Hybrydowe modele predykcyjne w marketingu relacji

• Autorzy: Mariusz Łapczyński

Warianty tytułu

Hybrid Predictive Models in Relationship Marketing

• Języki publikacji: PL

Abstrakty

Praca ma charakter teoretyczno-empiryczny, co wpływa na zawartość czterech kolejnych rozdziałów. Dwa pierwsze zawierają analizę dorobku naukowego w zakresie problematyki budowy modeli predykcyjnych w marketingu relacji z podziałem na poszczególne fazy cyklu życia klienta i etapy modelu ACURA (acquisition, cross-sell, up-sell, retention, advocacy). Dwa kolejne rozdziały mają charakter empiryczny, a znajdują się w nich przykłady hybrydowych modeli predykcyjnych oparte na drzewach klasyfikacyjnych i regułach asocjacyjnych. Głównym celem pracy jest porównanie statycznych hybrydowych modeli predykcyjnych z modelami podstawowymi użytymi w trakcie hybrydyzacji w trzech fazach cyklu życia klienta. (fragment tekstu)

EN

The book presents hybrid predictive models in relationship marketing. These models relate to the customer life cycle (acquisition phase, development phase and retention phase) and to the popular model ACURA. The need to analyse customer data is emphasized by all schools of relationship marketing. It is recognised that the relatively easy access to data combined with advanced models and analytical tools enable offers to be better tailored, targeting to be more effective, competitive advantage to be increased and customer retention to be improved. Analytical tasks are part of analytical CRM, database marketing, business intelligence and data-driven marketing, which can all to some extent be regarded as synonyms. Building predictive models goes beyond the services sphere (telecommunications industry, financial services) and relates to FMCG and durable goods. Combining models and analytical tools is now common practice when building predictive models in many research areas. The greater time the analytical procedure requires is often compensated by higher prediction accuracy, a reduced class imbalance problem and the ability to deliver clear patterns from complex structure datasets. Researchers distinguish between hybrid models (also known as two-step classification models, cascade models, integrated models, and cross-algorithm ensembles) where different analytical tools are combined and ensemble models (also known as committees) where the same analytical tools are combined. The author treats hybridisation as combining supervised models with unsupervised ones and combining classical statistical analytical tools with those derived from data mining and machine learning. When descriptive models (associations, sequences) are applied for predictive purposes, hybridisation is based on the sequential use of different tools that provide a clear interpretation of the results. The first hybrid predictive model refers to customer acquisition and was built by combining decision trees (C&RT algorithm) with k-means algorithm and Kohonen networks. The model is based on a real dataset from a cosmetics company's advertising campaign that uses a social networking website. The dependent variable has two categories: "the user clicked on the banner ad" and "the user ignored the advertising". After identifying the profile of Internet users who clicked on the banner ad, the company optimized the campaign by displaying it only on accounts of users with certain characteristics. The results clearly show that the hybrid approach has an advantage over the basic decision tree model with respect to all performance measures - accuracy, recall, precision and lift in the first two deciles. In the second hybrid predictive model - a churn model - decision trees (C&RT algorithm) were combined with logistic regression. During the experiment two datasets were used - both related to churn analysis and downloaded from popular online repositories. There are four advantages of the hybrid C&RT-logit approach: in-depth interpretation of the phenomenon, the identification of a stronger relationship between the independent variables and the dependent one, the unique probabilities of belonging to the category of dependent variable, and higher values of pseudo R2 measures when compared to a basic logistic regression model. The next two hybrid models presented in the book are based on association rules and sequential rules. Both refer to a phase of development of relations, that is, to cross-sell and up-sell. The first one is based on a real transaction dataset of a service company. The hybridisation procedure was carried out here in two ways. In the first approach, an algorithm searched for transactional rules in subsets (segments) that were delivered during RFM analysis. In the second approach, a large number of rules was clustered by using a k-means algorithm and SOM. The variables that were used during cluster analysis were the three popular performance measures: support, confidence and lift. Hybrid market basket analysis allows for faster selection of interesting transactional patterns. The second hybrid cross-sell and up-sell model combines sequential rules with social network analysis. The analysis looked at consumer behaviour during visits to an e-shop website offering clothing for women. It is an example of web usage mining, in particular web clickstream analysis. Due to the large number of rules, SNA was proposed, and its main goal was to effectively visualise the results. The final form of the model (the size and structure of the network) is affected by how two measures - support and confidence - are binarised. The book presents several alternative models with binarisation at the point of the median, arithmetic mean and upper quartile. (original abstract)

Słowa kluczowe

PL

Marketing relacji; Marketing społecznie zaangażowany; Data Mining; Zarządzanie Kontaktami z Klientem; Informacja marketingowa; Model logitowy; Rozprawa habilitacyjna; Uczenie maszynowe; Drzewo decyzyjne; Analiza sieciowa

EN

Relationship marketing; Cause Related Marketing (CRM); Data Mining; Customer Relationship Management (CRM); Marketing information; Logit model; Habilitation thesis; Machine learning; Decision tree; Network analysis

• Czasopismo: Zeszyty Naukowe / Uniwersytet Ekonomiczny w Krakowie. Seria Specjalna, Monografie
• Rocznik: 2016
• Numer: nr 240
• Strony: 204

