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Marketing Return on Investment: A Comparative Study of Traditional and Modern Models
Subject area: Science,Engineering and Technology · Area of research: Marketing Analytics
Abstract
This article explores the comparative effectiveness of traditional attribution models versus advanced, real-time Multi-Touch Attribution (MTA) models in optimizing marketing return on investment (ROI). Traditional models, such as first-click, last-click, linear, and time-decay, are simple to implement but often fail to capture the complexity of modern customer journeys, leading to inaccurate attributions and inefficient budget allocations. In contrast, advanced MTA models use machine learning algorithms and real-time data to dynamically assign credit across multiple touchpoints, providing a more precise understanding of each interaction's role in driving conversions. The article discusses the limitations of traditional models in multi-channel and omnichannel marketing environments, highlighting how real-time MTA models overcome these challenges by leveraging cross-device tracking, personalization, and predictive capabilities. It further addresses the technical and organizational challenges of implementing advanced MTA models, including data integration, skill requirements, and compliance with privacy regulations like GDPR and CCPA. Emerging technologies, such as AI, IoT, and blockchain, are also examined for their potential to enhance the transparency, security, and accuracy of MTA models. The article concludes that while advanced MTA models offer significant improvements in ROI optimization, they also come with increased complexity and cost. Future research is recommended to focus on improving model transparency, addressing ethical challenges, and balancing hyper-personalization with data privacy. This study provides insights for marketers and data scientists on leveraging advanced attribution models to enhance marketing performance.
References
[1] Agahari, W., Ofe, H. and de Reuver, M. (2022) 'It is not (only) about privacy: How multi-party computation redefines control, trust, and risk in data sharing', Electronic Markets, 33(1), pp. 1-12. Available at: https://dx.doi.org/10.1007/s12525-022-00572-w.
[2] Alom, N.B., 2024. A comprehensive analysis of customer behavior analytics, privacy concerns, and data protection regulations in the era of big data and machine learning. International Journal of Applied Machine Learning and Computational Intelligence, 14(5), pp.21-40.
[3] Aouad, A., Elmachtoub, A.N., Ferreira, K. and McNellis, R. (2019) 'Model Trees for Personalization', SSRN Electronic Journal, 4 June, pp. 1-10. Available at: https://doi.org/10.2139/ssrn.4356088.
[4] Asztalos, M., Madari, I. and Lengyel, L. (2010) 'Towards Formal Analysis of Multi-paradigm Model Transformations', Simulation, 86(7), pp. 456-467. Available at: https://dx.doi.org/10.1177/0037549709343545.
[5] Beck, B.B., Petersen, J.A. and Venkatesan, R., 2021. Multichannel data-driven attribution models: A review and research agenda. Marketing Accountability for Marketing and Non-marketing Outcomes, 18, pp.153-189.
[6] Benczúr, A.A., Kocsis, L. and Pálovics, R., 2018. Online machine learning in big data streams. arXiv preprint arXiv:1802.05872.
[7] Berman, R. (2018) 'Beyond the Last Touch: Attribution in Online Advertising', Marketing Science, 37(5), pp. 593-612. Available at: https://dx.doi.org/10.1287/mksc.2018.1104.
[8] Bhatta, I., 2022. Optimizing Marketing Channel Attribution for B2B and B2C with Machine Learning Based Lead Scoring Model. Capitol Technology University.
[9] Bidve, V., Nair, S., Sarasu, P., Kedia, S. and Tamkhade, J. (2023) 'Efficient Ad Placement Using Data Mining and Optimization', Indonesian Journal of Electrical Engineering and Computer Science, 32(1), pp. 563-570. Available at: https://doi.org/10.11591/ijeecs.v32.i1.pp563-570.
[10] Boakye, C. and Buabeng, I. (2015) 'Introducing Teachers to PowerPoint Presentation in a Technologically Challenging Environment: Action Research', Journal of Science Education, 5(2), pp. 1-12. Available at: https://dx.doi.org/10.1007/s10956-004-3622-8.
[11] Chakraborty, B. (2007) 'Feature Selection and Classification Techniques for Multivariate Time Series', Proceedings of the International Conference on Innovative Computing, Information and Control, pp. 45-53. Available at: https://dx.doi.org/10.1109/ICICIC.2007.309.
