پیوندهای مفید
اخبار و اعلانات
آمار
| تعداد نشریات | 163 |
| تعداد شمارهها | 6,877 |
| تعداد مقالات | 74,134 |
| تعداد مشاهده مقاله | 137,853,426 |
| تعداد دریافت فایل اصل مقاله | 107,232,304 |
Exploring Overlooked Features of Online Touchpoints in Multitouch Attribution Models: A Qualitative Study | ||
| Journal of Information Technology Management | ||
| دوره 17، شماره 3، 2025، صفحه 89-116 اصل مقاله (1.14 M) | ||
| نوع مقاله: Research Paper | ||
| شناسه دیجیتال (DOI): 10.22059/jitm.2025.392364.4053 | ||
| نویسندگان | ||
| Roya Zaare Nahandi1؛ Amir Khanlari* 2 | ||
| 1Ph.D. Candidate, Department of Marketing, Kish International Campus, University of Tehran, Kish, Iran. | ||
| 2Associate Prof., Department of Marketing, Faculty of Business Management, College of Management, University of Tehran, Tehran, Iran. | ||
| چکیده | ||
| The challenge of allocating marketing budgets across multiple online channels is a significant issue for practitioners and continues to be a compelling area of research within the academic community. Many practitioners attribute credit to touchpoints in analyzing online users’ journeys based on intuition or by comparing existing models. Touchpoints are the interaction moments between companies and customers. Marketers monitor all data related to touchpoints throughout the customer journey and attempt to assess the impact of each advertising channel. Understanding each touchpoint is crucial for making decisions about budget allocations and setting inventory prices. Numerous studies have been conducted to categorize and analyze touchpoints. However, a detailed and comprehensive study on this topic is lacking. In this study, nine semi-structured interviews were conducted with experts and academics in the field, leading to the identification of 35 distinct touchpoint features. The features were extracted using MAXQDA software and a thematic analysis methodology. These features have been organized into five main categories: Time (9 features), Technology (6 features), Marketing (7 features), Visits (7 features), and Events (6 features). Utilizing these features allows for detailed monitoring of online user behavior, and by integrating them into attribution models, it becomes possible to make accurate predictions about conversions. | ||
| کلیدواژهها | ||
| Online touchpoints؛ Multitouch attribution models؛ Digital marketing؛ Online customer journey | ||
| مراجع | ||
|
Abhishek, V., Fader, P., & Hosanagar, K. (2012). The Long Road to Online Conversion: A model of Multi-Channel Attribution. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.2158421
Anderl, E., Becker, I., Von Wangenheim, F., & Schumann, J. H. (2016a). Mapping the customer journey: Lessons learned from graph-based online attribution modeling. International Journal of Research in Marketing, 33(3), 457–474. https://doi.org/10.1016/j.ijresmar.2016.03.001
Anderl, E., Schumann, J. H., & Kunz, W. (2016b). Helping Firms reduce complexity in multichannel online data: A new Taxonomy-Based Approach for Customer Journeys. Journal of Retailing, 92(2), 185–203. https://doi.org/10.1016/j.jretai.2015.10.001
Batra, R., & Keller, K. L. (2016). Integrating Marketing Communications: New findings, new lessons, and new ideas. Journal of Marketing, 80(6), 122–145. https://doi.org/10.1509/jm.15.0419
Baxendale, S., Macdonald, E. K., & Wilson, H. N. (2015). The impact of different touchpoints on brand consideration. Journal of Retailing, 91(2), 235–253. https://doi.org/10.1016/j.jretai.2014.12.008
Becker, I. F., Linzmajer, M., & Von Wangenheim, F. (2017). Cross-Industrial user channel preferences on the path to online purchase: homogeneous, heterogeneous, or mixed? Journal of Advertising, 46(2), 248–268. https://doi.org/10.1080/00913367.2017.1300076
Becker, L., & Rech, E. (2024). Sensorial customer experiences in online touchpoints. In Emerald Publishing Limited eBooks (pp. 19–37). https://doi.org/10.1108/978-1-83753-686-320241002
Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), 77–101. https://doi.org/10.1191/1478088706qp063oa
