DOI QR코드

DOI QR Code

Fashion Brand Sales Forecasting Analysis Using ARDL Time Series Model -Focusing on Brand and Advertising Endorser's Web Search Volume, Information Amount, and Brand Promotion-

ARDL 시계열 모형을 활용한 패션 브랜드의 매출 예측 분석 -패션 브랜드와 광고모델의 웹 검색량, 정보량, 가격할인 프로모션을 중심으로-

  • Seo, Jooyeon (Dept. of Fashion Industry, Ewha Womans University) ;
  • Kim, Hyojung (Dept. of Fashion Industry, Ewha Womans University) ;
  • Park, Minjung (Dept. of Fashion Industry, Ewha Womans University)
  • 서주연 (이화여자대학교 의류산업학과) ;
  • 김효정 (이화여자대학교 의류산업학과) ;
  • 박민정 (이화여자대학교 의류산업학과)
  • Received : 2022.05.26
  • Accepted : 2022.08.02
  • Published : 2022.10.31

Abstract

Fashion companies are using a big data approach as a key strategic analysis to predict and forecast sales. This study investigated the effectiveness of the past sales, web search volume, information amount, brand promotion, and the advertising endorser on the sales forecasting model. The study conducted the autoregressive distributed lag (ARDL) time series model using the internal and external social big data of a national fashion brand. Results indicated that the brand's past sales, search volume, promotion, and amount of advertising endorser information amount significantly affected the sales forecast, whereas the brand's advertising endorser search volume and information amount did not significantly influence the sales forecast. Moreover, the brand's promotion had the highest correlation with sales forecasting. This study adds to information-searching behavior theory by measuring consumers' brand involvement. Last, this study provides digital marketers with implications for developing profitable marketing strategies on the basis of consumers' interest in the brand and advertising endorser.