[12] Chandra, S., Verma, S., Lim, W.M., Kumar, S. and Donthu, N. (2022) 'Personalization in personalized marketing: Trends and ways forward', Journal of Consumer Marketing, 39(4), pp. 87-95. Available at: https://dx.doi.org/10.1002/mar.21670.
[13] ChandraPrabha, S. and Lakshmi, S.K. (2023) 'Data Analysis and Machine Learning-based Modeling for Real-time Production', Scientific Temper, 14(2), pp. 102-109. Available at: https://dx.doi.org/10.58414/scientifictemper.2023.14.2.22.
[14] Chen, X., Sun, J. and Liu, H. (2021) 'Balancing web personalization and consumer privacy concerns: Mechanisms of consumer trust and reactance', Journal of Consumer Behaviour, 20(5), pp. 1127-1139. Available at: https://dx.doi.org/10.1002/CB.1947.
[15] Cui, T.H., Ghose, A., Halaburda, H., Iyengar, R., Pauwels, K., Sriram, S., Tucker, C. and Venkataraman, S., 2021. Informational challenges in omnichannel marketing: Remedies and future research. Journal of marketing, 85(1), pp.103-120.
[16] Dalessandro, B., Perlich, C., Stitelman, O. and Provost, F. (2012) 'Causally Motivated Attribution for Online Advertising', Proceedings of the 21st ACM International Conference on Information and Knowledge Management, pp. 7-15. Available at: https://dx.doi.org/10.1145/2351356.2351363.
[17] Deng, H., Zou, N., Du, M., Chen, W., Feng, G.-C. and Hu, X. (2021) 'A General Taylor Framework for Unifying and Revisiting Attribution Methods', arXiv preprint. Available at: https://arxiv.org/abs/2105.13841.
[18] Deng, J., Chen, X., Jiang, R., Song, X. and Tsang, I. (2021) 'A Multi-View Multi-Task Learning Framework for Multi-Variate Time Series Forecasting', IEEE Transactions on Knowledge and Data Engineering. Available at: https://dx.doi.org/10.1109/TKDE.2022.3218803.
[19] Desai, D., 2022. Hyper-personalization: an AI-enabled personalization for customer-centric marketing. In Adoption and Implementation of AI in Customer Relationship Management (pp. 40-53). IGI Global.
[20] Ding, N., Gao, H., Bu, H. and Ma, H. (2018) 'RADM: Real-time Anomaly Detection in Multivariate Time Series Based on Bayesian Network', 2018 IEEE International Conference on Smart Internet of Things (SmartIoT), pp. 82-89. Available at: https://dx.doi.org/10.1109/SMARTIOT.2018.00-13.
[21] Djonov, M. and Galabov, M. (2020) 'Real-time data integration AWS Infrastructure for Digital Twin', Proceedings of the 2020 International Conference on Industrial IoT, Big Data and Supply Chain (IIoTBDSC), pp. 1-6. Available at: https://dx.doi.org/10.1145/3407982.3407994.
[22] Eismann, S., Grohmann, J., Walter, J., Kistowski, J.V. and Kounev, S. (2019) 'Integrating Statistical Response Time Models in Architectural Performance Models', IEEE International Conference on Software Architecture, pp. 120-129. Available at: https://dx.doi.org/10.1109/ICSA.2019.00016.
[23] Erion, G., Janizek, J.D., Sturmfels, P., Lundberg, S.M. and Lee, S.-I. (2019) 'Learning Explainable Models Using Attribution Priors', arXiv preprint. Available at: https://arxiv.org/abs/1906.10670.
[24] Favre, C., Rougie, M., Bentayeb, F. and Boussaid, O. (2009) 'Gestion et analyse personnalisées des demandes marketing. Cas de LCL-Le Crédit Lyonnais', Ingénierie des Systèmes d'Information, 14(3), pp. 119-139. Available at: https://doi.org/10.3166/isi.14.3.119-139.
[25] Guo, W., Chang, R. and Li, B. (2019) 'Design of Real-time Data Access Scheme Based on Microservice Grid Operation', Proceedings of the 2019 International Conference on Computing and Information Systems, pp. 47-52. Available at: https://dx.doi.org/10.12783/dteees/iccis2019/31706.
