Predictive Analytics in Marketing: Contribution to Marketing Performance
DOI:
https://doi.org/10.62207/0qan8b95Keywords:
Predictive analysis, marketing decision making, consumer behavior prediction, brand equity, brand attachment, self-concept, value perception, social psychology theory, destination image, sustainability, marketing practices, predictive marketing algorithms.Abstract
This systematic literature review explores predictive analytics in marketing decision making and its relationship to key concepts in consumer behavior prediction. Drawing on established theories and empirical studies, this study explores the influence of customer-based brand equity, brand attachment, self-concept, perceived value, and other variables on consumer purchasing behavior and intentions. Additionally, this study investigates the impact of social psychology theory, destination image, sustainability in marketing, and marketing practices that align with consumer values on satisfaction, engagement, loyalty, and long-term relationships with brands. Apart from that, this study also tests the effectiveness of predictive marketing algorithms in improving marketing and sales performance. These findings emphasize the importance of integrating consumer-centric approaches and predictive analytics in forming successful marketing strategies and achieving desired results in today's competitive market landscape.
References
Abolghasemi, Y. and Dimitrov, S. (2020). Determining the causality between u.s. presidential prediction markets and global financial markets. International Journal of Finance & Economics, 26(3), 4534-4556. https://doi.org/10.1002/ijfe.2029
Adebiyi , A. , Adediran , A. , & Ayo , C. (2014). Stock price prediction using the arima model
Ahmar, A., Singh, P., Thanh, N., Tinh, N., & Hieu, V. (2022). Prediction of bric stock price using arima, suttearima, and holt-winters. Computers Materials & Continua, 70(1), 523-534. https://doi.org/10.32604/cmc.2022.017068
Akhmetova, S. and Nevskaya, L. (2020). Hr analytics: challenges and opportunities in russian companies.. https://doi.org/10.2991/aebmr.k.200324.011
Akyildirim, E., Nguyen, D., Şensoy, A., & Sikic, M. (2021). Forecasting high‐frequency excess stock returns via data analytics and machine learning. European Financial Management, 29(1), 22-75. https://doi.org/10.1111/eufm.12345
Akyildirim, E., Nguyen, D., Şensoy, A., & Sikic, M. (2021). Forecasting high‐frequency excess stock returns via data analytics and machine learning. European Financial Management, 29(1), 22-75. https://doi.org/10.1111/eufm.12345
Alanazi, T. (2022). Marketing 5.0: an empirical investigation of its perceived effect on marketing performance. Marketing and Management of Innovations, 13(4), 55-64. https://doi.org/10.21272/mmi.2022.4-06
Amirzadeh, R., Nazari, A., Thiruvady, D., & Ee, M. (2023). Modelling determinants of cryptocurrency prices: a bayesian network approach.. https://doi.org/10.48550/arxiv.2303.16148
Aride, O. and Pàmies, M. (2019). From values to behavior: proposition of an integrating model. Sustainability, 11(21), 6170. https://doi.org/10.3390/su11216170
Aziz, M., Jasri, A., Shamsudin, M., Maskat, R., Noordin, N., & Ninggal, M. (2021). Predicting common diseases among students using decision tree (j48) classification algorithm. International Journal of Academic Research in Business and Social Sciences, 11(9). https://doi.org/10.6007/ijarbss/v11-i9/11030
Aziz, M., Jasri, A., Shamsudin, M., Maskat, R., Noordin, N., & Ninggal, M. (2021). Predicting common diseases among students using decision tree (j48) classification algorithm. International Journal of Academic Research in Business and Social Sciences, 11(9). https://doi.org/10.6007/ijarbss/v11-i9/11030