Keywords

References

  1. Agmeka, F., Wathoni, R. N., & Santoso, A. S. (2019). The influence of discount framing towards brand reputation and brand image on purchase intention and actual behaviour in e-commerce. Procedia Computer Science, 161, 851-858. doi:10.1016/j.procs.2019.11.192
  2. Bandari, R., Asur, S., & Huberman, B. A. (2012). The pulse of news in social media: Forecasting popularity. Proceedings of the Sixth International AAAI Conference on Weblogs and Social Media, Ireland, 6(1), 26-33.
  3. Beheshti-Kashi, S., Karimi, H. R., Thoben, K.-D., Lutjen, M., & Teucke, M. (2015). A survey on retail sales forecasting and prediction in fashion markets. Systems Science & Control Engineering, 3(1), 154-161. doi:10.1080/21642583.2014.999389
  4. Belkin, N. J., Cool, C., Stein, A., & Thiel, U. (1995). Cases, scripts, and information-seeking strategies: On the design of interactive information retrieval systems. Expert Systems with Applications, 9(3), 379-395. doi:10.1016/0957-4174(95)00011-W
  5. Bennett, D. H. S., Anaza, N. A., & Andonova, Y. (2022). Big names and small price tags: an analysis of celebrity endorsement on consumers' perceptions of price, quality, and intent to purchase. Journal of Marketing Theory and Practice, 30(2), 154-171. doi:10.1080/10696679.2021.1896952
  6. Busu, M. (2020). Analyzing the impact of the renewable energy sources on economic growth at the EU level using an ARDL model. Mathematics, 8(8):1367. doi:10.3390/math8081367
  7. Carlson, B. D., Donavan, D. T., Deitz, G. D., Bauer, B. C., & Lala, V. (2020). A customer-focused approach to improve celebrity endorser effectiveness. Journal of Business Research, 109, 221-235. doi:10.1016/j.jbusres.2019.11.048
  8. Carlson, J. (2017). A content analysis of bonus pack promotions. Journal of Promotion Management, 23(6), 930-950. doi:10.1080/10496491.2017.1323265
  9. Chen, H. (A.)., Marmorstein, H., Tsiros, M., & Rao, A. R. (2012). When more is less: The impact of base value neglect on consumer preferences for bonus packs over price discounts. Journal of Marketing, 76(4), 64-77. doi:10.1509/jm.10.0443
  10. Cho, H. Y., Lee, K. H., Cho, K. J., & Kim, J. S. (2007). Correlation and hysteresis analysis between air and water temperatures in the coastal zone - Masan Bay. Journal of Korean Society of Coastal and Ocean Engineers, 19(3), 213-221.
  11. Cho, Y., Sohn, K., & Kwon, O. (2021). Comparison of models for stock price prediction based on keyword search volume according to the social acceptance of artificial intelligence. Journal of Intelligence and Information Systems, 27(1), 103-128. doi:10.13088/jiis.2021.27.1.103
  12. Choi, T.-M., Hui, C.-L., & Yu, Y. (2011). Intelligent time series fast forecasting for fashion sales: A research agenda. Proceedings of 2011 International Conference on Machine Learning and Cybernetics, China, 3, 1010-1014. doi:10.1109/ICMLC.2011.6016870
  13. Choi, T.-M., Hui, C.-L., Liu, N., Ng, S.-F., & Yu, Y. (2014). Fast fashion sales forecasting with limited data and time. Decision Support Systems, 59, 84-92. doi:10.1016/j.dss.2013.10.008
  14. Dai, H., Ge, L., Li, C., & Wen, Y. (2022). The interaction of discount promotion and display-related promotion on ondemand platforms. Information Systems and e-Business Management, 20(2), 285-302. doi:10.1007/s10257-021-00514-7
  15. Dangi, A., Saini, C. P., Singh, V., & Hooda, J. (2021). Customer perception, purchase intention and buying decision for branded products: measuring the role of price discounts. Journal of Revenue and Pricing Management, 20(2), 194-203. doi:10.1057/s41272-021-00300-7
  16. Deloitte. (2020). 글로벌 명품 산업 2020 [Global luxury industry 2020] [PDF document]. Deloitte. Retrieved from https://www2.deloitte.com/content/dam/Deloitte/kr/Documents/consumer-business/2020/kr_consumer_article-20201130.pdf
  17. Deng, M., & Gu, X. (2021). Information acquisition, emotion experience and behaviour intention during online shopping: an eye-tracking study. Behaviour & Information Technology, 40(7), 635-645. doi:10.1080/0144929X.2020.1713890
  18. Ekmis, M. A., Hekimoglu, M., & Atak Bulbul, B. (2017). Revenue forecasting using a feed-forward neural network and ARIMA model. Sigma Journal of Engineering and Natural Sciences, 8(2), 129-134.
  19. Ettredge, M., Gerdes, J., & Karuga, G. (2005). Using web-based search data to predict macroeconomic statistics. Communications of the ACM, 48(11), 87-92. doi:10.1145/1096000.1096010
  20. France, S. L., Shi, Y., & Kazandjian, B. (2021). Web Trends: A valuable tool for business research. Journal of Business Research, 132, 666-679. doi:10.1016/j.jbusres.2020.10.019
  21. Gao, J., Zhang, C., Wang, K., & Ba, S. (2012). Understanding online purchase decision making: The effects of unconscious thought, information quality, and information quantity. Decision Support Systems, 53(4), 772-781. doi:10.1016/j.dss.2012.05.011
  22. Gilbert, D. C., & Jackaria, N. (2002). The efficacy of sales promotions in UK supermarkets: a consumer view. International Journal of Retail & Distribution Management, 30(6), 315-322. doi:10.1108/09590550210429522
  23. Gopinath, S., Thomas, J. S., & Krishnamurthi, L. (2014). Investigating the relationship between the content of online word of mouth, advertising, and brand performance. Marketing Science, 33(2), 241-258. doi:10.1287/mksc.2013.0820