[26] Ha, T. and Kim, S. (2023) 'Improving Trust in AI with Mitigating Confirmation Bias: Effects of Explanation Type and Debiasing Strategy for Decision-Making with Explainable AI', International Journal of Human-Computer Interaction, pp. 1-9. Available at: https://dx.doi.org/10.1080/10447318.2023.2285640.
[27] Halimi, A.B., Chavosh, A., Namdar, J., Espahbodi, S. and Esferjani, P.S. (2011) 'The Contribution of Personalization to Customers’ Loyalty Across the Bank Industry in Sweden', Journal of Service Management, 21(2), pp. 124-135. Available at: https://dx.doi.org/10.1108/JOSM-08-2015-0233.
[28] Herzner, W., Schlick, R., Le Guennec, A. and Martin, B. (2007) 'Model-Based Simulation of Distributed Real-time Applications', IEEE International Conference on Industrial Informatics, pp. 320-325. Available at: https://dx.doi.org/10.1109/INDIN.2007.4384909.
[29] Hosahally, S. and Zaremba, A., 2023. A decision-making characteristics framework for marketing attribution in practice: Improving empirical procedures. Journal of Digital & Social Media Marketing, 11(1), pp.89-100.
[30] Hu, J. and Zhong, N. (2005) 'Behavior-based online customer segmentation', Proceedings of the Sixth IEEE International Conference on Advanced Data Mining and Applications, pp. 230-235. Available at: https://dx.doi.org/10.1109/AMT.2005.1505294.
[31] Inoue, A. (2010) 'Marketing Communication Strategy and Marketing ROI under Cross-Media Environment', in Proceedings of the International Conference on e-Business and Information. ICEBI. Atlantis Press, pp. 10-15. Available at: https://doi.org/10.2991/ICEBI.2010.10.
[32] Jha, N., Trevisan, M., Mellia, M., Fernandez, D. and Irarrazaval, R. (2024) 'Privacy Policies and Consent Management Platforms: Growth and Users' Interactions over Time', arXiv preprint. Available at: https://dx.doi.org/10.48550/arXiv.2402.18321.
[33] Ji, W. and Wang, X., 2017, February. Additional multi-touch attribution for online advertising. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 31, No. 1).
[34] Ji, W., Wang, X. and Zhang, D., 2016, October. A probabilistic multi-touch attribution model for online advertising. In Proceedings of the 25th acm international on conference on information and knowledge management (pp. 1373-1382).
[35] Jiang, Y., Shang, J. and Yildirim, P. (2014) 'Optimizing Online Promotion Planning: A Multi-Objective, Multi-Market, Multi-Period Approach', SSRN Electronic Journal. Available at: https://dx.doi.org/10.2139/ssrn.2713049.
[36] Jiang, Z. and Liu, K. (2018) 'Real-time interpretation and optimization of time series data stream in big data', Proceedings of the 2018 International Conference on Cloud Computing and Big Data Analytics, pp. 231-238. Available at: https://dx.doi.org/10.1109/ICCCBDA.2018.8386520.
[37] Johnsen, M., 2024. AI in Digital Marketing. Walter de Gruyter GmbH & Co KG.
[38] Jordan, P.R., Mahdian, M., Vassilvitskii, S. and Vee, E. (2011) 'The Multiple Attribution Problem in Pay-Per-Conversion Advertising', Algorithmic Game Theory, 5(1), pp. 80-92. Available at: https://dx.doi.org/10.1007/978-3-642-24829-0_5.
[39] Kadyrov, T. and Ignatov, D. (2019) 'Attribution of Customers' Actions Based on Machine Learning Approach', arXiv preprint. Available at: https://doi.org/10.48550/arXiv.2311.06192.
[40] Kannan, P.K., Reinartz, W. and Verhoef, P.C., 2016. The path to purchase and attribution modeling: Introduction to special section. International Journal of Research in Marketing, 33(3), pp.449-456.
[41] Kirievsky, L. and Kirievsky, A. (2004) 'Attribution Analysis: Issues Old and New', Social Science Research Network. Available at: https://dx.doi.org/10.2139/ssrn.632943.