Balkan, S. and Demirkan, H. (2015). Teaching predictive model management in mis classrooms: a tutorial. Communications of the Association for Information Systems, 37. https://doi.org/10.17705/1cais.03728
Balkan, S. and Demirkan, H. (2015). Teaching predictive model management in mis classrooms: a tutorial. Communications of the Association for Information Systems, 37. https://doi.org/10.17705/1cais.03728
Balkan, S. and Demirkan, H. (2015). Teaching predictive model management in mis classrooms: a tutorial. Communications of the Association for Information Systems, 37. https://doi.org/10.17705/1cais.03728
Balkan, S. and Demirkan, H. (2015). Teaching predictive model management in mis classrooms: a tutorial. Communications of the Association for Information Systems, 37. https://doi.org/10.17705/1cais.03728
Basu, R. (2023). Marketing analytics: the bridge between customer psychology and marketing decision‐making. Psychology and Marketing, 40(12), 2588-2611. https://doi.org/10.1002/mar.21908
Basu, R. (2023). Marketing analytics: the bridge between customer psychology and marketing decision‐making. Psychology and Marketing, 40(12), 2588-2611. https://doi.org/10.1002/mar.21908
Bradlow, E., Gangwar, M., Kopalle, P., & Voleti, S. (2017). The role of big data and predictive analytics in retailing. Journal of Retailing, 93(1), 79-95. https://doi.org/10.1016/j.jretai.2016.12.004
Brynjolfsson, E., Wang, J., & McElheran, K. (2021). The power of prediction: predictive analytics, workplace complements, and business performance. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.3849716
Cao, G., Duan, Y., & Banna, A. (2019). A dynamic capability view of marketing analytics: evidence from uk firms. Industrial Marketing Management, 76, 72-83. https://doi.org/10.1016/j.indmarman.2018.08.002
Cao, G., Duan, Y., & Banna, A. (2019). A dynamic capability view of marketing analytics: evidence from uk firms. Industrial Marketing Management, 76, 72-83. https://doi.org/10.1016/j.indmarman.2018.08.002
Cao, G., Duan, Y., & Banna, A. (2019). A dynamic capability view of marketing analytics: evidence from uk firms. Industrial Marketing Management, 76, 72-83. https://doi.org/10.1016/j.indmarman.2018.08.002
Chinnappa, T., Karunakaran, N., & Kumar, K. (2021). Customer relationship management vs consumerism: in post covid-19 period. Journal of Management Research and Analysis, 8(1), 32-34. https://doi.org/10.18231/j.jmra.2021.008
Choi, Y., & Choi, J.W. (2023). Assessing the Predictive Performance of Machine Learning in Direct Marketing Response. Int. J. E Bus. Res., 19, 1-12.
Dar, I., Rana, A., & Nasir, I. (2023). Marketing analytics theoretical development and trends: the journey of defining marketing analytics. Research Journal for Societal Issues, 5(1), 54-70. https://doi.org/10.56976/rjsi.v5i1.57
Dixit, S., Ahirwar, M., Saketnath, D., & Kaur, N. (2023). Designing an interface to forecast stock close price using arima., 879-884. https://doi.org/10.52458/978-81-955020-5-9-83
Ghose, S. (2019). Marketing analytics: methods, practice, implementation, and links to other fields. Expert Systems With Applications, 119, 456-475. https://doi.org/10.1016/j.eswa.2018.11.002
Goenandar, B. and Ariyanti, M. (2021). Analysis of demography, psychograph and behavioral aspects of telecom customers using predictive analytics to increase voice package sales. Journal of Consumer Sciences, 6(1), 1-19. https://doi.org/10.29244/jcs.6.1.1-19
Goi, C. (2021). The dark side of customer analytics: the ethics of retailing. Asian Journal of Business Ethics, 10(2), 411-423. https://doi.org/10.1007/s13520-021-00138-7