Twórcy

autor

Mariusz Łapczyński

• Uniwersytet Ekonomiczny w Krakowie

Bibliografia

• Agrawal R., Imielinski T., Swami A. [1993], Mining Association Rules between Sets of Items in Large Databases, Proceedings of the ACM SIGMOD Conference on Management of Data, Washington D.C., May, s. 207-216.
• Ait-Saadi I., Mansor J., Norghani M.N. [2008], Asian Currency Crises: Indicators of Vulnerability Using CART-Logit Model [w:] Macroeconomic and Financial Linkages: Theory and Practice, Faculty of Economics, University of Cambridge.
• Ansell J., Harrison T., Archibald T. [2007], Identifying Cross-selling Opportunities, Using Lifestyle Segmentation and Survival Analysis, "Marketing Intelligence and Planning", nr 25(4), s. 394-410.
• Awang M.K., Rahman M.N.A, Ismail M.R. [2012], Data Mining for Churn Prediction: Multiple Regressions Approach [w:] Computer Applications for Database, Education, and Ubiquitous Computing. Communications in Computer and Information Science, red. T.H. Kim i in., vol. 352, Springer-Verlag, Berlin-Heidelberg, s. 318-324.
• Badania marketingowe. Podstawowe metody i obszary zastosowań [2002], red. K. Mazurek-Łopacińska, Wydawnictwo Akademii Ekonomicznej we Wrocławiu, Wrocław.
• Baecke P., Van den Poel D. [2011], Data Augmentation by Predicting Spending Pleasure Using Commercially Available External Data, "Journal of Intelligent Information Systems", nr 36, s. 367-383.
• Baecke P., Van den Poel D. [2013], Improving Customer Acquisition Models by Incorporating Spatial Autocorrelation at Different Levels of Granularity, "Journal of Intelligent Information Systems", nr 41, s. 73-90.
• Banasik A., Beliczyński J. [2003], Zarządzanie relacjami z klientami. Aplikacje systemu CRM, Wydawnictwo Akademii Ekonomicznej w Krakowie, Kraków.
• Bembenek B. [2006], Wokół metodologii wdrożeń CRM we współczesnym przedsiębiorstwie [w:] Badania marketingowe w przestrzeni europejskiej, red. K. Mazurek-Łopacińska, Prace Naukowe Akademii Ekonomicznej we Wrocławiu, nr 1107, Wrocław, s. 414-421.
• Berenson C. [1969], Marketing Information Systems, "Journal of Marketing", vol. 33, October, s. 16-23.
• Berry L.L. [1983], Relationship Marketing [w:] Emerging Perspectives in Services Marketing, red. L.L. Berry, G.L. Shostack i G.D. Upah, American Marketing Association, Chicago, s. 25-28.
• Berry L.L. [2000], Relationship Marketing of Services. Growing Interest, Emerging Perspectives [w:] Handbook of Relationship Marketing, red. J.N. Sheth i A. Parvatiyar, Sage Publications, Thousand Oaks, s. 149-170.
• Berry M.J.A., Linoff G.S. [2000], Mastering Data Mining. The Art and Science of Customer Relationship Management, John Wiley and Sons, New York.
• Berry M.J.A., Linoff G.S. [2004], Data Mining Techniques for Marketing, Sales, and Customer Relationship Management, 2nd ed., Wiley and Sons, New York.
• Bhaskar T., Sundararajan R., Krishnan P.G. [2009], A Fuzzy Mathematical Programming Approach for Cross-sell Optimization in Retail Banking, "Journal of the Operational Research Society", vol. 60, nr 5, s. 717-727.
• Bhasker B., Park H.H., Park J., Kim H.S. [2006], Product Recommendations for Cross-Selling in Electronic Business [w:] AI 2006: Advances in Artificial Intelligence, red. A. Sattar i B.H. Kang, Springer-Verlag, Berlin-Heidelberg, s. 1042-1047.
• Bhattacharya C.B., Bolton R.N. [2000], Relationship Marketing in Mass Markets [w:] Handbook of Relationship Marketing, red. J.N. Sheth i A. Parvatiyar, Sage Publications, Thousand Oaks, s. 327-354.
• Białynicka-Birula J. [2011], Analiza sieciowa układów wartości dla klienta [w:] Wartość dla klienta w układach rynkowych. Aspekty metodologiczne, red. A. Sagan, Wydawnictwo Uniwersytetu Ekonomicznego w Krakowie, Kraków, s. 98-111.
• Blattberg R.C., Kim B.D., Neslin S.A. [2008], Database Marketing. Analyzing and Managing Customers, Springer Science + Business Media, New York.
• Bose I., Chen X. [2009], Hybrid Models Using Unsupervised Clustering for Prediction of Customer Churn, "Journal of Organizational Computing and Electronic Commerce", vol. 19, nr 2, April-June, s. 133-151.
• Boulding W. i in. [2005], A Customer Relationship Management Roadmap: What Is Known, Potential Pitfalls, and Where to Go, "Journal of Marketing", vol. 69, October, s. 155-166.
• Bramer M. [2007], Principles of Data Mining, Springer-Verlag, London.
• Breiman L., Friedman J.H., Olshen R.A., Stone C.J. [1984], Classification and Regression Trees. Belmont, Wadsworth Belmont, CA.
• Brien R.H., Stafford J.E. [1968], Marketing Information Systems: A New Dimension for Marketing Research, "Journal of Marketing", vol. 32, July, s. 19-23.
• Brijs T. [2002], Retail Market Basket Analysis: A Quantitative Modelling Approach, Dissertation submitted to obtain the degree of Doctor in Applied Economic Sciences at the Limburg University Center, Limburg.
• Buckinx W., Van den Poel D. [2005], Customer Base Analysis: Partial Defection of Behaviourally Loyal Clients in a Non-contractual FMCG Retail Setting, "European Journal of Operational Research", nr 164, s. 252-268.