[42] Kumar, S., Gupta, G., Prasad, R., Chatterjee, A., Vig, L. and Shroff, G., 2020, November. Camta: Causal attention model for multi-touch attribution. In 2020 International Conference on Data Mining Workshops (ICDMW) (pp. 79-86). IEEE.
[43] Kushnarevych, A. and Kollárová, D. (2023) 'Development of Artificial Intelligence as a Breakthrough for Personalization in Marketing', 2023 IEEE Conference on Electrical and Computer Engineering, pp. 321-329. Available at: https://dx.doi.org/10.1109/ICECCME57830.2023.10252550.
[44] Laatabi, A., Bécu, N., Marilleau, N., Pignon-Mussaud, C., Amalric, M., Bertin, X., Anselme, B. and Beck, E. (2021) 'Mapping and Describing Geospatial Data to Generalize Complex Models: The Case of LittoSIM-GEN', arXiv preprint. Available at: https://arxiv.org/abs/2101.07523.
[45] Lee, G., Matsunaga, A.M., Dura-Bernal, S., Zhang, W., Lytton, W., Francis, J. and Fortes, J. (2014) 'Towards real-time communication between in vivo neurophysiological data sources and simulator-based brain biomimetic models', Computational Surgery, 1(3), pp. 1-10. Available at: https://dx.doi.org/10.1186/s40244-014-0012-3.
[46] Lemmerz, T., Herlé, S. and Blankenbach, J. (2023) 'Geostatistics on Real-Time Geodata Streams—High-Frequent Dynamic Autocorrelation with an Extended Spatiotemporal Moran’s I Index', International Journal of Geo-Information, 12(9), pp. 1-20. Available at: https://dx.doi.org/10.3390/ijgi12090350.
[47] Lenskold, J.D. (2003) Marketing ROI: The Path to Campaign, Customer, and Corporate Profitability. New York: McGraw-Hill. Available at
[48] Leonardi, G., Portinale, L., Artusio, P. and Valsania, M. (2016) 'Recommending Personalized Asset Investments through Case-Based Reasoning: The SMARTFASI System', Proceedings of the 2016 IEEE International Conference on Tools with Artificial Intelligence, pp. 126-133. Available at: https://doi.org/10.1109/ICTAI.2016.0126.
[49] Lopez, S., 2023. Optimizing Marketing ROI with Predictive Analytics: Harnessing Big Data and AI for Data-Driven Decision Making. Journal of Artificial Intelligence Research, 3(2), pp.9-36.
[50] Mansfield-Devine, S. (2008) 'Open source: does transparency lead to security?', Network Security, 9, pp. 4-9. Available at: https://dx.doi.org/10.1016/S1361-3723(08)70137-4.
[51] McGuigan, L., Sivan-Sevilla, I., Parham, P. and Shvartzshnaider, Y. (2023) 'Private attributes: The meanings and mechanisms of “privacy-preserving” adtech', New Media & Society. Available at: https://dx.doi.org/10.1177/14614448231213267.
[52] Mehta, K. and Singhal, E. (2020) 'Marketing Channel Attribution Modelling: Markov Chain Analysis', International Journal of Indian Culture and Business Management, 14(3), pp. 279-294. Available at: https://dx.doi.org/10.1504/ijicbm.2020.10027991.
[53] Méndez-Suárez, M. and Monfort, A. (2021) 'Marketing Attribution in Omnichannel Retailing', Marketing Science and Digital Analytics, pp. 275-295. Available at: https://dx.doi.org/10.1007/978-3-030-76935-2_14.
[54] Morǎrescu, I., Varma, V., Buşoniu, L. and Lasaulce, S. (2020) 'Space-time budget allocation policy design for viral marketing', Nonlinear Analysis: Hybrid Systems, 38, pp. 1-12. Available at: https://dx.doi.org/10.1016/j.nahs.2020.100899.
[55] Mrad, A.B. and Hnich, B., 2024. Intelligent attribution modeling for enhanced digital marketing performance. Intelligent Systems with Applications, 21, p.200337.
[56] Muellerschoen, R. and Caissy, M. (2004) 'Real-time data flow and product generating for GNSS', International Conference on Space Technology, pp. 83-92. Available at: https://dx.doi.org/10.4108/ICST.SIMUTOOLS2010.8676.
