Profiling consumers’ online shopping and following social media influencers behaviors
DOI:
https://doi.org/10.51359/2526-7884.2024.261052Keywords:
Consumer Profiling, Consumer Demographics, Market Segmentation, Social Media Influencer, Decision TreeAbstract
In order to develop effective digital marketing strategies, this study explores how demographic factors may affect consumers’ online shopping and following social media influencers behaviors. Market segmentation theory and consumer demographics theory provide the theoretical foundation for this study. By analyzing survey data collected from 6,034 U.S. adults with decision tree analysis, there are a number of significant findings. First, educational level, income, and age category are the important predictors for consumers’ buying things online using a desktop or laptop computer behavior. Second, age category, geographic location (urban, suburban, rural) are the predictors for consumers’ buying things online using a smartphone behavior. Third, educational level, age, and geographic location are the predictors for consumers’ preferring online shopping over in-store shopping behavior. Fourth, age category and race-ethnicity are the predictors for following social media influencers behavior. Fifth, gender and age category are the predictors for purchasing something after seeing an influencer’s posts behavior. Finally, age category and gender are the predictors for purchase decisions getting impacted by influencers. The results provide valuable insights about consumer behaviors online, market segmentation, and influencer marketing strategies.
References
Atske, S. & Perrin, A. (2021, July 16). Home broadband adoption, computer ownership vary by race, ethnicity in the U.S. Pew Research Center. https://www.pewresearch.org/short-reads/2021/07/16/home-broadband-adoption-computer-ownership-vary-by-race-ethnicity-in-the-u-s/
Baker, S. (2020, January 7). How Social Media Influencers Can Impact Your Customers' Purchase Decisions. Social Media Today. https://www.socialmediatoday.com/news/how-social-media-influencers-can-impact-your-customers-purchase-decisions/569813/
Bartikowski, B., Laroche, M., Jamal, A., & Yang, Z. (2018, January). The type-of-internet-access digital divide and the well-being of ethnic minority and majority consumers: A multi-country investigation. Journal of Business Research, 82, 373-380. https://doi.org/10.1016/j.jbusres.2017.05.033
Beatty, P., Reay, I., Dick, S., & Miller, J. (2011). Consumer trust in e-commerce web sites: A meta analysis. ACM Computing Surveys, 43(3), Article 14, 1-46, https://dl.acm.org/doi/10.1145/1922649.1922651
Dopson, E. (2023). 30+ Influencer marketing statistics you should know. Shopify. https://www.shopify.com/blog/influencer-marketing-statistics
Faverio, M., & Anderson, M. (2022, November 21). For shopping, phones are common and influencers have become a factor – especially for young adults. Pew Research Center. https://www.pewresearch.org/short-reads/2022/11/21/for-shopping-phones-are-common-and-influencers-have-become-a-factor-especially-for-young-adults/
Fietkiewicz, K. J., Lins, E., Baran, K.S., & Stock, W. G. (2016). Inter-generational comparison of social media use: Investigating the online behavior of different generational cohorts. 49th Hawaii International Conference on System Sciences (HICSS) Conference Proceeding, Koloa, HI, 3829-3838. https://doi.org/10.1109/HICSS.2016.477
Friemel, T. N. (2016). The digital divide has grown old: Determinants of a digital divide among seniors. New Media & Society, 18(2), 313-331. https://doi.org/10.1177/1461444814538648
Gatziolis, K. G., Tselikas, N.D., Moscholios. I.D. (2022). Adaptive User Profiling in E-Commerce and Administration of Public Services. Future Internet, 14(5):144. https://doi.org/10.3390/fi14050144
Hazari, S. & Sethna, B. N. (2023) A Comparison of Lifestyle Marketing and Brand Influencer Advertising for Generation Z Instagram Users. Journal of Promotion Management, 29(4), 491-534, https://doi.org/10.1080/10496491.2022.2163033
IBM (2010). Predictive modeling with IBM SPSS Modeler student guide. Course Code: 0A032. IBM.