Gouvea, R., Kapelianis, D., & Montoya, M. (2016). Marketing challenges and opportunities in emerging economies: a brazilian perspective. Thunderbird International Business Review, 60(2), 193-205. https://doi.org/10.1002/tie.21840
Gupta, S. (2022). Application of predictive analytics in agriculture. Technoaretetransactions on Intelligent Data Mining and Knowledge Discovery, 2(4). https://doi.org/10.36647/ttidmkd/02.04.a001
Gupta, V., Jung, K., & Yoo, S. (2020). Exploring the power of multimodal features for predicting the popularity of social media image in a tourist destination. Multimodal Technologies and Interaction, 4(3), 64. https://doi.org/10.3390/mti4030064
Hollebeek, L. and Chen, T. (2014). Exploring positively- versus negatively-valenced brand engagement: a conceptual model. Journal of Product & Brand Management, 23(1), 62-74. https://doi.org/10.1108/jpbm-06-2013-0332
Husein, A., waruwu, F., Bara, Y., Donpril, M., & Harahap, M. (2021). Clustering algorithm for determining marketing targets based customer purchase patterns and behaviors. Sinkron, 6(1), 137-143. https://doi.org/10.33395/sinkron.v6i1.11191
Jatain, A. (2019). Temperature monitoring in home automation system using predictive analytics & iot. International Journal of Research in Advent Technology, 7(5), 89-96. https://doi.org/10.32622/ijrat.752019210
Johnson, D., Sihi, D., & Muzellec, L. (2021). Implementing big data analytics in marketing departments: mixing organic and administered approaches to increase data-driven decision making. Informatics, 8(4), 66. https://doi.org/10.3390/informatics8040066
Johnson, D., Sihi, D., & Muzellec, L. (2021). Implementing big data analytics in marketing departments: mixing organic and administered approaches to increase data-driven decision making. Informatics, 8(4), 66. https://doi.org/10.3390/informatics8040066
Kambeu, E. (2019). Trading volume as a predictor of market movement. International Journal of Finance & Banking Studies (2147-4486), 8(2), 57-69. https://doi.org/10.20525/ijfbs.v8i2.177
Keller, K. (1993). Conceptualizing, measuring, and managing customer-based brand equity. Journal of Marketing, 57(1), 1. https://doi.org/10.2307/1252054
Khalid, N., Wel, C., Mokhtaruddin, S., & Alam, S. (2018). The influence of self-congruity on purchase intention for cosmetic merchandises. International Journal of Academic Research in Business and Social Sciences, 8(4). https://doi.org/10.6007/ijarbss/v8-i4/4122
Khan, Y. (2019). Customer churn prediction using predictive analytics in telecommunication market: a review. Journal of Applied and Emerging Sciences, 9(2), 97. https://doi.org/10.36785/jaes.92315
Kotras, B. (2020). Mass personalization: predictive marketing algorithms and the reshaping of consumer knowledge. Big Data & Society, 7(2), 205395172095158. https://doi.org/10.1177/2053951720951581
Latha, C., Malepati, V., & Kolusu, S. (2020). S&p bse sensex and s&p bse it return forecasting using arima. Financial Innovation, 6(1). https://doi.org/10.1186/s40854-020-00201-5
Lee, J., Bahl, A., Black, G., Duber-Smith, D., & Vowles, N. (2016). Sustainable and non-sustainable consumer behavior in young adults. Young Consumers Insight and Ideas for Responsible Marketers, 17(1), 78-93. https://doi.org/10.1108/yc-08-2015-00548
Lubis, A. F. (2020). THE STATE DETERMINES LEGAL SYSTEM WITH INTERNATIONAL HUMAN RIGHTS INSTRUMENTS. INTERNATIONAL JOURNAL OF MULTI SCIENCE, 1(04), 87-94.
Lubis, A. F. (2020). The Competence of the Judiciary in Cases of Document Forgery Conducted by a TNI Soldier Before Joining TNI. Tabsyir: Jurnal Dakwah dan Sosial Humaniora, 1(3), 01-09.