• Burez J., Van den Poel D. [2007], CRM at a Pay-TV Company: Using Analytical Models to Reduce Customer Attrition by Targeted Marketing for Subscription Services, "Expert Systems with Applications", nr 32, s. 277-288.
• Burgess A.N., Pandelidaki S. [1998], A Statistical Methodology for Specifying Neural Network Models: Application to the Identification of Cross-selling Opportunities [w:] Bio-Mimetic Approaches in Management Science, red. I.M. Aurifeille i C. Deissenberg, Springer-Verlag, Berlin-Heidelberg, s. 139-152.
• Buttle F. [2009], Customer Relationship Management. Concepts and Techniques, 2nd ed., Butterworth-Heinemann, Burlington, MA.
• Chapman P. i in. [2000], CRISP-DM 1.0. Step-by-Step Data Mining Guide, www.crisp.org (data dostępu: czerwiec 2008).
• Christopher M., Payne A., Ballantyne D. [2008], Relationship Marketing. Creating Stakeholder Value, 5th ed., Butterworth Heinemann, Oxford.
• Chu B.H., Tsai M.S., Ho Ch. S. [2007], Toward a Hybrid Data Mining Model for Customer Retention, "Knowledge-Based Systems", nr 20, s. 703-718.
• Churchill G.A. [1991], Marketing Research. Methodological Foundations, 5th ed., The Dryden Press International Edition, Orlando.
• Coussement K., Van den Poel D. [2008], Churn Prediction in Subscription Services: An Applications of Support Vector Machines While Comparing Two Parameter-Selection Techniques, "Expert Systems with Applications", nr 34, s. 313-327.
• Cox D.F., Good R.E. [1967], How to Build a Marketing Information System, "Harvard Business Review", vol. 45, nr 3, May-June, s. 145-154.
• Cunningham M.J. [2002], Customer Relationship Management, Capstone Publishing, Oxford.
• Customer Retention: A Business Imperative [2004], Baxter Strategies Incorporated, Woodbury, NY.
• Czerwiński Z. [2000], Jeszcze raz o prognozach [w:] Przestrzenno-czasowe modelowanie i prognozowanie zjawisk gospodarczych, red. A. Zeliaś, Materiały z XXI Ogólnopolskiego Seminarium Naukowego zorganizowanego przez Zakład Teorii Prognoz Katedry Statystyki Akademii Ekonomicznej w Krakowie (Zakopane, 21-23 kwietnia 1999), Kraków, s. 18-25.
• D'Ambrosio A., Pecoraro M. [2011], Multidimensional Scaling as Visualization Tool of Web Sequence Rules [w:] Classification and Multivariate Analysis for Complex Data Structures, red. B. Fichet, Springer, Berlin-Heidelberg, s. 309-316.
• Danaher P.J. [2008], Advertising Models [w:] Handbook of Marketing Decision Models, red. B. Wierenga, Springer-Verlag, Berlin-Heidelberg, s. 81-106.
• Das K. [2009], Relationship Marketing Research (1994-2006). An Academic Literature Review and Classification, "Marketing Intelligence and Planning", vol. 27, nr 3, s. 326-363.
• Dyche J. [2001], The CRM Handbook: A Business Guide to Customer Relationship Management, Addison-Wesley Professional, Crawfordsville, Indiana.
• Edelstein H. [1998], Data Mining - Let's Get Practical, "DB 2 Magazine Online", www. db2mag.com (data dostępu: październik 2001).
• Fader P.S., Hardie B.G.S., Lee K.L. [2006], More than Meets the Eye, "Marketing Research", vol. 18, nr 2, s. 9-14.
• Farquad M.A.H., Ravi V., Raju S.B. [2009], Data Mining Using Rules Extracted from SVM: An Application to Churn Prediction in Bank Credit Cards [w:] Rough Sets, Fuzzy Sets, Data Mining and Granular Computing. Lecture Notes in Computer Science, red. H. Sakai i in., vol. 5908, Springer-Verlag, Berlin-Heidelberg, s. 390-397.
• Farris P.W. i in. [2006], Marketing Metrics: 50+ Metrics Every Executive Should Master, Wharton School Publishing, New Jersey.
• Fayyad U., Piatesky-Shapiro G., Smyth P. [1996], From Data Mining to Knowledge Discovery in Databases, "Artificial Intelligence Magazine", Fall, s. 37-54.
• Fonfara K. [2004], Marketing partnerski na rynku przedsiębiorstw, PWE, Warszawa.
• Foss B., Stone M. [2002], CRM in Financial Services. A Practical Guide to Making Customer Relationship Management Work, Bell and Bain, Glasgow.
• Friedman J.H. [1997], Data Mining and Statistics: What's the Connection?, Proceedings of the 29th Symposium on the Interface: Computing Science and Statistics, Houston, Texas, May, s. 1-7.
• Gaddam S.R., Phoha V.V., Balagani K.S. [2007], K-means + ID3: A Novel Method for Supervised Anomaly Detection by Cascading K-means Clustering and ID3 Decision Tree Learning Methods, "IEEE Transactions on Knowledge and Data Engineering", vol. 19, nr 3, March, s. 345-354.
• Gandhi N., Bodkin Ch.D. [1996], Marketing Information Systems: Initiating a Dialogue for Cross-Disciplinary Courses, "Marketing Education Review", vol. 6, nr 2, Summer, s. 11-19.
• Gansky S.A., Cheng N., Pollick H.F. [2005], Predicting Early Childhood Caries with Individual, Family and Neighborhood Factors, Proceedings of IADR, Baltimore.
• Garbacik E. [1980], Ekonomika obrotu towarowego i usług, PWE, Warszawa.
• Gargano M.L., Raggad B.G. [1999], Data Mining - A Powerful Information Creating Tool, "OCLC Systems and Services", vol. 15, nr 2, s. 81-90.
• Gatnar E. [1998], Symboliczne metody klasyfikacji danych, PWN, Warszawa.
