Analytical decision list algorithm for managing customer reaction to a marketing campaign

• Autorzy: Ushakova Iryna , Hrabovskyi Yevhen , Szymczyk Katarzyna

Identyfikatory

DOI

10.17512/pjms.2023.28.1.21

Warianty tytułu

PL

Algorytm analitycznej listy decyzyjnej do zarządzania reakcją klienta na kampanię marketingową

• Języki publikacji: EN

Abstrakty

EN

In this paper, the authors aim to develop a methodology for customer segmentation based on their response to marketing campaigns, considering customer value using predictive analytics methods and computer modeling tools. The scientific novelty of this article is the method of modeling and analyzing customer reactions to a marketing campaign. This method includes the following stages: questionnaire development and customer data collection; preliminary analysis of the received data; preparation of customer data in a formalized presentation; RFM analysis of customer value; building a model of customer feedback on a marketing campaign based on the solution list algorithm; analysis of the obtained results. The decision list algorithm was chosen to model customer response to marketing campaigns, which provides an inherent order to the rule set and a more accessible interpretation of the results. The IBM SPSS Modeler was used as a modeling tool. Customer information for the model was obtained through a survey conducted among customers of companies manufacturing packaging goods using a specially designed questionnaire. The practical value of the research lies in the application of the results of customer segmentation to create marketing strategies by a company that can consider the results of both models and group them to cover a wider range of customers.

PL

W artykule autorzy stawiają sobie za cel opracowanie metodologii segmentacji klientów na podstawie ich reakcji na kampanie marketingowe, z uwzględnieniem wartości klienta z wykorzystaniem metod analityki predykcyjnej i narzędzi modelowania komputerowego. Nowością naukową artykułu jest metoda modelowania i analizy reakcji klientów na kampanię marketingową. Metoda ta obejmuje następujące etapy: opracowanie kwestionariusza i zebranie danych o klientach; wstępną analizę otrzymanych danych; przygotowanie danych klienta w sformalizowanej prezentacji; analizę RFM wartości klienta; zbudowanie modelu opinii klientów o kampanii marketingowej w oparciu o algorytm listy rozwiązań; analizę uzyskanych wyników. Algorytm listy decyzyjnej został wybrany do modelowania reakcji klientów na kampanie marketingowe, co zapewnia nieodłączne uporządkowanie zestawu reguł i bardziej przystępną interpretację wyników. Jako narzędzie modelowania wykorzystano program IBM SPSS Modeler. Informacje o kliencie dotyczące modelu uzyskano poprzez ankietę przeprowadzoną wśród klientów firm produkujących towary opakowaniowe za pomocą specjalnie zaprojektowanej ankiety. Praktyczna wartość badania polega na zastosowaniu wyników segmentacji klientów do tworzenia strategii marketingowych przez firmę, która może uwzględnić wyniki obu modeli i pogrupować je w celu objęcia szerszego grona klientów.

Słowa kluczowe

EN

predictive analytics; strategic foresight; marketing campaign; decision list algorithm; computer modelling

PL

analityka predykcyjna; foresight strategiczny; kampania marketingowa; algorytm listy decyzyjnej; modelowanie komputerowe

• Wydawca: Czestochowa University of Technology
• Czasopismo: Polish Journal of Management Studies
• Rocznik: 2023
• Tom: Vol. 28. No. 1
• Strony: 360--379
• Opis fizyczny: Bibliogr. 43 poz., rys., tab.