Lv, Y. and Qin, L. (2021). Value perception impact and countermeasures analysis of new energy vehicle purchase behavior based on consumer level user review big data mining. Matec Web of Conferences, 336, 09030. https://doi.org/10.1051/matecconf/202133609030
Malter, M., Holbrook, M., Kahn, B., Parker, J., & Lehmann, D. (2020). The past, present, and future of consumer research. Marketing Letters, 31(2-3), 137-149. https://doi.org/10.1007/s11002-020-09526-8
Mangla, S., Luthra, S., Rana, N., & Dwivedi, Y. (2018). Predicting changing pattern: building model for consumer decision making in digital market. Journal of Enterprise Information Management, 31(5), 674-703. https://doi.org/10.1108/jeim-01-2018-0003
Mangla, S., Luthra, S., Rana, N., & Dwivedi, Y. (2018). Predicting changing pattern: building model for consumer decision making in digital market. Journal of Enterprise Information Management, 31(5), 674-703. https://doi.org/10.1108/jeim-01-2018-0003
Miklošík, A., Kuchta, M., Evans, N., & Žák, Š. (2019). Towards the adoption of machine learning-based analytical tools in digital marketing. Ieee Access, 7, 85705-85718. https://doi.org/10.1109/access.2019.2924425
Miklošík, A., Kuchta, M., Evans, N., & Žák, Š. (2019). Towards the adoption of machine learning-based analytical tools in digital marketing. Ieee Access, 7, 85705-85718. https://doi.org/10.1109/access.2019.2924425
Mohamed, H. and Al-Azab, M. (2021). Big data analytics in airlines: opportunities and challenges. Journal of Association of Arab Universities for Tourism and Hospitality, 0(0), 0-0. https://doi.org/10.21608/jaauth.2021.100797.1254
Müller, S. (2016). Advanced spatial analytics and management: models, methods and applications. Problems and Perspectives in Management, 14(2), 67-73. https://doi.org/10.21511/ppm.14(2).2016.07
Müller, S. (2016). Advanced spatial analytics and management: models, methods and applications. Problems and Perspectives in Management, 14(2), 67-73. https://doi.org/10.21511/ppm.14(2).2016.07
Müller, S. (2016). Advanced spatial analytics and management: models, methods and applications. Problems and Perspectives in Management, 14(2), 67-73. https://doi.org/10.21511/ppm.14(2).2016.07
Nanne, A., Antheunis, M., Lee, C., Postma, E., Wubben, S., & Noort, G. (2020). The use of computer vision to analyze brand-related user generated image content. Journal of Interactive Marketing, 50(1), 156-167. https://doi.org/10.1016/j.intmar.2019.09.003
Nyberg, H. and Pönkä, H. (2016). International sign predictability of stock returns: the role of the united states. Economic Modelling, 58, 323-338. https://doi.org/10.1016/j.econmod.2016.06.013
Odunfa, V., Fateye, T., & Adewusi, A. (2021). Application of artificial intelligence (ai) approach to african real estate market analysis opportunities and challenges. Advances in Multidisciplinary & Scientific Research Journal Publication, 29, 121-132. https://doi.org/10.22624/aims/abmic2021p9
Park, C., MacInnis, D., Priester, J., Eisingerich, A., & Iacobucci, D. (2010). Brand attachment and brand attitude strength: conceptual and empirical differentiation of two critical brand equity drivers. Journal of Marketing, 74(6), 1-17. https://doi.org/10.1509/jmkg.74.6.1
Qian, B. and Rasheed, K. (2006). Stock market prediction with multiple classifiers. Applied Intelligence, 26(1), 25-33. https://doi.org/10.1007/s10489-006-0001-7
Qiu, M. and Shen, Y. (2016). Predicting the direction of stock market index movement using an optimized artificial neural network model. Plos One, 11(5), e0155133. https://doi.org/10.1371/journal.pone.0155133
Reimsbach, D., Schiemann, F., Hahn, R., & Schmiedchen, E. (2019). In the eyes of the beholder: experimental evidence on the contested nature of materiality in sustainability reporting. Organization & Environment, 33(4), 624-651. https://doi.org/10.1177/1086026619875436