• Gatnar E. [2001], Nieparametryczna metoda dyskryminacji i regresji, PWN, Warszawa.
• Gatnar E. [2008], Podejście wielomodelowe w zagadnieniach dyskryminacji i regresji, Wydawnictwo Naukowe PWN, Warszawa.
• Giudici P. [2003], Applied Data Mining. Statistical Methods for Business and Industry, John Wiley and Sons, Norfolk.
• Giudici P., Dequarti E. [2011], Statistical Models to Predict Academic Churn Risk [w:] Classification and Multivariate Analysis for Complex Data Structures, red. B. Fichet i in., Springer-Verlag, Berlin-Heidelberg, s. 41-49.
• Glymour C., Madigan D., Pregibon D., Smyth P. [1997], Statistical Themes and Lessons for Data Mining, "Data Mining and Knowledge Discovery", nr 1, s. 11-28.
• Gopal R.K., Meher S.K. [2008], Customer Churn Time Prediction in Mobile Telecommunication Industry Using Ordinal Regression [w:] Advances in Knowledge Discovery and Data Mining. Lecture Notes in Computer Science, red. T. Washio i in., vol. 5012, Springer-Verlag, Berlin-Heidelberg, s. 884-889.
• Gordon I.H. [2001], Relacje z klientem. Marketing partnerski, PWE, Warszawa.
• Goslar M.D. [1986], Capability Criteria for Marketing Decision Support Systems, "Journal of Managament Information Systems", vol. 3, nr 1, Summer, s. 81-95.
• Gregor B., Grzegorczyk M. [2006], Pomiar w zarządzaniu relacjami z klientami [w:] Ekspansja czy regres marketingu?, red. E. Duliniec i in., PWE, Warszawa, s. 47-56.
• Gruen T.W. [2000], Membership Customers and Relationship Marketing [w:] Handbook of Relationship Marketing, red. N. Sheth i A. Parvatiyar, Sage Publications, California, s. 355-380.
• Gummesson E. [2008], Total Relationship Marketing, 3rd. ed, Butterworth-Heinemann, Oxford.
• Gupta S., Lehmann D.R. [2008], Models of Customer Value [w:] Handbook of Marketing Decision Models, red. B. Wierenga, Springer-Verlag, Berlin-Heidelberg, s. 255-290.
• Gupta S., Zeithaml V. [2005], Customer Metrics and Their Impact on Financial Performance, "Marketing Science", nr 25, s. 718-739.
• Hand D.J. [1998], Data Mining: Statistics and More?, "The American Statistician", nr 52, s. 112-118.
• Harmon R.R. [2003], Marketing Information Systems, "Encyclopedia of Information Systems", vol. 3, Elsevier Science, s. 137-151.
• Hastie T., Tibshirani R., Friedman J. [2009], The Elements of Statistical Learning. Data Mining, Inference, and Prediction, 2nd ed., Springer, New York.
• Hsu J. [2002], Data Mining Trends and Developments: The Key Data Mining Technologies and Applications for the 21st Century, Fairleigh Dickinson University, San Antonio, TX.
• Huang B.Q. i in. [2010], Using Genetic K-Means Algorithm for PCA Regression Data in Customer Churn Prediction [w:] Advanced Data Mining and Applications. Lecture Notes in Computer Science, red. L. Cao, J. Zhong i Y. Feng, vol. 6441, Springer-Verlag, Berlin-Heidelberg, s. 210-220.
• Huang B.Q., Kechadi M.T., Buckley B. [2009], Customer Churn Prediction for Broadband Internet Services [w:] Data Warehousing and Knowledge Discovery. Lecture Notes in Computer Science, red. T.B. Pedersen, M.K. Mohania i A.M. Tjoa, vol. 5691, Springer-Verlag, Berlin-Heidelberg, s. 229-243.
• Idris A., Khan A., Lee Y.S. [2013], Intelligent Churn Prediction in Telecom: Employing mRMR Feature Selection and RotBoost Based Ensemble Classification, "Applied Intelligence", nr 39, s. 659-672.
• Imber J. [2000], Dictionary of Marketing Terms, 3rd ed., Barron's Educational Series, New York.
• Introduction to Data Mining and Knowledge Discovery [1998], 2nd ed., The Two Crows Corporation, www.twocrows.com (data dostępu: listopad 2001).
• Iosifescu M. [1988], Skończone procesy Markowa i ich zastosowania, PWN, Warszawa.
• IT-Glossary [2013], http://www.gartner.com/it-glossary/business-intelligencebi/ (data dostępu: lipiec 2013).
• Ivanovic A., Collin P.H. [2003], Dictionary of Marketing, 3rd ed., Bloomsbury Publishing, London.
• Jajuga K. [1993], Statystyczna analiza wielowymiarowa, PWN, Warszawa.
• Jarrar Y.F., Neely A. [2002], Cross-selling in the Financial Sector: Customer Profitability Is Key, "Journal of Targeting, Measurement and Analysis for Marketing", vol. 10, nr 3, s. 282-296.
• Jeffery M. [2010], Data-Driven Marketing. The 15 Metrics Everyone in Marketing Should Know, Kellogg School of Management, Willey, New Jersey.
• Kamakura W.A. [2012], Sequential Market Basket Analysis, "Marketing Letters", vol. 23, s. 505-516.
• Kamakura W.A., Kossar B.S., Wedel M. [2004], Identifying Innovators for the Cross-Selling of New Products, "Management Science", vol. 50, nr 8, August, s. 1120-1133.
• Kim S., Shin K.S., Park K. [2005], An Application of SVM for Customer Churn Analysis: Credit Card Case [w:] Advances in Natural Computation. Lecture Notes in Computer Science, red. L. Wang, K. Chen i Y.S. Ong, vol. 3611, Springer-Verlag, Berlin-Heidelberg, s. 636-647.
• Kłeczek R. [2012], Marketing i wartość. Metodologiczne aspekty badania skuteczności działań marketingowych, Wydawnictwo Uniwersytetu Ekonomicznego we Wrocławiu, Wrocław.