[57] Nijssen, E., Schepers, J. and Belanche, D. (2016) 'Why did they do it? How customers’ self-service technology introduction attributions affect the customer-provider relationship', Journal of Service Management, 23(4), pp. 360-375. Available at: https://dx.doi.org/10.1108/JOSM-08-2015-0233.
[58] Oklander, M., Oklander, T., Yashkina, O., Pedko, I., and Chaikovska, M. (2018) 'Analysis of Technological Innovations in Digital Marketing', Eastern-European Journal of Enterprise Technologies, 10(9), pp. 25-35. Available at: https://doi.org/10.15587/1729-4061.2018.143956.
[59] Olama, M., McNair, A.W., Sukumar, S. and Nutaro, J. (2014) 'A qualitative readiness-requirements assessment model for enterprise big-data infrastructure investment', Proceedings of SPIE, 9121. Available at: https://dx.doi.org/10.1117/12.2050605.
[60] Pattanayak, S., Pati, P.B. and Singh, T., 2022, May. Performance Analysis of Machine Learning Algorithms on Multi-Touch Attribution Model. In 2022 3rd International Conference for Emerging Technology (INCET) (pp. 1-7). IEEE.
[61] Peralta, B., López, M., Ruiz, J., Nicolis, O. and Caro, L. (2023) 'Uplift Modelling Applied to a Chilean Retail Company with Siamese Neural Networks', 2023 IEEE Artificial Intelligence Conference. Available at: https://dx.doi.org/10.1109/AIC57670.2023.10263841.
[62] Ponomarenko, I. and Siabro, S. (2022) 'Peculiarities of using personalized marketing in the conditions of digitalization', Scientific Journal, 12(172), pp. 102-110. Available at: https://dx.doi.org/10.32782/2224-6282/172-9.
[63] Quan, D., Yin, L. and Guo, Y. (2015) 'Enhancing the Trajectory Privacy with Laplace Mechanism', 2015 IEEE International Conference on Trust, Security and Privacy in Computing and Communications, pp. 157-164. Available at: https://dx.doi.org/10.1109/Trustcom.2015.508.
[64] Ratul, Q.E.A., Serra, E. and Cuzzocrea, A. (2021) 'Evaluating Attribution Methods in Machine Learning Interpretability', IEEE International Conference on Big Data, pp. 1-9. Available at: https://dx.doi.org/10.1109/BigData52589.2021.9671501.
[65] Roselli, D., Matthews, J.N. and Talagala, N. (2019) 'Managing Bias in AI', Proceedings of the 2019 ACM Conference on Fairness, Accountability, and Transparency, pp. 60-68. Available at: https://dx.doi.org/10.1145/3308560.3317590.
[66] Sabitha, J., 2024. AI-Driven Customer Segmentation And Personalization. Zibaldone Estudios italianos, 11(2), pp.1-20.
[67] Savadkoohi, F. (2012) 'Personalized Online Promotions: Long-Term Impacts on Customer Behavior', Journal of Consumer Marketing, 27(4), pp. 345-354. Available at: https://dx.doi.org/10.1108/JOSM-08-2015-0233.
[68] Serralunga, F., Mussati, M. and Aguirre, P. (2013) 'Model Adaptation for Real-Time Optimization in Energy Systems', Industrial & Engineering Chemistry Research, 52(47), pp. 16867-16874. Available at: https://dx.doi.org/10.1021/IE303621J.
[69] Serrano-Malebrán, J. and Arenas-Gaitán, J. (2021) 'When does personalization work on social media? a posteriori segmentation of consumers', Multimedia Tools and Applications, 80(16), pp. 23467-23489. Available at: https://dx.doi.org/10.1007/s11042-021-11303-2.
[70] Shaikh, N.I. and Prabhu, V. (2010) 'Estimating and Tracking Marketing Effectiveness Using Adaptive High Fidelity Models', Social Science Research Network. Available at: https://dx.doi.org/10.2139/ssrn.3122008.
[71] Sinha, R., Arbour, D. and Puli, A. (2022) 'Bayesian Modeling of Marketing Attribution', arXiv preprint. Available at: https://arxiv.org/abs/2205.15965.