Twórcy

autor

Ushakova Iryna

• Simon Kuznets Kharkiv National University of Economics, Ukraine

• https://orcid.org/0000-0001-8315-0917

autor

Hrabovskyi Yevhen

• Simon Kuznets Kharkiv National University of Economics, Ukraine

• https://orcid.org/0000-0001-7799-7249

autor

Szymczyk Katarzyna

• Czestochowa University of Technology, Poland

• https://orcid.org/0000-0003-0869-2454

Bibliografia

• 1.Alwis, P. K. D. N. M., Kumara, B. T. G. S. and Hapuarachchi, H. A. C. S., (2018). Customer Churn Analysis and Prediction in Telecommunication for Decision Making. 2018 International Conference on Business Innovation (ICOBI), NSBM, Colombo, Sri Lanka.
• 2.Andersen, P., Weisstein, F. L. and Song, L., (2019). Consumer response to marketing channels: A demand-based approach. Journal of Marketing Channels, 26(1), 43-59.
• 3.Asniar, Surendro, K., (2019). Predictive Analytics for Predicting Customer Behavior. 2019 International Conference of Artificial Intelligence and Information Technology (ICAIIT).
• 4.Bahrami, M., Bozkaya, B. and Balcisoy, S., (2020). Using Behavioral Analytics to Predict Customer Invoice Payment. Big Data, 8(1), 25-37.
• 5.Baryshnikova, N., Kiriliuk, O. and Klimecka-Tatar, D., (2021). Enterprises’ strategies transformation in the real sector of the economy in the context of the COVID-19 pandemic. Production Engineering Archives, 27(1), 8-15.
• 6.Bobocea, L., Spiridon, S., Petrescu, L., Gheorghe, C. and Purcarea, V., (2016). The management of external marketing communication instruments in health care services. Journal of Medicine and Life, 9(2), 137-140.
• 7.Brei, V. A., (2020). Machine Learning in Marketing: Overview, Learning Strategies, Applications, and Future Developments. Foundations and Trends® in Marketing, 14(3), 173-236.
• 8.Castanho, R. A., Vulevic, A., Gómez, J. M. N., Cabezas, J., Fernández-Pozo, L., Loures, L., ... & Kurowska-Pysz, J. (2023). Assessing the impact of marketing and advertising as strategic approaches to Eurocities development: An Iberian case study approach. European Journal of International Management, 19(1), 58-91.
• 9.Czajkowska, A., (2016). SWOT analysis application for indications of the strategy action chosen enterprise in the construction sector. Production Engineering Archives, 10(1), 33- 37.
• 10.De Caigny, A., Coussement, K., Verbeke, W., Idbenjra, K. and Phan, M., (2021). Uplift modeling and its implications for B2B customer churn prediction: A segmentation-based modeling approach. Industrial Marketing Management, 99, 28-39.
• 11.Dorokhov, O., Dorokhova, L., Malyarets, L. and Ushakova, I., (2020). Customer churn predictive modeling by classification methods. Series III - Matematics, Informatics, Physics, 13(62)(1), 347-362.
• 12.Giri, A., Paul, P., (2020). Applied marketing analytics using SPSS. PHI Learning Pvt. Ltd.
• 13.Grundey, D., (2010). Planning for Sales Promotion at Lithuanian Supermarkets. Economics and Sociology, 3(2).
• 14.Gupta, S., Joshi, S., (2022). Predictive Analytic Techniques for enhancing marketing performance and Personalized Customer Experience. IEEE Xplore.
• 15.Hambrick, D. C., MacMillan, I. C. and Day, D. L., (1982). Strategic Attributes and Performance in the BCG Matrix—A PIMS-Based Analysis of Industrial Product Businesses. Academy of Management Journal, 25(3), 510-531.
• 16.Ho, J. K. K., (2014). Formulation of a systemic PEST analysis for strategic analysis. European academic research, 2(5), 6478-6492.
• 17.Hou, R., Ye, X., Zaki, H. B. O. and Omar, N. A. B., (2023). Marketing Decision Support System Based on Data Mining Technology. Applied Sciences, 13(7), 4315.
• 18.Hrabovskyi, Y., Kots, H. and Szymczyk, K., (2022a). Justification of the innovative strategy of information technology implementation for the implementation of multimedia publishing business projects. Proceedings on Engineering Sciences, 4(4), 467-480.
• 19.Hrabovskyi, Y., Minukhin, S. and Brynza, N., (2022b). Development of an information support methodology for quality assessment of the prepress process. Eastern-European Journal of Enterprise Technologies, 6(2 (120)), 30-40.