• Knox S. i in. [2003], Customer Relationship Management. Perspectives from the Marketplace, Butterworth-Heinemann, Burlington, MA.
• Kolonko J. [1980], Analiza dyskryminacyjna i jej zastosowania w ekonomii, PWN, Warszawa.
• Kopaliński W. [2000], Słownik wyrazów obcych i zwrotów obcojęzycznych z almanachem, Klub Świat Książki, Warszawa.
• Kotler P. [1997], Marketing Management: Analysis, Planning, Implementation, and Control, 9th ed., Pearson Education, Upper Saddler River, NJ.
• Kowalska-Musiał M. [2006], Marketing relacyjny - zmiana paradygmatu czy nowa orientacja rynkowa, "Marketing i Rynek", nr 3, s. 2-8.
• Kowalska-Musiał M. [2009], Metody wizualizacji danych sieciowych, Prace Naukowe Uniwersytetu Ekonomicznego we Wrocławiu, nr 86, Wrocław, s. 223-231.
• Kriegel H.P. i in. [2007], Future Trends in Data Mining, "Data Mining and Knowledge Discovery", vol. 15, s. 87-97.
• Kudyba S., Hoptroff R. [2001], Data Mining and Business Intelligence: A Guide to Productivity, Idea Group Publishing, Hershey.
• Kumar V., Reinartz W. [2012], Customer Relationship Management. Concept, Strategy and Tools, 2nd ed., Springer, Berlin-Heidelberg.
• Kuziak M. [2012], Social media jako przestrzeń działań marketingowych: istota, kategoryzacja i pomiar [w:] Kierunki rozwoju współczesnego marketingu, red. A. Czubała, R. Niestrój i J.W. Wiktor, Fundacja Uniwersytetu Ekonomicznego w Krakowie, Kraków, s. 141-155.
• Kyle P.W. [1971], A Data Base for a Marketing Information System, "European Journal of Marketing", vol. 5, nr 2, s. 22-29.
• Larivière B., Van den Poel D. [2004], Investigating the Role of Product Features in Preventing Customer Churn, by Using Survival Analysis and Choice Modeling: The Case of Financial Services, "Expert Systems with Applications", nr 27, s. 277-285.
• Lee J.S., Lee J.C. [2006], Customer Churn Prediction by Hybrid Model [w:] Advanced Data Mining and Applications. Lecture Notes in Computer Science, red. X. Li, O.R. Zaiane i Z. Li, vol. 4093, Springer-Verlag, Berlin, s. 959-966.
• Lee K.C., Jo N.Y. [2010], Bayesian Network Approach to Predict Mobile Churn Motivations: Emphasis on General Bayesian Network, Markov Blanket, and What-If Simulation [w:] Future Generation Information Technology. Lecture Notes in Computer Science, red. T.H. Kim i in., vol. 6485, Springer-Verlag, Berlin-Heidelberg, s. 304-313.
• Lejeune M.A.P.M. [2001], Measuring the Impact of Data Mining on Churn Management, "Internet Research: Electronic Networking Applications and Policy", vol. 11, nr 5, s. 375-387.
• Lemmens A., Croux C. [2006], Bagging and Boosting Classification Trees to Predict Churn, "Journal of Marketing Research", vol. XLIII, May, s. 276-286.
• Lent B., Swami A.N., Widom J. [1997], Clustering Association Rules, Proceedings of the Thirteenth International Conference on Data Engineering, Birmingham U.K., IEEE Computer Society, s. 220-231.
• Li S., Sun B., Wilcox R.T. [2005], Cross-Selling Sequentially Ordered Products: An Application to Consumer Banking Services, "Journal of Marketing Research", vol. XLII, May, s. 233-239.
• Li Y., Deng Z., Qian Q., Xu R. [2011], Churn Forecast Based on Two-step Classification in Security Industry, "Intelligent Information Management", nr 3, s. 160-165.
• Li Y., Wang J. [2013], Combining Sequential Pattern Mining with Semantic Reasoning for Personalized Recommendation, "International Journal of Engineering and Industries (IJEI)", vol. 4, nr 1, s. 30-37.
• Liao K.H., Chueh H.E. [2011], Applying Fuzzy Data Mining to Telecom Churn Management [w:] Intelligent Computing and Information Science. Communications in Computer and Information Science, red. R. Chen, vol. 134, Springer-Verlag, Berlin-Heidelberg, s. 259-264.
• Lindahl W.E., Winship C. [1994], A Logit Model with Interactions for Predicting Major Gift Donors, "Research in Higher Education", vol. 35, nr 6, s. 729-743.
• Little J.D.C. [1979], Decision Support Systems for Marketing Managers, " Journal of Marketing", vol. 43, Summer, s. 9-26.
• Liu B. [2007], Web Data Mining. Exploring Hyperlinks, Contents, and Usage Data, Springer, Heidelberg.
• Liu D.R., Lai C.H., Lee W.J. [2009], A Hybrid of Sequential Rules and Collaborative Filtering for Product Recommendation, "Information Sciences", vol. 179, nr 20, 29 September, s. 3505-3519.
• Łapczyński M. [2003], Wprowadzenie do data mining, "Zeszyty Naukowe Akademii Ekonomicznej w Krakowie", nr 640, s. 49-62.
• Łapczyński M. [2009], Modele hybrydowe CART-LOGIT w analizie danych rynkowych [w:] Projektowanie, ocena i wykorzystanie danych rynkowych, red. J. Dziechciarz, Prace Naukowe Uniwersytetu Ekonomicznego we Wrocławiu, nr 51, Wrocław, s. 85-95.
• Łapczyński M. [2010], Drzewa klasyfikacyjne w badaniach rynkowych i marketingowych., Wydawnictwo Uniwersytetu Ekonomicznego w Krakowie, Kraków.
• Łapczyński M. [2014], Analiza asocjacji i sekwencji w badaniach marketingowych, Wydawnictwo Uniwersytetu Ekonomicznego w Krakowie, Kraków.
• Łapczyński M. [2016], The Use of Hybrid Predictive C&RT-logit Models in Analytical CRM [w:] Analysis of Large and Complex Data, red. W.F.X. Adalbert i H.A. Kestler, Springer-Verlag, Berlin-Heidelberg.