[72] Spagnuelo, D., Ferreira, A. and Lenzini, G. (2019) 'Accomplishing Transparency within the General Data Protection Regulation', Proceedings of the 14th International Conference on Availability, Reliability and Security, pp. 114-125. Available at: https://dx.doi.org/10.5220/0007366501140125.
[73] Sreepathy, H.V., Rao, B.D., Jaysubramanian, M.K. and Rao, B.D., 2024. Data Ingestions as a Service (DIaaS): A Unified interface for Heterogeneous Data Ingestion, Transformation, and Metadata Management for Data Lake. IEEE Access.
[74] Sridhar, S., Mantrala, M., Naik, P. and Thorson, E. (2011) 'Dynamic Marketing Budgeting for Platform Firms: Theory, Evidence, and Application', Journal of Marketing Research, 48(6), pp. 929-943. Available at: https://dx.doi.org/10.1509/jmr.10.0035.
[75] Sun, Y. and Sundararajan, M. (2011) 'Axiomatic Attribution for Multilinear Functions', Proceedings of the ACM Conference on Knowledge Discovery and Data Mining, pp. 120-129. Available at: https://dx.doi.org/10.1145/1993574.1993601.
[76] Syafii, M., and Budiyanto, N. (2022) 'Penerapan Digital Marketing dengan Analisis STP (Segmenting, Targeting, Positioning)', Jurnal Ilmu Rekayasa dan Pengembangan Teknologi, 4(1), pp. 25-35. Available at: https://doi.org/10.36499/jinrpl.v4i1.5950.
[77] Takahashi, R., Yoshizumi, T., Mizuta, H., Abe, N., Kennedy, R.L., Jeffs, V.J., Shah, R. and Crites, R.H. (2014) 'Multi-period marketing-mix optimization with response spike forecasting', IBM Journal of Research and Development, 58(5), pp. 1-13. Available at: https://dx.doi.org/10.1147/JRD.2014.2337131.
[78] Tao, J., Chen, Q., Snyder, J.W., Kumar, A.S., Meisami, A. and Xue, L. (2023) 'A Graphical Point Process Framework for Understanding Removal Effects in Multi-Touch Attribution', SSRN Electronic Journal, pp. 1-17. Available at: https://dx.doi.org/10.2139/ssrn.4356088.
[79] Vanlalchhuanawmi, C. and Deb, S. (2023) 'Solar Photovoltaic Generation Forecasting using LSTM and SARIMAX model', 2023 IEEE International Conference on Intelligent Data and Application (ICIDeA), pp. 415-420. Available at: https://dx.doi.org/10.1109/ICIDeA59866.2023.10295240.
[80] Vyas, S., Tyagi, R.K., Jain, C. and Sahu, S., 2021, July. Literature review: A comparative study of real time streaming technologies and apache kafka. In 2021 Fourth International Conference on Computational Intelligence and Communication Technologies (CCICT) (pp. 146-153). IEEE.
[81] Wadle, L.-M., Martin, N. and Ziegler, D. (2019) 'Privacy and Personalization: The Trade-off between Data Disclosure and Personalization Benefit', Proceedings of the 2019 ACM Conference on Human Factors in Computing Systems, pp. 1-10. Available at: https://dx.doi.org/10.1145/3314183.3323672.
[82] Wang, T., Yang, H., Yu, H., Zhou, W., Liu, Y. and Song, H. (2019) 'A Revenue-Maximizing Bidding Strategy for Demand-Side Platforms', IEEE Access, 7, pp. 61462-61472. Available at: https://dx.doi.org/10.1109/ACCESS.2019.2919450.
[83] Xhepa, M. and Kanakala, N.S., 2022. Machine Learning Model Computation in AWS and Azure. https://www.christianbaun.de/CGC22/Skript/Team_4_Machine_Learning_Model_Computation_in_AWS_and_Azure.pdf
[84] Xiao, Y., Yin, H., Zhang, Y., Qi, H., Zhang, Y. and Liu, Z. (2021) 'A dual-stage attention-based Conv-LSTM network for spatio-temporal correlation and multivariate time series prediction', International Journal of Intelligent Systems, pp. 55-70. Available at: https://dx.doi.org/10.1002/int.22370.
