Identification of advanced data analysis in marketing: A systematic literature review

Aim/purpose – Marketing is an important area of activity for the vast majority of enterprises. Many of them try using marketing data analysis. Both the literature and the practice of many enterprises describe the use of advanced data analysis. However, interpretations of this concept differ. The aim of this paper is to identify the interpretation of advanced data analysis in marketing, in support of decision-making processes applied in the retail trading sector. Design/methodology/approach – The study was conducted using a systematic literature review, suggested by B. Kitchenham (2004), extended by C. Wohlin & R. Prikladniki (2013). This method was modified and expanded through the division of the whole study into two phases. Each phase is intended to facilitate obtaining answers to different important research questions. The first phase constitutes an exploratory study, whose results allow the detailed analysis of the literature in the second phase of the study. Findings – The results of this study of the relevant literature indicate that scholarly pub-lications do not use the phrase ‘advanced data analysis’, and its context is described with the term ‘data analysis’. Another term used broadly within the sphere of data analysis is ‘big data’. The concept of ‘data analysis’ in marketing is focused around the term ‘big data analytics’ and terms linked to the word ‘customer’, such as ‘customer-centric’, ‘customer engagement’, ‘customer experience’, ‘customer targeting service’, and ‘customers classification’. The study of the literature undertaken indicates that marketing employs data analysis in such areas as customer needs identification and market segmentation. Research implications/limitations – The study of the literature review was carried out using selected four databases containing publications, i.e. Web of Science, IEEE, Springer and ACM for the period 2008 to 2018. The research described in the article can be continued in two ways. First, by analysing the literature presented in this paper on advanced data analysis in marketing using the method called snowball sampling. Secondly, the results obtained from the first stage of the study can be used to conduct the study with other databases. Originality/value/contribution – The main contribution of this work is the proposal of modifying the systematic literature review method, which was expanded through the introduction of two phases. This division of two stages is important for conducting studies of literature when there are no clear, established definitions for the concepts being employed. The result of the study is also a set of ordered terms and their meanings that clearly define advanced data analysis in marketing

Associations between depressive symptoms and disease progression in older patients with chronic kidney disease: results of the EQUAL study

Background Depressive symptoms are associated with adverse clinical outcomes in patients with end-stage kidney disease; however, few small studies have examined this association in patients with earlier phases of chronic kidney disease (CKD). We studied associations between baseline depressive symptoms and clinical outcomes in older patients with advanced CKD and examined whether these associations differed depending on sex. Methods CKD patients (>= 65 years; estimated glomerular filtration rate <= 20 mL/min/1.73 m(2)) were included from a European multicentre prospective cohort between 2012 and 2019. Depressive symptoms were measured by the five-item Mental Health Inventory (cut-off <= 70; 0-100 scale). Cox proportional hazard analysis was used to study associations between depressive symptoms and time to dialysis initiation, all-cause mortality and these outcomes combined. A joint model was used to study the association between depressive symptoms and kidney function over time. Analyses were adjusted for potential baseline confounders. Results Overall kidney function decline in 1326 patients was -0.12 mL/min/1.73 m(2)/month. A total of 515 patients showed depressive symptoms. No significant association was found between depressive symptoms and kidney function over time (P = 0.08). Unlike women, men with depressive symptoms had an increased mortality rate compared with those without symptoms [adjusted hazard ratio 1.41 (95% confidence interval 1.03-1.93)]. Depressive symptoms were not significantly associated with a higher hazard of dialysis initiation, or with the combined outcome (i.e. dialysis initiation and all-cause mortality). Conclusions There was no significant association between depressive symptoms at baseline and decline in kidney function over time in older patients with advanced CKD. Depressive symptoms at baseline were associated with a higher mortality rate in men

Stages and areas of the use of IT tools supporting the management of IT projects

Conducting informatic projects requires the application of modern solutions that streamline workflow. Current projects are becoming more complicated, during their implementation, large amounts of data and information are generated. In addition, in many external sources we can find data and information of the various forms and levels of detail. To get from this data a valuable knowledge, it is necessary to use new data analysis techniques and new technological solutions. The article sets out to present a dissertation of considerations for IT projects and the steps of IT project were highlighted with areas and IT tools that support their implementation. The aim of this article is to assess the current level of use of IT tools by the companies considered as leaders, at various stages of IT projects implementation. The authors also rated the intensity of IT tools usage in various areas of project execution. The paper includes results of research conducted, and can serve as a guideline for enterprises developing IT tools intended to streamline informatic projects. The contents of the dissertation are based on literature studies and empirical research

Wykorzystanie narzędzi klasy BI i systemów Big Data do zarządzania projektami

The success of projects in enterprises and the success of an entire organization’s business largely depend on the possession and efficient use of the relevant information. In a broader context, success depends on having the adequate knowledge at the right time and place. Business processes generate large amount of data that are collected and processed in a way enabling transforming data into a measurable and useful value, which is information. Its efficient usage streamlines business processes, and allows to respond quickly to changes and proper decision-making. The aim of the paper is to present and define the project management challenges and ideas of Business Intelligence and Big Data systems. The types of analysis available in both platforms are also discussed. In the paper, the authors try to identify the areas of project management that can benefit from Business Intelligence and Big Data analysis