• Łapczyński M., Surma J. [2012], Hybrid Predictive Models for Optimizing Marketing Banner Ad Campaign in On-line Social Network, Proceedings of the 2012 International Conference on Data Mining (DMIN 2012), red. R. Stahlbock i G.M. Weiss, CSREA Press, USA, s. 140-146.
• Maindonald J. [2001], Data Mining from a Statistical Perspective, wwwmaths.anu.edu.au (data dostępu: październik 2001).
• Makridakis S., Wheelwright S.C. [1977], Forecasting: Issues and Challenges for Marketing Management, "Journal of Marketing", October, s. 26-27.
• Mannila H. [1996], Data Mining: Machine Learning, Statistics, and Databases, Department of Computer Science, University of Helsinki, www.cs.helsinki.fi (data dostępu: listopad 2001).
• Masand B. i in. [1999], CHAMP: A Prototype for Automated Cellular Churn Prediction, "Data Mining and Knowledge Discovery", nr 3, s. 219-225.
• Mattison R. [2005], The Telco Churn Management Handbook, XiT Press, Oakwood Hills, Illinois.
• Mazurek-Łopacińska K., Sobocińska M. [2012], Rozwój koncepcji marketingowych w aspekcie wpływu na zmiany obszarów, metod i technik badawczych, Prezentacja wygłoszona podczas XVI Warsztatów Metodologicznych im. Profesora Stefana Mynarskiego, Kraków, 23 listopada.
• Miguéis V.L. i in. [2012], Predicting Partial Customer Churn Using Markov for Discrimination for Modeling First Purchase Sequences, "Advances in Data Analysis and Classification", nr 6, s. 337-353.
• Mitręga M. [2005], Marketing relacji. Teoria i praktyka, CeDeWu, Warszawa.
• Mitręga M., Forkmann S., Ramos C., Henneberg S.C. [2012], Networking Capability in Business Relationships - Concept and Scale Development, "Industrial Marketing Management", vol. 41(5), s. 739-751.
• Mobasher B. [2007], Web Usage Mining [w:] Web Data Mining. Exploring Hyperlinks, Contents, and Usage Data, red. B. Liu, Springer-Verlag, Berlin-Heidelberg, s. 449-483.
• Montgomery D.B., Urban G.L. [1970], Marketing Decision-Information Systems: An Emerging View, "Journal of Marketing Research", vol. VII, May, s. 226-234.
• Morik M., Köpcke H. [2004], Analysing Customer Churn in Insurance Data - a Case Study, Proceedings of the 8th European Conference on Principles and Practice of Knowledge Discovery in Databases, red. J.F. Boulicaut i in., Springer-Verlag, Berlin Heidelberg, s. 325-336.
• Moro S., Laureano R., Cortez P. [2011], Using Data Mining for Bank Direct Marketing: An Application of the CRISP-DM Methodology, Proceedings of the European Simulation and Modelling Conference - ESM'2011, red. P. Novais i in., Guimarães, Portugal, October, s. 117-121.
• Morwitz V.G., Johnson E., Schmittlein D. [1993], Does Measuring Intent Change Behavior?, "The Journal of Consumer Research", vol. 20, nr 1, s. 46-61.
• Möller K., Pels J., Saren M. [2010], The Marketing Theory or Theories into Marketing? Plurality of Research Traditions and Paradigms [w:] The Sage Handbook of Marketing Theory, red. P. Maclaran i in., Sage, London, s. 151-173.
• Mynarski S. [1995], Badania rynkowe w warunkach konkurencji, Fogra, Kraków.
• Naveen N., Ravi V., Rao C.R. [2010], Data Mining via Rules Extracted from GMDH: An Application to Predict Churn in Bank Credit Cards [w:] Knowledge-Based and Intelligent Information and Engineering Systems. Lecture Notes in Computer Science, red. R. Setchi i in., vol. 6276, Springer-Verlag, Berlin-Heidelberg, s. 80-89.
• Neslin S.A. i in. [2006], Defection Detection: Measuring and Understanding the Predictive Accuracy of Customer Churn Models, "Journal of Marketing Research", vol. XLIII, May, s. 204-211.
• Netessine S., Savin S., Xiao W. [2006], Revenue Management through Dynamic Cross-selling in E-commerce Retailing, "Operations Research", vol. 54, nr 5, September-October, s. 893-913.
• Nie G. i in. [2009], Finding the Hidden Pattern of Credit Card Holder's Churn: A Case of China [w:] Computational Science - ICCS 2009. Lecture Notes in Computer Science, red. G. Allen i in., vol. 5545, Springer-Verlag, Berlin-Heidelberg, s. 561-569.
• Nowak S. [2007], Metodologia badań społecznych, PWN, Warszawa.
• Otto J. [2004], Marketing relacji. Koncepcja i stosowanie, 2 wyd., Wydawnictwo C.H. Beck, Warszawa.
• Osiewalski J., Osiewalski K. [2012], Modele hybrydowe MSV-MGARCH z dwoma procesami ukrytymi, "Zeszyty Naukowe Uniwersytetu Ekonomicznego w Krakowie", nr 895, s. 5-18.
• Parol M. [2014], Prognozowanie ultrakrótkoterminowe mocy generowanej w odnawialnych źródłach energii z wykorzystaniem logiki rozmytej, "Przegląd Elektrotechniczny", nr 6, s. 265-268.
• Parvatiyar A., Sheth J.N. [2000], The Domain and Conceptual Foundations of Relationship Marketing [w:] Handbook of Relationship Marketing, red. J.N. Sheth i A. Parvatiyar, Sage Publications, California, s. 3-38.
• Pawłowski Z. [1981], Elementy ekonometrii, PWN, Warszawa.
• Pawłowski Z. [1982], Zasady predykcji ekonometrycznej, PWN, Warszawa.
• Payne A. [2005], Handbook of CRM: Achieving Excellence in Customer Management, Butterworth-Heinemann, Burlington, MA.