Cai, H., Ren, K., Zhang, W., Malialis, K., Wang, J., Yu, Y., & Guo, D. (2017). Real-Time Bidding by Reinforcement Learning in Display Advertising. Proceedings of the Tenth ACM International Conference on Web Search and Data Mining. https://doi.org/10.1145/3018661.3018702
Cambra-Fierro, J., Melero-Polo, I., Sese, F. J., & Van Doorn, J. (2017). Customer-Firm interactions and the path to profitability. Journal of Service Research, 21(2), 201–218. https://doi.org/10.1177/1094670517738369
Commerce, S. (2025, January 11). 51 ECommerce Statistics in 2025 (Global and U.S. data) - SellersCommerce. SellersCommerce. https://www.sellerscommerce.com/blog/ecommerce-statistics/
De Haan, E., Wiesel, T., & Pauwels, K. (2016). The effectiveness of different forms of online advertising for purchase conversion in a multiple-channel attribution framework. International Journal of Research in Marketing, 33(3), 491–507. https://doi.org/10.1016/j.ijresmar.2015.12.001
De Keyser, A., Verleye, K., Lemon, K. N., Keiningham, T. L., & Klaus, P. (2020). Moving the customer experience field forward: introducing the Touchpoints, Context, Qualities (TCQ) nomenclature. Journal of Service Research, 23(4), 433–455. https://doi.org/10.1177/1094670520928390
Dinner, I. M., Heerde Van, H. J., & Neslin, S. A. (2014). Driving Online and Offline Sales: The Cross-Channel Effects of Traditional, Online Display, and Paid Search Advertising. Journal of Marketing Research, 51(5), 527-545. https://doi.org/10.1509/jmr.11.0466
Fernández, L. (2020). Applications of Multi-Touch Attribution Modelling [Marster’s Thesis, Univesidad Torcuato di Tella], Buenos Aires.
Flick, U. (2018). Doing grounded theory. SAGE Publications Ltd.
Gao, Y., & Qiao, M. (2025). Reach measurement, optimization and frequency capping in targeted online advertising under K-Anonymity. arXiv.org. https://arxiv.org/abs/2501.04882
Gentile, C., Spiller, N., & Noci, G. (2007). How to sustain the customer experience: European Management Journal, 25(5), 395–410. https://doi.org/10.1016/j.emj.2007.08.005
Gupta, S., Leszkiewicz, A., Kumar, V., Bijmolt, T., & Potapov, D. (2020). Digital Analytics: modeling for insights and new methods. Journal of Interactive Marketing, 51(1), 26–43. https://doi.org/10.1016/j.intmar.2020.04.003
Hallikainen, H., Alamäki, A., & Laukkanen, T. (2018). Individual preferences of digital touchpoints: A latent class analysis. Journal of Retailing and Consumer Services, 50, 386–393. https://doi.org/10.1016/j.jretconser.2018.07.014
Hu, L., & Olivieri, M. (2020). Social media management in the traveller’s customer journey: an analysis of the hospitality sector. Current Issues in Tourism, 24(12), 1768–1779. https://doi.org/10.1080/13683500.2020.1819969
Johnson, P. and Clark, M. (2006) ‘Editors’ introduction: Mapping the terrain: An overview of business and management research methodologies’, in P. Johnson and M. Clark (eds) Business and Management Research Methodologies. London: Sage, pp. xxv–lv.
Kaur, S., & Gupta, S. K. (2020). A fuzzy-based framework for evaluation of website design quality index. International Journal on Digital Libraries, 22(1), 15–47. https://doi.org/10.1007/s00799-020-00292-6
Kincl, T., & Štrach, P. (2010). Measuring website quality: asymmetric effect of user satisfaction. Behaviour and Information Technology, 31(7), 647–657. https://doi.org/10.1080/0144929x.2010.526150
King, N., Brooks, J., & Horrocks, C. (2018). Interviews in qualitative research. King, Nigel - Brooks, Joanna - Horrocks, Christine - SAGE Publications Ltd - Torrossa. https://www.torrossa.com/en/resources/an/5019272
Klapdor, S., Anderl, E., Schumann, J. H., & Wangenheim, F. V. (2015). How to Use Multichannel Behavior to Predict online Conversions: Behavior Patterns across online Channels inform strategies for Turning Users into Paying Customers. Journal of Advertising Research, 55(4), 433–442. https://doi.org/10.2501/JAR-2015-024
Kranzbühler, A., Kleijnen, M. H. P., & Verlegh, P. W. J. (2018). Outsourcing the pain, keeping the pleasure: effects of outsourced touchpoints in the customer journey. Journal of the Academy of Marketing Science, 47(2), 308–327. https://doi.org/10.1007/s11747-018-0594-5
