the relationship between risk assessments and incident information, patterns in the average duration of stay, and we identify and predict Care Intensity Package (ZZP) combinations. Fourth and finally, we position all data mining goals in a two-by-two matrix to visualize the relative importance of each goal in relation to both quality of care and financial state of care institutions.     

Discrimination discovery in scientific project evaluation: A case study

Discovering contexts of unfair decisions in a dataset of historical decision records is a non-trivial problem. It requires the design of ad hoc methods and techniques of analysis, which have to comply with existing laws and with legal argumentations. While some data mining techniques have been adapted to the purpose, the state-of-the-art of research still needs both methodological refinements, the consolidation of a Knowledge Discovery in Databases (KDD) process, and, most of all, experimentation with real data. This paper contributes by presenting a case study on gender discrimination in a dataset of scientific research proposals, and by distilling from the case study a general discrimination discovery process. Gender bias in scientific research is a challenging problem, that has been tackled in the social sciences literature by means of statistical regression. However, this approach is limited to test an hypothesis of discrimination over the whole dataset under analysis. Our methodology couples data mining, for unveiling previously unknown contexts of possible discrimination, with statistical regression, for testing the significance of such contexts, thus obtaining the best of the two worlds.     

Social media and mental health in democracy movement in Hong Kong: A population-based study

Social media use has proliferated in the past ten years and studies are beginning to investigate the associations of social media use with political movements and mental health. This study extends this literature by testing a novel hypothesis that social resource loss on social media (e.g., “unfriending”) may be associated with increased symptoms of depression and anxiety in social upheaval. A population-based sample of 1,208 Chinese Hong Kong citizens (mean age = 46.89; 52.4% female) was recruited by random digit dialing in February 2015, two months after the conclusion of the Umbrella Movement in Hong Kong. Respondents reported social resource loss on social media, and anxiety and depressive symptoms. Hierarchical regression analyses revealed that social resource loss on social media was positively associated with depressive symptoms but not anxiety symptoms. Age moderated the positive association between social resource loss on social media and depressive symptoms. Simple slope tests revealed that the association was significant only among middle-aged (39–55 years) and older (≥56 years) adults but not younger (18–38 years) adults. The current findings shed light on the role of social media in mental health during political movements across different age groups.     

Data mining and knowledge discovery in materials science and engineering: A polymer nanocomposites case study

In this study, data mining and knowledge discovery techniques were employed to validate their efficacy in acquiring information about the viscoelastic properties of vapor-grown carbon nanofiber (VGCNF)/vinyl ester (VE) nanocomposites solely from data derived from a designed experimental study. Formulation and processing factors (VGCNF type, use of a dispersing agent, mixing method, and VGCNF weight fraction) and testing temperature were utilized as inputs and the storage modulus, loss modulus, and tan delta were selected as outputs. The data mining and knowledge discovery algorithms and techniques included self-organizing maps (SOMs) and clustering techniques. SOMs demonstrated that temperature had the most significant effect on the output responses followed by VGCNF weight fraction. SOMs also showed how to prepare different VGCNF/VE nanocomposites with the same storage and loss modulus responses. A clustering technique, i.e., fuzzy C-means algorithm, was also applied to discover certain patterns in nanocomposite behavior after using principal component analysis as a dimensionality reduction technique. Particularly, these techniques were able to separate the nanocomposite specimens into different clusters based on temperature and tan delta features as well as to place the neat VE specimens (i.e., specimens containing no VGCNFs) in separate clusters. Most importantly, the results from data mining are consistent with previous response surface characterizations of this nanocomposite system. This work highlights the significance and utility of data mining and knowledge discovery techniques in the context of materials informatics.     

The tweets that killed a university: A case study investigating the use of traditional and social media in the closure of a state university

The 12-month discussion surrounding a regional university campus quickly evolved from a suggestion of independence, to a plan, to the ultimate closure of the university. This unique series of events at the University of South Florida Polytechnic (USFP) allows for an investigation of how various forms of media were used during this significant event that impacted college student’s education and immediate future. A campus wide survey was combined with social and online media monitoring to assess the topics, authors, and methods used during prominent discussions during and preceding the closure of USFP. Although social media played a crucial role, the most common format was Twitter and it was used almost exclusively by members of the media itself. Students instead relied on traditional sources to gather information. Additionally, students expressed their opinion utilizing classic methods, such as petitions, foregoing more modern Twitter or Facebook campaigns. It is incorrect to automatically assume younger demographic authorship or utilization of social media technology. Whereas social media use could expand even more over the next decade, identifying authorship remains critical as it is unclear how frequent social media is viewed as an official method of public discussion, especially when politics and higher education collide.     

The role of communication practices in the strengthening of counter networks: Case experiences from the health care sector of Mozambique

In this article we provide a theoretically informed empirical analysis of the introduction and use of information and communication technology (ICT) within the primary health care (PHC) sector of Mozambique. The theoretical lens for this analysis is developed from Manuel Castells' (1996, 1997, 2001) ideas on the network society and counter domination. These ideas help us to conceptualize the communicative action required to strengthen the PHC sector as a “counter network,” which has the normative aim to strengthen the health information system (HIS) as a key strategy to improve health care delivery. Taking an informational perspective, the role of communication is highlighted as playing an important constitutive basis in the strengthening of this network. These conceptual ideas are applied to the empirical analysis of an ongoing project (the Health Information Systems Programme or HISP), and to analyze some key constraints and strategies for strengthening these networks. This study makes key contributions to both the theoretical and practical domains of HIS in developing countries. © 2005 Wiley Periodicals, Inc.