• Peppers D., Rogers M. [2004], Managing Customer Relationships. A Strategic Framework, John Wiley and Sons, Hoboken, New Jersey.
• Phadke C. i in. [2013], Prediction of Subscriber Churn Using Social Network Analysis, "Bell Labs Technical Journal", nr 17(4), s. 63-76.
• Pitta D.A. [1998], Marketing One-to-One and Its Dependence on Knowledge Discovery in Databases, "The Journal of Consumer Marketing", nr 15(5), s. 468-480.
• Płaczek B. [2012], Modelowanie strumieni pojazdów z zastosowaniem automatów komórkowych i liczb rozmytych, "Logistyka", nr 4, s. 607-618
• Prinzie A., Van den Poel D. [2006], Investigating Purchasing-sequence Patterns for Financial Services Using Markov, MTD and MTDg Models, "European Journal of Operational Research", nr 170, s. 710-734.
• Prinzie A., Van den Poel D. [2007], Predicting Home-appliance Acquisition Sequences: Markov/Markov for Discrimination and Survival Analysis for Modeling Sequential Information in NPTB Models, "Decision Support Systems", nr 44, s. 28-45.
• Prinzie A., Van den Poel D. [2008], Random Forests for Multiclass Classification: Random MultiNomial Logit, "Expert Systems with Applications", nr 34, s. 1721-1732.
• Prinzie A., Van den Poel D. [2011], Modeling Complex Longitudinal Consumer Behavior with Dynamic Bayesian Networks: An Acquisition Pattern Analysis Application, "Journal of Intelligent Information Systems", nr 36, s. 283-304.
• Pyle D. [1999], Data Preparation for Data Mining, Morgan Kaufmann Publishers, San Francisco, California.
• Reinartz W.J., Venkatesan R. [2008], Decision Models for Customer Relationship Management (CRM) [w:] Handbook of Marketing Decision Models, red. B. Wierenga, Springer Science + Business Media, Berlin-Heidelberg, s. 291-326.
• Rezende S.O. i in. [2009], Combining Data-Driven and User-Driven Evaluation Measures to Identify Interesting Rules [w:] Post-mining of Association Rules. Techniques for Effective Knowledge Extraction, red. Y. Zhao, Ch. Zhang i L. Cao, IGI Global, New York, s. 38-55.
• Rodriguez J.J., Kuncheva L.I., Alonso C.J. [2006], Rotation Forest: A New Classifier Ensemble Method, "IEEE Transactions Pattern Analysis Machine Intelligence", nr 28(10), s. 1619-1630.
• Rogoziński K. [1998], Nowy marketing usług, Wydawnictwo Akademii Ekonomicznej w Poznaniu, Poznań.
• Rószkiewicz M. [2011], Analiza klienta, SPSS Polska, Kraków.
• Rudawska E. [2008], Znaczenie relacji z klientami w procesie kształtowania wartości przedsiębiorstwa, Wydawnictwo Naukowe Uniwersytetu Szczecińskiego, Szczecin.
• Rutkowski I., Wrzosek W. [1985], Strategia marketingowa, PWE, Warszawa.
• Sagan A. [2001], Metody sieciowe w analizie łańcuchów środków-celów z wykorzystaniem programu UCINET, "Zeszyty Naukowe Akademii Ekonomicznej w Krakowie", nr 558, s. 23-36.
• Sagan A. [2011], Krzywe operacyjno-charakterystyczne w ewaluacyjnych badaniach marketingowych, "Zeszyty Naukowe Uniwersytetu Ekonomicznego w Krakowie", nr 864, s. 15-17.
• Sagan A. [2012], Paradygmaty w marketingu - próba syntezy, "Marketing i Rynek", nr 11, s. 2-10.
• Sagan A. [2013], Zmienne ukryte w badaniach marketingowych, Wydawnictwo Uniwersytetu Ekonomicznego w Krakowie, Kraków.
• Sagan A., Łapczyński M. [2014], Modele hybrydowe CART-logit w analizie procesu podejmowania decyzji w gospodarstwie domowym, Prace Naukowe Uniwersytetu Ekonomicznego we Wrocławiu, nr 336, Wrocław, s. 60-69.
• Salazar M.T., Harrison T., Ansell J. [2007], An Approach for the Identification of Cross-sell and Up-sell Opportunities Using a Financial Services Customer Database, "Journal of Financial Services Marketing", vol. 12(2), s. 115-131.
• Saravanan M., Vijay Raajaa G.S. [2012], A Graph-Based Churn Prediction Model for Mobile Telecom Networks [w:] Advanced Data Mining and Applications. Lecture Notes in Computer Science, red. S. Zhou, S. Zhang i G. Karypis, vol. 7713, Springer-Verlag, Berlin-Heidelberg, s. 367-382.
• Shawkat A., Wasimi S.A. [2007], Data Mining: Methods and Techniques, Thomson Learning, Australia.
• Shim B., Choi K., Suh Y. [2012], CRM Strategies for a Small-sized Online Shopping Mall Based on Association Rulesand Sequential Patterns, "Expert Systems with Applications", vol. 39, s. 7736-7742.
• Shouman, M., Turner, T., Stocker, R. [2012], Integrating Decision Tree and K-Means Clustering with Different Initial Centroid Selection Methods in the Diagnosis of Heart Disease Patients, Proceedings of the 2012 International al Conference on Data Mining, red. R. Stahlbock i G.M. Weiss, CSREA Press, Las Vegas, Nevada, s. 24-30.
• Singh S.S., Jain D.C. [2013], Measuring Customer Lifetime Value: Models and Analysis, "Faculty and Research Working Paper", INSEAD, Hyderabad, India, s. 1-50.
• Sisodia R.S., Wolfe D.B. [2000], Information Technology. Its Role in Building, Maintaining and Enhancing Relationships [w:] Handbook of Relationship Marketing, red. J.N. Sheth i A. Parvatiyar, Sage Publications, California, s. 525-563.
• Sojkin B. [2009], Informacyjne podstawy decyzji marketingowych, PWE, Warszawa.