• 20.Kalicanin, K., Colovic, M., Njegus, A. and Mitic, V., (2019). Benefits of Artificial Intelligence and Machine Learning in Marketing. Proceedings of the International Scientific Conference - Sinteza 2019.
• 21.Karimi, S., Papamichail, K. N. and Holland, C. P., (2015). The effect of prior knowledge and decision-making style on the online purchase decision-making process: A typology of consumer shopping behaviour. Decision Support Systems, 77(1), 137-147.
• 22.Kasem, M. S., Hamada, M. and Taj-Eddin, I., (2023). Customer Profiling, Segmentation, and Sales Prediction using AI in Direct Marketing. ArXiv (Cornell University).
• 23.Kazmierczak-Piwko, L., Kułyk, P., Dybikowska, A., Dubicki, P. and Binek, Z., (2022). Sustainable consumption among children and adolescents. Production Engineering Archives, 28(3), 257-267.
• 24.Kim, S., Lee, H., (2022). Customer Churn Prediction in Influencer Commerce: An Application of Decision Trees. Procedia Computer Science, 199, 1332-1339.
• 25.Knez, M., Obrecht, M., (2018). How can people be convinced to buy electric cars? - case of Slovenia. Production Engineering Archives, 21(21), 24-27.
• 26.Liu, C.-J., Huang, T.-S., Ho, P.-T., Huang, J.-C. and Hsieh, C.-T., (2020). Machine learning-based e-commerce platform repurchase customer prediction model. Plos One, 15(12), e0243105.
• 27.Mauro, A. D., Sestino, A. and Bacconi, A., (2022). Machine learning and artificial intelligence use in marketing: a general taxonomy. Italian Journal of Marketing, 2022(4), 439-457.
• 28.Mohajan, H., (2017). An Analysis on BCG Growth Sharing Matrix. Noble International Journal of Business and Management Research, 2(1), 1-6.
• 29.Noori, B., (2021). Classification of Customer Reviews Using Machine Learning Algorithms. Applied Artificial Intelligence, 35(8), 567-588.
• 30.Oliveira, G. D., Dias, L. C., (2020). The potential learning effect of a MCDA approach on consumer preferences for alternative fuel vehicles. Annals of Operations Research, 293(2), 767-787.
• 31.Rauyruen, P., Miller, K. E., (2007). Relationship quality as a predictor of B2B customer loyalty. Journal of Business Research, 60(1), 21-31.
• 32.Reshetnikova, M. S., Mikhaylov, I. A., (2023). Artificial Intelligence Development: Implications for China, Montenegrin Journal of Economics, 19(1), 139-152.
• 33.Sarker, I. H., (2022). AI-Based Modeling: Techniques, Applications and Research Issues Towards Automation, Intelligent and Smart Systems. SN Computer Science, 3(2). springer.
• 34.Siau, K., Yang, Y., (2017). Impact of Artificial Intelligence, Robotics, and Machine Learning on Sales and Marketing. MWAIS 2017 Proceedings, 48.
• 35.SPSS ModelerAlgorithms Guide (2020). IBM Corporation. 804 p.
• 36.Stancu, I., Meghisan, G.-M., (2014). Strategic Planning Based on Consumers’ Decision Making Process towards Mobile Telecommunications Operators. Procedia Economics and Finance, 15, 1528-1534.
• 37.Sterne, J., (2017). Artificial intelligence for marketing: practical applications. Wiley. Copyright.
• 38.Tamaddoni Jahromi, A., Stakhovych, S. and Ewing, M., (2014). Managing B2B customer churn, retention and profitability. Industrial Marketing Management, 43(7), 1258-1268.
• 39.Tong, Y., Saladrigues, R., (2023). The Influence of Intellectual Capital on the Financial Performance of Spanish New Firms, Montenegrin Journal of Economics, 19(2), 179-188.
• 40.Ushakova, I., Skorin, Y., Shcherbakov, A. and Kharkiv, S., (2021). Methods of quality assurance of software development based on a systems approach. III International Scientific and Practical Conference “Information Security and Information Technologies,” Odesa, Ukraine.
• 41.Wątróbski, J., Jankowski, J. and Ziemba, P., (2016). Multistage performance modelling in digital marketing management. Economics and Sociology, 9(2), 101-125.
• 42.Wolniak, R.,(2020). Main Functions of Operation Management. Production Engineering Archives, 26(1), 11-14.
• 43.Zhao, X., Keikhosrokiani, P., (2022). Sales Prediction and Product Recommendation Model Through User Behavior Analytics. Computers, Materials and Continua, 70(2), 3855- 3874.

Uwagi

Opracowanie rekordu ze środków MNiSW, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2024).