[85] Yang, E., Xu, Q., Yan, D., Wang, Z. and Liu, S. (2020) 'Television Advertising Household Push and Operational Analysis', Proceedings of the 32nd Chinese Control and Decision Conference, pp. 623-627. Available at: https://dx.doi.org/10.1109/CCDC49329.2020.9164595.
[86] Yang, X., Jiang, X., Jiang, C. and Xu, L. (2021) 'Real-Time Modeling of Regional Tropospheric Delay Based on Multicore Support Vector Machine', Mathematical Problems in Engineering, pp. 1-10. Available at: https://dx.doi.org/10.1155/2021/7468963.
[87] Yip, W. and Marlin, T. (2004) 'The effect of model fidelity on real-time optimization performance', Computer Aided Chemical Engineering, 12, pp. 87-94. Available at: https://dx.doi.org/10.1016/s0098-1354(03)00164-9.
[88] Yuvaraj, C.B., Chandavarkar, B.R., Kumar, V.S. and Sandeep, B.S., 2018, August. Enhanced last-touch interaction attribution model in online advertising. In 2018 IEEE Distributed Computing, VLSI, Electrical Circuits and Robotics (DISCOVER) (pp. 110-114). IEEE.
[89] Zhang, H., Li, S., Chen, Y., Dai, J. and Yi, Y. (2022) 'A Novel Encoder-Decoder Model for Multivariate Time Series Forecasting', Computational Intelligence and Neuroscience, 2022, pp. 1-12. Available at: https://dx.doi.org/10.1155/2022/5596676.
[90] Zhang, X., Zhang, B., Chen, W. and Xu, J. (2010) 'Research on real-time data stream integration system for process industry', Proceedings of the 2010 International Conference on Computer and Communication Technologies in Agriculture Engineering, pp. 211-216. Available at: https://dx.doi.org/10.1109/CCTAE.2010.5543622.
[91] Zhang, Y., Wei, Y. and Ren, J. (2014) 'Multi-touch Attribution in Online Advertising with Survival Theory', 2014 IEEE International Conference on Data Mining (ICDM), 14 December, pp. 130-137. Available at: https://dx.doi.org/10.1109/ICDM.2014.130.
[92] Zhou, Q., Wang, S. and Yin, Z. (2014) 'On the Data Mining Technology Applied to Active Marketing Model of International Luxury Marketing Strategy in China-An Empirical Analysis', TELKOMNIKA, 12(2), pp. 438-444. Available at: https://dx.doi.org/10.11591/TELKOMNIKA.V12I2.4384.
How to cite this paper
@article{1706549,
author = {Nigel Nkomo, Munashe Naphtali Mupa},
title = {Marketing Return on Investment: A Comparative Study of Traditional and Modern Models},
journal = {Iconic Research And Engineering Journals},
year = {2024},
volume = {8},
number = {5},
pages = {453-473},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1706549.pdf},
abstract = {This article explores the comparative effectiveness of traditional attribution models versus advanced, real-time Multi-Touch Attribution (MTA) models in optimizing marketing return on investment (ROI). Traditional models, such as first-click, last-click, linear, and time-decay, are simple to implement but often fail to capture the complexity of modern customer journeys, leading to inaccurate attributions and inefficient budget allocations. In contrast, advanced MTA models use machine learning algorithms and real-time data to dynamically assign credit across multiple touchpoints, providing a more precise understanding of each interaction's role in driving conversions. The article discusses the limitations of traditional models in multi-channel and omnichannel marketing environments, highlighting how real-time MTA models overcome these challenges by leveraging cross-device tracking, personalization, and predictive capabilities. It further addresses the technical and organizational challenges of implementing advanced MTA models, including data integration, skill requirements, and compliance with privacy regulations like GDPR and CCPA. Emerging technologies, such as AI, IoT, and blockchain, are also examined for their potential to enhance the transparency, security, and accuracy of MTA models. The article concludes that while advanced MTA models offer significant improvements in ROI optimization, they also come with increased complexity and cost. Future research is recommended to focus on improving model transparency, addressing ethical challenges, and balancing hyper-personalization with data privacy. This study provides insights for marketers and data scientists on leveraging advanced attribution models to enhance marketing performance.},
month = {November},
}