• Spangenberg E.R., Greenwald A.G. [1999], Social Influence by Requesting Self-prophecy, "Journal of Consumer Psychology", nr 8, s. 61-89.
• Srivastava J., Cooley R., Deshpande M., Tan P.N. [2000], Web Usage Mining: Discovery and Applications of Usage Patterns from Web Data, "ACM SIGKDD Explorations Newsletter", vol. 1, nr 2, January, s. 12-23.
• Srivastava T., Desikan P., Kumar V. [2005], Web Mining - Concepts, Applications and Research Directions, "Studies in Fuzziness and Soft Computing", vol. 180, s. 275-307.
• Steinberg D., Cardell N.S. [1998], The Hybrid CART-logit Model in Cassification and Data Mining, www.salford-systems.com (data dostępu: 10.01.2008).
• Storbacka K. [2000], Customer Profitability. Analysis and Design Issues [w:] The Handbook of Relationship Marketing, red. J.N. Sheth i A. Parvatiyar, Sage Publications, California, s. 565-586.
• Storbacka K., Lehtinen J.R. [2001], Sztuka budowania trwałych związków z klientami, Oficyna Ekonomiczna, Kraków.
• Strzyżewska M., Rószkiewicz M. [2002], Analizy marketingowe, Difin, Warszawa.
• Surma J. [2009], Business Intelligence. Systemy wspomagania decyzji biznesowych, PWN, Warszawa.
• Tähtinen J., Halinen A. [2002], Research on Ending Exchange Relationships: A Categorization, Assessment and Outlook, "Marketing Theory", vol. 2, nr 2, s. 165-188.
• Terlutter R., Weinberg P. [2006], Relationship Marketing in European Consumer Goods Markets: From Marketing Mix Orientation to Customer Life Cycle Management [w:] Strategic Management - New Rules for Old Europe, red. Ch. Scholz i J. Zentes, Springer, Berlin-Heidelberg, s. 123-136.
• Tsiptsis K., Chorianopoulos A. [2009], Data Mining Techniques in CRM. Inside Customer Segmentation, John Wiley and Sons, West Sussex.
• Tukey J.W. [1977], Exploratory Data Analysis, Addison-Wesley, Reading, MA.
• Tull D.S., Hawkins D.I. [1993], Marketing Research: Measurement and Methods, 6th ed., Prentice Hall, Upper Saddle River, NJ.
• Van den Poel D., Larivière B. [2004], Customer Attrition Analysis for Financial Services Using Proportional Hazard Models, "European Journal of Operational Research", nr 157, s. 196-217.
• Venkatadri M., Reddy L.C. [2011], A Review on Data mining from Past to the Future, "International Journal of Computer Applications", vol. 15, nr 7, Februrary, s. 19-22.
• Verhaert G.A., Van den Poel D. [2011], Improving Campaign Success Rate by Tailoring Donation Requests along the Donor Lifecycle, "Journal of Interactive Marketing", nr 25, s. 51-63.
• Wang K., Zhou S., Yang Q., Yeung J.M.S. [2003], Mining Customer Value: From Association Rules to Direct Marketing, Proceedings from 19th International Conference on Data Engineering, 5-8 March, s. 738-765.
• Wei M., Chai L., Wei R., Huo W. [2008], A Solution to the Cross-selling Problem of PAKDD-2007: Ensemble Model of treeNet and Logistic Regression, "International Journal of Data Warehousing and Mining", vol. 4, nr 2, s. 9-14.
• Weiss S.M., Indurkhya N. [1998], Predictive Data Mining, Morgan Kaufmann, San Francisco.
• Wiktor J.W. [2013], Komunikacja marketingowa, PWN, Warszawa.
• Witkowska D. [2005], Podstawy ekonometrii i teorii prognozowania, Oficyna Ekonomiczna, Kraków.
• Witten I.H., Frank E., Hall M.A. [2011], Data Mining. Practical Machine Learning Tools and Techniques, 3rd ed., Morgan Kaufmann, Burlington.
• Wong R.Ch.W., Fu A.W.Ch. [2004], ISM: Item Selection for Marketing with Cross-Selling Considerations [w:] Advances in Knowledge Discovery and Data Mining, red. H. Dai, R. Srikant i C. Zhang, Springer-Verlag, Berlin-Heidelberg, s. 431-440.
• Wong R.Ch.W., Fu A.W.Ch., Wang K. [2005], Data Mining for Inventory Item Selection with Cross-Selling Considerations, "Data Mining and Knowledge Discovery", nr 11, s. 81-112.
• Woodall T. [2003], Conceptualising "Value for the Customer": An Attributional, Structural and Dispositional Analysis, "Academy of Marketing Science Review", nr 12, s. 1-41.
• Wuebben M. [2008], Analytical CRM. Developing and Maintaining Profitable Customer Relationships in Non-Contractual Settings, Gabler Edition Wissenschaft, Wiesbaden.
• Xu M., Walton J. [2005], Gaining Customer Knowledge through Analytical CRM, "Industrial Management + Data Systems", vol. 105, nr 7, s. 955-971.
• Yadin D. [2002], The International Dictionary of Marketing, Kogan Page, London.
• Zeliaś A. [1997], Teoria prognozy, PWE, Warszawa.
• Zhang C.X., Wang G.W., Zhang J.S. [2012], An Empirical Biasvariance Analysis of DECORATE Ensemble Method at Different Training Sample Sizes, "Journal of Applied Statistics", nr 39(4), s. 829-850.
• Zhang C.X., Zhang J.S. [2008], RotBoost: A Technique for Combining Rotation Forest and AdaBoost, "Pattern Recognition Letters", nr 29(10), s. 1524-1536.
• Zhao Y. i in. [2005], Customer Churn Prediction Using Improved One-Class Support Vector Machine [w:] Advanced Data Mining and Applications, red. X. Li, S. Wang i Z.Y. Dong, Springer-Verlag, Berlin-Heidelberg, s. 300-306.
• Zhao Y., Zhang Ch., Cao L. [2009], Post-mining of Association Rules. Techniques for Effective Knowledge Extraction, IGI Global, New York