Robinson, M. and Kabari, L. (2021). Predicting foreign exchange using digital signal processing. British Journal of Computer Networking and Information Technology, 4(2), 1-11. https://doi.org/10.52589/bjcnit-sqwfnrnd
Shah, D., Isah, H., & Zulkernine, F. (2019). Stock market analysis: a review and taxonomy of prediction techniques. International Journal of Financial Studies, 7(2), 26. https://doi.org/10.3390/ijfs7020026
Shah, N., Steyerberg, E., & Kent, D. (2018). Big data and predictive analytics. Jama, 320(1), 27. https://doi.org/10.1001/jama.2018.5602
Shmueli, G. and Koppius, O. (2010). Predictive analytics in information systems research. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.1606674
Shmueli, M. (2011). Predictive analytics in information systems research. Mis Quarterly, 35(3), 553. https://doi.org/10.2307/23042796
Shu-li, W., Yau, W., Ong, T., & Chong, S. (2021). Integrated churn prediction and customer segmentation framework for telco business. Ieee Access, 9, 62118-62136. https://doi.org/10.1109/access.2021.3073776
Sirgy, M. and Su, C. (2000). Destination image, self-congruity, and travel behavior: toward an integrative model. Journal of Travel Research, 38(4), 340-352. https://doi.org/10.1177/004728750003800402
Sprott, D., Czellar, S., & Spangenberg, E. (2009). The importance of a general measure of brand engagement on market behavior: development and validation of a scale. Journal of Marketing Research, 46(1), 92-104. https://doi.org/10.1509/jmkr.46.1.92
Sudhir, K. (2001). Competitive pricing behavior in the auto market: a structural analysis. Marketing Science, 20(1), 42-60. https://doi.org/10.1287/mksc.20.1.42.10196
Vollrath, M. and Villegas, S. (2021). Avoiding digital marketing analytics myopia: revisiting the customer decision journey as a strategic marketing framework. Journal of Marketing Analytics, 10(2), 106-113. https://doi.org/10.1057/s41270-020-00098-0
Wamba, S., Gunasekaran, A., Akter, S., Ren, S., Dubey, R., & Childe, S. (2017). Big data analytics and firm performance: effects of dynamic capabilities. Journal of Business Research, 70, 356-365. https://doi.org/10.1016/j.jbusres.2016.08.009
Wang, G., Gunasekaran, A., Ngai, E., & Παπαδόπουλος, Θ. (2016). Big data analytics in logistics and supply chain management: certain investigations for research and applications. International Journal of Production Economics, 176, 98-110. https://doi.org/10.1016/j.ijpe.2016.03.014
Wedel, M. and Kannan, P. (2016). Marketing analytics for data-rich environments. Journal of Marketing, 80(6), 97-121. https://doi.org/10.1509/jm.15.0413
Wedel, M. and Kannan, P. (2016). Marketing analytics for data-rich environments. Journal of Marketing, 80(6), 97-121. https://doi.org/10.1509/jm.15.0413
Wedel, M. and Kannan, P. (2016). Marketing analytics for data-rich environments. Journal of Marketing, 80(6), 97-121. https://doi.org/10.1509/jm.15.0413
Yuan, S. and Pan, X. (2022). Nonlinear impact of market segmentation on the upgrading of chinese manufacturing enterprises. Chinese Management Studies, 17(3), 510-528. https://doi.org/10.1108/cms-08-2021-0334
Zhou, M., Chen, G., Ferreira, P., & Smith, M. (2021). Consumer behavior in the online classroom: using video analytics and machine learning to understand the consumption of video courseware. Journal of Marketing Research, 58(6), 1079-1100. https://doi.org/10.1177/00222437211042013
Downloads
Published
Issue
Section
License
Copyright (c) 2024 Ali Muhajir (Author)
![Creative Commons License](http://i.creativecommons.org/l/by-nc/4.0/88x31.png)
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.