Modeling the Dynamic Impact of TikTok Advertising Expenditure on Skincare Product Demand: Evidence from Time Series Analysis

Inten Permata Sari; Timotius FCW Sutrisno

doi:10.37034/jems.v8i3.411

Authors

Inten Permata Sari Universitas Ciputra Surabaya
Timotius FCW Sutrisno Universitas Ciputra Surabaya

DOI:

https://doi.org/10.37034/jems.v8i3.411

Keywords:

TikTok Shop, Advertising, Time Series, Forecast, E-commerce

Abstract

This study examines the dynamic impact of TikTok advertising expenditure on skincare product demand using a time-series framework. The objective is to evaluate whether incorporating digital advertising spending as an exogenous variable improves sales forecasting accuracy on social commerce platforms. Monthly secondary data from Company X, covering shampoo and cream products sold on TikTok from October 2024 to October 2025, were analyzed. The study compares ARIMA, ARIMA with Trend, and ARIMAX models. Stationarity was tested using the Augmented Dickey–Fuller test, while model selection was based on AIC, MSE, RMSE, and MAPE. The results reveal heterogeneous demand characteristics across products. Shampoo demand shows strong persistence and relatively stable patterns, with advertising expenditure having a positive but limited incremental effect on forecasting accuracy. In contrast, cream demand is highly sensitive to advertising intensity. The ARIMAX model significantly outperforms alternative models for cream products, producing substantially lower forecast errors. These findings indicate that promotional elasticity differs across product categories. Managerially, the results suggest that promotion-driven products require tighter integration between marketing expenditure planning and operational forecasting, while habitual products may rely more on historical demand patterns. This study contributes to digital marketing and forecasting literature by empirically demonstrating the product-specific effectiveness of social media advertising within a dynamic time-series context.

References

Nurbianto, A. T., & Christian, T. F. (2024). Marketing strategies through product awareness, service quality and product quality assurance on consumer purchasing decisions. Devotion: Journal of Research and Community Service, 5(4), 461-470. https://doi.org/10.59188/devotion.v5i4.712

Zahira, Z., Harmanda, V., & Dewi, S. M. (2024). Pengaruh Iklan Tiktok terhadap Minat Beli Produk Skincare di SMK Perwira Negara. Jurnal kajian dan Penelitian Umum, 2(6), 217-226. https://doi.org/10.47861/jkpu-nalanda.v2i6.1471

Bessie, J. L., & Wie, W. E. (2024). Pengaruh social media marketing Tiktok terhadap minat beli konsumen pada produk Scarlett Whitening. Journal of Management: Small and Medium Enterprises (SMEs), 17(1), 211-225. https://doi.org/10.35508/jom.v17i1.11547

Zhang, H., Zheng, S., & Zhu, P. (2024). Why are Indonesian consumers buying on live streaming platforms? Research on consumer perceived value theory. Heliyon, 10(13). https://doi.org/10.1016/j.heliyon.2024.e33518

Zhang, C., & Li, M. (2025). The impact of social media advertising on consumer purchase decisions. Frontiers in Business, Economics and Management, 18(1), 5-10. https://doi.org/10.54097/brzpft10

Hanaysha, J. R. (2022). Impact of social media marketing features on consumer's purchase decision in the fast-food industry: Brand trust as a mediator. International journal of information management data insights, 2(2), 100102. https://doi.org/10.1016/j.jjimei.2022.100102

Andryanus, A., & Bahri, R. S. (2024). The influence of social media marketing and word of mouth on purchasing decisions for skincare products at Alfamidi. Indonesian Journal of Multidisciplinary Science, 4(3), 186-193. https://doi.org/10.55324/ijoms.v4i3.1045

Fildes, R., Ma, S., & Kolassa, S. (2022). Retail forecasting: Research and practice. International Journal of Forecasting, 38(4), 1283-1318. https://doi.org/10.1016/j.ijforecast.2019.06.004

Petropoulos, F., Apiletti, D., Assimakopoulos, V., Babai, M. Z., Barrow, D. K., Taieb, S. B., ... & Ziel, F. (2022). Forecasting: theory and practice. International Journal of forecasting, 38(3), 705-871. https://doi.org/10.1016/j.ijforecast.2021.11.001

Estherina, I. (2025). Jumlah penjual TikTok Shop by Tokopedia naik 40 persen menjelang Ramadan 2025. Tempo. Retrieved from https://www.tempo.co/ekonomi/jumlah-penjual-tiktok-shop-by-tokopedia-naik-40-persen-menjelang-ramadan-2025-1220961

Artha, M. N. G. P., & Sutrisno, T. F. C. (2024). Impact of TikTok live stream attributes on social presence and behavioral intention among Surabaya users. Jurnal Aplikasi Manajemen dan Bisnis, 10(3), 1009–1021. https://doi.org/10.17358/jabm.10.3.1009

Macías Urrego, J. A., García Pineda, V., & Montoya Restrepo, L. A. (2024). The power of social media in the decision-making of current and future professionals: a crucial analysis in the digital era. Cogent Business & Management, 11(1). https://doi.org/10.1080/23311975.2024.2421411

Febiola, F., Sutrisno, T. F., & Andrina, A. A. A. P. (2025, October). Analysis of Generation Z Behavior and Motivation in Online Shopping: Its Relation to Information Processing on Tokopedia E-commerce. In Proceeding of The International Conference on Multidisciplinary Studies (ICOMSI) (Vol. 2, No. 1).

Hyndman, R. J., & Athanasopoulos, G. (2021). Forecasting: Principles and practice (3rd ed.). OTexts.

Güler, A. K., Musa, A., Tarım, M., Saraç, O., & Göktürk, M. (2024). Forecasting Restaurant Sales with the Sensitivity of Weather Conditions and Special Days Using Facebook Prophet. Journal of Data Applications, 2, 15-30. https://doi.org/10.26650/JODA.1450459

De Livera, A. M., Hyndman, R. J., & Snyder, R. D. (2011). Forecasting time series with complex seasonal patterns using exponential smoothing. Journal of the American Statistical Association, 106(496), 1513–1527. https://doi.org/10.1198/jasa.2011.tm09771

Lim, B., & Zohren, S. (2021). Time-series forecasting with deep learning: A survey. Philosophical Transactions of the Royal Society A, 379(2194). https://doi.org/10.1098/rsta.2020.0209

Zhang, G. P. (2003). Time series forecasting using a hybrid ARIMA and neural network model. Neurocomputing, 50, 159–175. https://doi.org/10.1016/S0925-2312(01)00702-0

Gardner, E. S. (2006). Exponential smoothing: The state of the art—Part II. International Journal of Forecasting, 22(4), 637–666. https://doi.org/10.1016/j.ijforecast.2006.03.005

Makridakis, S., Hyndman, R. J., & Petropoulos, F. (2020). Forecasting in social settings: The state of the art. International Journal of Forecasting, 36(1), 15-28. https://doi.org/10.1016/j.ijforecast.2019.05.011

Kim, S., & Kim, H. (2016). A new metric of absolute percentage error for intermittent demand forecasts. International Journal of Forecasting, 32(3), 669–679. https://doi.org/10.1016/j.ijforecast.2015.12.003