Kuehnl, C., Jozic, D., & Homburg, C. (2019). Effective customer journey design: consumers’ conception, measurement, and consequences. Journal of the Academy of Marketing Science, 47(3), 551–568. https://doi.org/10.1007/s11747-018-00625-7
Leguina, J. R., Rumin, Á. C., & Rumin, R. C. (2021). Optimizing the Frequency Capping: A robust and reliable methodology to define the number of ads to maximize ROAS. Applied Sciences, 11(15), 6688. https://doi.org/10.3390/app11156688
Lemon, K. N., & Verhoef, P. C. (2016). Understanding customer experience throughout the customer journey. Journal of Marketing, 80(6), 69–96. https://doi.org/10.1509/jm.15.0420
Li, H., & Kannan, P. (2014). Attributing conversions in a multichannel online marketing environment: an empirical model and a field experiment. Journal of Marketing Research, 51(1), 40–56. https://doi.org/10.1509/jmr.13.0050
Li, H., Kannan, P. K., Viswanathan, S., & Pani, A. (2016). Attribution strategies and return on keyword investment in paid search advertising. Marketing Science, 35(6), 831–848. https://doi.org/10.1287/mksc.2016.0987
Machado, M. R., & Karray, S. (2022). Applying hybrid machine learning algorithms to assess customer risk-adjusted revenue in the financial industry. Electronic Commerce Research and Applications, 56, 101202. https://doi.org/10.1016/j.elerap.2022.101202
Madhavi, C., & Frow, P. (2017). Customer experience and journey: emerging aspects. International Journal of Managerial Studies and Research, 5(10). https://doi.org/10.20431/2349-0349.0510003
Manchanda, P., Dubé, J., Goh, K. Y., & Chintagunta, P. K. (2006). The effect of banner advertising on internet purchasing. Journal of Marketing Research, 43(1), 98–108. https://doi.org/10.1509/jmkr.43.1.98
Matoulek, M. (2018). Data analytical way to identify an appropriate attribution model for digital marketing. Master’s thesis, Ceské vysoké ucení technické v Praze. Vypoˇ cetní a informacní centrum.
Maxwell, J. A. & Miller, B. A. (2008). Categorizing and connecting strategies in qualitative data analysis. In S. N. Hesse-Biber & P. Leavy (Eds.), Handbook of emergent methods (pp. 461–477). Guilford Press.
Miralles-Pechuán, L., Ponce, H., & Martínez-Villaseñor, L. (2020). A 2020 perspective on “A novel methodology for optimizing display advertising campaigns using genetic algorithms.” Electronic Commerce Research and Applications, 40, 100953. https://doi.org/10.1016/j.elerap.2020.100953
Mrad, A. B., & Hnich, B. (2024). Intelligent attribution modeling for enhanced digital marketing performance. Intelligent Systems With Applications, 21, 200337. https://doi.org/10.1016/j.iswa.2024.200337
Neslin, S. A., Grewal, D., Leghorn, R., Shankar, V., Teerling, M. L., Thomas, J. S., & Verhoef, P. C. (2006). Challenges and opportunities in multichannel customer management. Journal of Service Research, 9(2), 95–112. https://doi.org/10.1177/1094670506293559
Pantano, E., & Gandini, A. (2017). Exploring the forms of sociality mediated by innovative technologies in retail settings. Computers in Human Behavior, 77, 367–373. https://doi.org/10.1016/j.chb.2017.02.036
Pattanayak, S., Pati, P. B., & Singh, T. (2022). Performance analysis of machine learning algorithms on Multi-Touch Attribution Model. 2022 3rd International Conference for Emerging Technology (INCET), 1–7. https://doi.org/10.1109/incet54531.2022.9824865
Ren, K., Fang, Y., Zhang, W., Liu, S., Li, J., Zhang, Y., Yu, Y., & Wang, J. (2018). Learning Multi-touch Conversion Attribution with Dual-attention Mechanisms for Online Advertising. Proceedings of the 27th ACM International Conference on Information and Knowledge Management - CIKM ’18. https://doi.org/10.1145/3269206.3271677
Rosenbaum, M. S., Otalora, M. L., & Ramírez, G. C. (2017). How to create a realistic customer journey map. Business Horizons, 60(1), 143–150. https://doi.org/10.1016/j.bushor.2016.09.010
Samuel van Tol, D. (2024). Evaluating the feasibility of supervised machine learning models for multi-touch attribution [Marster’s Thesis, Erasmus University Rotterdam], Netherlands.