Ketipov, R., Angelova, V., Doukovska, L., & Schnalle, R. (2023). Predicting user behavior in e-commerce using machine learning. Cybernetics and Information Technologies, 23(3), 89-11). https://doi.org/10.2478/cait-2023-0026
Kotler, P. (1989). From mass marketing to mass customization. Planning review, 17(5), 10-47.
Kotler, P. (1999). Kotler on marketing: How to create, win, and dominate markets. The Free Press.
Laudon, K. C., & Traver, C. G. (2024). E-commerce 2023: Business, technology, society (17th. Ed.). Pearson.
Martins, J. M., & Brooks, G. (2010). Teaching consumer demographics to marketing students, Popular Research Policy Review, 29, 81-92. https://doi.org/10.1007/s11113-009-9146-5
Martins, J. M., Yusuf, F., & Swanson, D. A. (2012). Consumer demographics and behaviour: Markets are people. Springer.
Maya, V. (2021). Customer profiling 101: Definition, method, examples. Customers First Academy. https://customersfirstacademy.com/customer-profiling/#:~:text=Customer%20profiling%20is%20the%20process%20of%20defining%20who,description%20of%20your%20ideal%20customer%20or%20customer%20segment.
Mohamed, S., Sethom, K., Namoun, A., Tufail, A., Kim, K-H., & Almoamari, H. (2022). Customer Profiling Using Internet of Things Based Recommendations. Sustainability, 14(18):11200. https://doi.org/10.3390/su141811200
Mothersbaugh, D. L., Hawkins, D. I., & Kleiser, S. B. (2020). Consumer behavior: Building marketing strategy (14th ed.). McGraw Hill.
Pearce, K. E. & Rice, R. E. (2013). Digital divide from access to activities: Comparing mobile and personal computer Internet users. Journal of Communication, 63, 721-744.
Pearce, K. E. & Rice, R. E. (2014). The language divide—The persistence of English proficiency as a gateway to the Internet: The cases of Armenia, Azerbaijan, and Georgia. International Journal of Communication, 8, 2834-2859.
Pew Research Center. (2022). American Trends Panel Wave 111 [Data]. https://www.pewresearch.org/internet/dataset/american-trends-panel-wave-111/
Needle, F. (2023, May 11). Benefits of influencer marketing [data + expert insight]. HubSpot Blog. https://blog.hubspot.com/marketing/benefits-of-influencer-marketing
Ntawanga, F., Calitz, A. P., & Barnard, L. (2008, October). Maintaining customer profiles in an e-commerce environment. In Proceedings of the 2008 annual research conference of the South African Institute of Computer Scientists and Information Technologists on IT research in developing countries: riding the wave of technology (pp. 169-179). https://dl.acm.org/doi/10.1145/1456659.1456679
Reinikainen, H., Munnukka, J., Maity, D. & Luoma-aho, V. (2020). You really are a great big sister’ – parasocial relationships, credibility, and the moderating role of audience comments in influencer marketing. Journal of Marketing Management, 36 (3-4), 279-298, https:/doi.org/10.1080/0267257X.2019.1708781
Rice, R. E. & Katz, J. E. (2003). Comparing internet and mobile phone usage: Digital divides of usage, adoption, and dropouts. Telecommunications Policy, 27(8/9), 597-623. https://doi.org/10.1016/S0308-5961(03)00068-5
Rice, R. E., & Pearce, K. E. (2015). Divide and diffuse: Comparing digital divide and diffusion of innovations perspectives on mobile phone adoption. Mobile Media & Communication, 3(3), 401-424. https://doi.org/10.1177/2050157915590469
Rice, R. E., Pearce, K. E., & Calderwood, K. J. (2023). W(h)ither the device divide? Changing relationships between personal computer or mobile device with online activities. Mobile Media & Communication, 11(3), 484-506. https://doi.org/10.1177/20501579221142134
Ruvio, A., & Iacobucci, D. (2023). Consumer behavior. Wiley.