A methodological approach to analysis and exploration of marketing data

W artykule zaproponowano metodykę realizacji projektu systemu wspomagania decyzji marketingowych z wykorzystaniem metod eksploracji danych i technologii Big Data. Inspiracją podejścia była metodyka eksploracji danych CRISP-DM, która oryginalnie nie była zorientowana na projekty Big Data. Z tego powodu metodykę tę zmodyfikowano pod kątem celu i wymagań funkcjonalnych oraz technologicznych projektowanego przez nas systemu. Główne prace badawcze w projekcie koncentrowały się na analizie i eksploracji dużych, heterogenicznych zbiorów danych o dużej zmienności. W artykule szczegółowo opisano etapy procesu realizacji projektu według rozszerzonej metodyki CRISP-DM, z uwzględnieniem specyfiki procesów analizy i eksploracji dużych baz danych marketingowych przetwarzanych w czasie rzeczywistym. W celu ilustracji podejścia podano też przykłady zadań w trakcie realizacji etapów projektu na konkretnych danych o klientach, transakcjach i produktach sklepu internetowego.The article proposes a methodology for development of a marketing Decision Support System using data mining methods and Big Data technologies. The main research findings focus on the analysis and exploration of very large, heterogeneous sets of highly volatile marketing data. The approach is inspired by the CRISP-DM methodology which is not oriented towards Big Data applications. The article describes in detail the stages of the project development according to the extended CRISP-DM methodology, taking into account the specificity of the analysis and exploration processes of large marketing databases processed in real time. In order to illustrate the approach, the examples based on real data about customers, transactions and products of the Internet store were discussed

Dynamic Modelling of Inter-Organizational Networks Using the Domain Knowledge and Big Data Analytics

Inter-organizational networks are becoming deeply rooted in the organization management’s practice and theory. Still, there is an evident lack of a data-driven, adaptive tool aiding managerial decision-making processes in the network context. The inter-disciplinary team of authors showed that modern approach including big data analytics and data science has a great potential to support this particularly sophisticated task. The article presented a novel approach of combining a domain model with big data analytics and machine learning and graph algorithms to forecast network events. Then, the model was verified against a selection of known and current managerial tasks in the inter-organizational context. The resulting concept of a decision support system presented an implementation of a human-machine environment in which the machine solved tasks of pattern recognition and the human (i.e. domain expert) interpreted the results on different levels of abstraction using the domain knowledge

Usability of Knowledge Portals for Exclusives in Local Governments

Part 2: Models and Functioning of Knowledge ManagementInternational audienceExclusion phenomenon in common understanding denotes processes in which members of society or groups of people are permanently blocked from resources (mostly considered as social exclusion). No doubts, such sort of phenomena is observed as unwanted not only from “outsiders” but also from local and global society points of view. The exclusion (and its antonym inclusion) phenomenon can be investigated including many perspectives: starting from identifying exclusion as a process, through multidimensional aspects up to solutions available in the domain.In order to be successful in overtaking this phenomenon groups and institutions involved in this process should be supported by ICT solutions. The paper consists of six parts which gradually present context of the problem and proposed solutions. After short introduction concerning research background the discussed concept of exclusion processes and knowledge portals are presented. In the main section a general idea of knowledge portal for exclusives is proposed and specialty of these portal for regional implementation in the Silesia agglomeration is discussed. It creates opportunities for formulation final conclusions about the necessity and usability of the developed platform

Patterns of Locus of Control in People Suffering from Heart Failure: An Approach by Clustering Method

Background and Objectives: The assumption of responsibility in dealing with chronic diseases is of relevance in a resource-oriented and not only deficit-oriented medicine, especially in dealing with chronic diseases, including patients with chronic heart failure. The aim of the present study is to examine, based on the model of “locus of control”, whether there are different patterns that would be relevant for a more targeted education and support of self-management in dealing with heart failure. Materials and Methods: For this purpose, a sample (n = 758) from 11 Polish cardiology centers have been assessed using the standardized self-assessment scale Multidimensional Health Locus of Control (MHLC), consisting of three dimensions: (i) internal localization of health control; (ii) external control by powerful others; (iii) external control by chance. Results: Using these three criteria, nine different clusters were extracted (mean size: 84 ± 33 patients, min 31, max 129). Three clusters included over 100 patients, whereas only two included less than 50 people. Only one cluster gathered 42 patients who will be able to cooperate with professionals in the most fruitful way. There were two clusters, including patients with beliefs related to the risk of ignoring professional recommendations. Clusters where patients declared beliefs about others’ control with low internal control should also be provided with specific help. Conclusions: The division into clusters revealed significant variability of belief structures about health locus of control within the analyzed group. The presented methodological approach may help adjust education and motivation to a selected constellation of beliefs as a compromise between group-oriented vs. individual approach

An Artificial Intelligence Approach to Guiding the Management of Heart Failure Patients Using Predictive Models: A Systematic Review

Heart failure (HF) is one of the leading causes of mortality and hospitalization worldwide. The accurate prediction of mortality and readmission risk provides crucial information for guiding decision making. Unfortunately, traditional predictive models reached modest accuracy in HF populations. We therefore aimed to present predictive models based on machine learning (ML) techniques in HF patients that were externally validated. We searched four databases and the reference lists of the included papers to identify studies in which HF patient data were used to create a predictive model. Literature screening was conducted in Academic Search Ultimate, ERIC, Health Source Nursing/Academic Edition and MEDLINE. The protocol of the current systematic review was registered in the PROSPERO database with the registration number CRD42022344855. We considered all types of outcomes: mortality, rehospitalization, response to treatment and medication adherence. The area under the receiver operating characteristic curve (AUC) was used as the comparator parameter. The literature search yielded 1649 studies, of which 9 were included in the final analysis. The AUCs for the machine learning models ranged from 0.6494 to 0.913 in independent datasets, whereas the AUCs for statistical predictive scores ranged from 0.622 to 0.806. Our study showed an increasing number of ML predictive models concerning HF populations, although external validation remains infrequent. However, our findings revealed that ML approaches can outperform conventional risk scores and may play important role in HF management