Sands, S., Ferraro, C., Campbell, C., & Pallant, J. (2016). Segmenting multichannel consumers across search, purchase and after-sales. Journal of Retailing and Consumer Services, 33, 62–71. https://doi.org/10.1016/j.jretconser.2016.08.001
Santos, S., & Gonçalves, H. M. (2021). Touchpoints and Channels: Classifications, characteristics, and issues for future research. In Smart innovation, systems and technologies (pp. 311–323). https://doi.org/10.1007/978-981-33-4183-8_25
Saunders, M. N. K., Lewis, P., & Thornhill, A. (2016). Research methods for business students (7th ed.). Pearson Education.
Schmidt, S., & Eisend, M. (2015). Advertising Repetition: A Meta-Analysis on Effective Frequency in Advertising. Journal of Advertising, 44(4), 415–428. https://doi.org/10.1080/00913367.2015.1018460
Schweidel, D. A., Bart, Y., Inman, J. J., Stephen, A. T., Libai, B., Andrews, M., Rosario, A. B., Chae, I., Chen, Z., Kupor, D., Longoni, C., & Thomaz, F. (2022). How consumer digital signals are reshaping the customer journey. Journal of the Academy of Marketing Science, 50(6), 1257–1276. https://doi.org/10.1007/s11747-022-00839-w
Shang, J. (2023). The different types of Multi-Touch Attribution modeling [INFOGRAPHIC]. AdRoll. https://www.adroll.com/blog/the-different-types-of-multi-touch-attribution-modeling
Shankar, V., Inman, J. J., Mantrala, M., Kelley, E., & Rizley, R. (2011). Innovations in Shopper Marketing: current insights and future research issues. Journal of Retailing, 87, S29–S42. https://doi.org/10.1016/j.jretai.2011.04.007
Shao, X., & Li, L. (2011). Data-driven multi-touch attribution models. Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining - KDD ’11. https://doi.org/10.1145/2020408.2020453
Shaw, C., & Ivens, J. (2002). Building great customer experiences. In Palgrave Macmillan UK eBooks. https://doi.org/10.1057/9780230554719
Straker, K., Wrigley, C., & Rosemann, M. (2015). Typologies and touchpoints: designing multi-channel digital strategies. Journal of Research in Interactive Marketing, 9(2), 110–128. https://doi.org/10.1108/jrim-06-2014-0039
Swinscoe A (2020) Attention marketers and customer experience leaders: Here is how the coronavirus pandemic is changing customer behaviour. In: Cust. Think. https://customerthink. com/attention-marketers-and-customer-experience-leaders-here-is-how-the-coronavirus-pan demic-is-changing-customer-behaviour/. Accessed 09 Sep 2024
Tueanrat, Y., Papagiannidis, S., & Alamanos, E. (2021). Going on a journey: A review of the customer journey literature. Journal of Business Research, 125, 336–353. https://doi.org/10.1016/j.jbusres.2020.12.028
Van Nguyen, A. T., McClelland, R., & Thuan, N. H. (2022). Exploring customer experience during channel switching in omnichannel retailing context: A qualitative assessment. Journal of Retailing and Consumer Services, 64, 102803. https://doi.org/10.1016/j.jretconser.2021.102803
Verhoef, P. C., Kannan, P., & Inman, J. J. (2015). From Multi-Channel retailing to Omni-Channel retailing. Journal of Retailing, 91(2), 174–181. https://doi.org/10.1016/j.jretai.2015.02.005
Wagner, G., Schramm-Klein, H., & Steinmann, S. (2018). Online retailing across e-channels and e-channel touchpoints: Empirical studies of consumer behavior in the multichannel e-commerce environment. Journal of Business Research, 107, 256–270. https://doi.org/10.1016/j.jbusres.2018.10.048
Xu, L., Duan, J. A., & Whinston, A. (2014). Path to Purchase: a mutually exciting point process model for online advertising and conversion. Management Science, 60(6), 1392–1412. https://doi.org/10.1287/mnsc.2014.1952
Yang, D., Dyer, K., & Wang, S. (2020). Interpretable Deep learning model for online multi-touch attribution. arXiv (Cornell University). https://doi.org/10.48550/arxiv.2004.00384
Zhang, Y., Wei, Y., & Ren, J. (2014). Multi-touch Attribution in Online Advertising with Survival Theory. 2014 IEEE International Conference on Data Mining. https://doi.org/10.1109/icdm.2014.130
| ||
|
آمار تعداد مشاهده مقاله: 157 تعداد دریافت فایل اصل مقاله: 96 |
||
سامانه مدیریت نشریات علمی. قدرت گرفته از سیناوب