Saima. & Khan, M. A. (2021) Effect of Social Media Influencer Marketing on Consumers’ Purchase Intention and the Mediating Role of Credibility, Journal of Promotion Management, 27(4), 503-523. https://www.tandfonline.com/doi/full/10.1080/10496491.2020.1851847
Santora, J. (2023, November 6). 17 influencer marketing statistics to fuel your strategy. Influencer Marketing Hub. https://influencermarketinghub.com/influencer-marketing-statistics
Sheth, J. (1977, June 1). Demographics in consumer behaviors. JAG: https://jagsheth.com/marketing-theory/demographics-in-consumer-behavior
Shieber, H. (2020, January 7). The next generation of influencer marketing: Creator collaborations and peer-to-peer platforms. Forbe. https://www.forbes.com/sites/forbesagencycouncil/2020/01/07/the-next-generation-of-influencer-marketing-creator-collaborations-and-peer-to-peer-platforms/
Skrovan, S. (2017, Feb 22). Why most shoppers still choose brick-and-mortar stores over e-commerce. Retail Dive. https://www.retaildive.com/news/why-most-shoppers-still-choose-brick-and-mortar-stores-over-e-commerce
Smith, S. M., & Albaum, G. S. (2005). Fundamentals of marketing research. Sage.
Strickland, J. (2014). Predictive analytics using R. Lulu.
Tighe, D. (2023, May 16). Consumers that would shop mostly online vs. offline worldwide 2023, by country. Statista. https://www.statista.com/statistics/1384193/mostly-online-vs-offline-shopping-worldwide/
Topal, İ. (2019). Estimation of online purchasing intentions using decision tree. Journal of Management and Economics Research, 17(4), 269-280. https://doi.org/10.11611/yead.542249
Vilkaite-Vaitone, N. (2024). From likes to sustainability: How social media influencers are changing the way we consume. Sustainability, 16(4), 1393. https://doi.org/10.3390/su16041393
Vogels, E. (2021, August 19). Some digital divides persist between rural, urban and suburban America. Pew Research Center. https://www.pewresearch.org/short-reads/2021/08/19/some-digital-divides-persist-between-rural-urban-and-suburban-america/
Wu, M. Y. (2018). Ethical Customer Relations in Electronic Commerce Environment: Dialogic Communication and Making Customers’ Expectations Real. China Media Research, 14(3), 34-43.
Wu, M. Y. (2022a). Fostering resilience: Understanding generational differences in information and communication technology (ICT) and social media use, Journal of Communication Technology, 5(2), 30-52, https://doi.org/10.51548/joctec-2022-007
Wu, M. Y. (2022b, July 19). How organizations can foster positive electronic word of mouth (eWOM) in an e-commerce environment. Northeastern University. https://inspireandinfluence.cps.northeastern.edu/2022/07/19/how-can-organizations-foster-positive-electronic-word-of-mouth-ewom-in-e-commerce-environment/
Wu, M. Y. (2022c). Looking back and moving forward: How psychological and demographic factors affect consumer behaviors amid the COVID-19 pandemic. Consumer Behavior Review, 6(1), e-254806. https://doi.org/10.51359/2526-7884.2022.254806
Wu, M. Y. (2023). Perceived risk and demographic factors on consumers’ choice in times of crisis. Consumer Behavior Review, 7(1), e-256550. https://doi.org/10.51359/2526-7884.2023.256550
Yadav, M. P., Feeroz, M., & Yadav, V. K. (2012). Mining the customer behavior using web usage mining in e-commerce, In Proceeding of the 3rd International Conference on Computing, Communication and Networking Technologies (ICCCNT'12) (pp. 1 - 5), Coimbatore, India. https://doi.org/10.1109/ICCCNT.2012.6395938
Zhao, X., Zhu, Z., Shan, M., Cao, R., & Chen, H. A. (2024). “Informers” or “entertainers”: The effect of social media influencers on consumers' green consumption. Journal of Retailing and Consumer Services, 77, 103647. https://doi.org/10.1016/j.jretconser.2023.103647
Zhou, S., Blazquez, M., McCormick, H., & Barnes, L. (2021). How social media influencers’ narrative strategies benefit cultivating influencer marketing: Tackling issues of cultural barriers, commercialised content, and sponsorship disclosure. Journal of Business Research, 134, 122-142. https://doi.org/10.1016/j.jbusres.2021.05.011
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