  24. Gruhl, D., Guha, R., Kumar, R., Novak, J., & Tomkins, A. (2005). The predictive power of online chatter. Proceedings of the eleventh ACM SIGKDD International conference on Knowledge discovery in data mining, USA, 78-87. doi:10.1145/1081870.1081883
  25. Guitart, I. A., & Stremersch, S. (2021). The impact of informational and emotional television ad content on online search and sales. Journal of Marketing Research, 58(2), 299-320. doi:10.1177/0022243720962505
  26. Han, K. H. (2021). Prediction of women's golf wear sales using machine learning - Focused on weather factors and days of the week -. Journal of the Korean Society of Costume, 71(1), 17-33. doi:10.7233/jksc.2021.71.1.017
  27. Hong, J., & Lee, H. (2013). A study on the sales forecast model of apparel products using meteorological factors. Journal of Commodity Science and Technology, 31(1), 109-122. doi:10.36345/kacst.2013.31.1.009
  28. Hwang, J. H. (2022). Analysis of perception on happy housing using blog mining technique. The Journal of the Korea Contents Association, 22(2), 211-223. doi:10.5392/JKCA.2022.22.02.211
  29. Jin, H., Li, G., Park, S. T., & Zhu, J. (2017). The effects of consumer characteristics on information searching behavior in wireless mobile SNS: Using SEM analysis. Wireless Personal Communications, 93(1), 81-96. doi:10.1007/s11277-016-3523-2
  30. Jun, J.-K., & Choi, Y.-S. (2011). Consumer responses to product related online blogs characterized by information sources. The e-Business Studies, 12(1), 207-227. doi:10.15719/geba12.1.201103.207
  31. Kaleem, T., Miller, D., Waddle, M. R., Stross, W., Miller, R. C., & Trifiletti, D. M. (2018). Google search trends in oncology and the impact of celebrity cancer awareness. International Journal of Radiation Oncology . Biology . Physics, 102(3S), E270-E271. doi:10.1016/j.ijrobp.2018.07.876
  32. Khan, M. K., Teng, J.-Z., Khan, M. I., & Khan, M. F. (2021). Stock market reaction to macroeconomic variables: An assessment with dynamic autoregressive distributed lag simulations. International Journal of Finance & Economics. Advance online publication. doi:10.1002/ijfe.2543
  33. Khan, Z. A., Koondhar, M. A., Khan, I., Ali, U., & Tianjun, L. (2021). Dynamic linkage between industrialization, energy consumption, carbon emission, and agricultural products export of Pakistan: an ARDL approach. Environmental Science and Pollution Research, 28(32), 43698-43710. doi:10.1007/s11356-021-13738-4
  34. Kim, E. L., & Tanford, S. (2021). The windfall gain effect: Using a surprise discount to stimulate add-on purchases. International Journal of Hospitality Management, 95:102918. doi:10.1016/j.ijhm.2021.102918
  35. Kim, H. (2022). The impact of fashion industry news on the stock price index of apparel companies: Applying LDA topic modeling and ARIMAX time series analysis (Unpublished doctoral dissertation). Ewha Womans University, Seoul.
  36. Kim, S. (2022). From portal to mega-platform. Media & Society, 30(1), 252-275. doi:10.52874/medsoc.2022.02.30.1.252
  37. Kim, Y.-W., & Kim, Y. (2021). A study on the interrelationship between sales of restaurants in Dongs and the volume of blog posts: Case of the Seoul Special City. Journal of Foodservice Management Society of Korea, 24(2), 123-146. doi: 10.47584/jfm.2021.24.2.123
  38. Kong, Q., Peng, D., Ni, Y., Jiang, X., & Wang, Z. (2021). Trade openness and economic growth quality of China: Empirical analysis using ARDL model. Finance Research Letters, 38:101488. doi:10.1016/j.frl.2020.101488
  39. Lee, G.-S., & Lee, J.-C. (2018). A classification of medical and advertising blogs using machine learning. Journal of the Korea Academia-Industrial cooperation Society, 19(11), 730-737. doi:10.5762/KAIS.2018.19.11.730
  40. Lee, K. H., & Kim, K. S. (2017). Estimation of tourism demand using ARDL model. Korean Corporation Management Review, 24(4), 59-74. doi:10.21052/KCMR/2017.24.4.03
  41. Lee, K. M., & Kim, H. (2015). A study on brand trait transference to celebrity endorser. The Journal of the Korea Contents Association, 15(12), 493-503. doi:10.5392/JKCA.2015.15.12.493
  42. Lee, M. G., & Kim, H. J. (2021). Analysis of the sales promotion strategy of online fashion shopping mall. Journal of Culture Product & Design, 64, 227-240. doi:10.18555/kicpd.2021.64.21
  43. Lee, S.-J., & Lee, S. (2017). The impact of K-Beauty search volumes on export and tourism: Based on the Google search and YouTube page view. Review of Culture and Economy, 20(2), 119-147.
  44. Lemon, K. N., & Verhoef, P. C. (2016). Understanding customer experience throughout the customer journey. Journal of Marketing, 80(6), 69-96. doi:10.1509/jm.15.0420
  45. Lim, H., Yang, H.-D., & Baek, H. (2014). An empirical study on the impact of blogs and online news on the success of film : Focusing on before and after film release. Journal of Information Technology Applications and Management, 21(4), 157-171. doi:10.21219/jitam.2014.21.4.157
  46. Lim, K. R. (2020, February 13). 칸타 "올해 글로벌 패션 시장 규모357 조" [Kanta said, "This year's global fashion market is worth 357 trillion won"]. FASHION POST. Retrieved from https://fpost.co.kr/board/bbs/board.php?bo_table=newsinne%ws&wr_id=1010,%2020.02.12
  47. Lin, L., Lee, J-G., & Ko, K. A. (2018). A longitudinal analysis of model and creative strategies in TV commercials. Advertising Research, (119), 5-41. doi:10.16914/ar.2018.119.5
  48. Liu, H., & Cheong, S. (2016). Empirical analysis on the effects of tourism revenue on the local financial self-sufficiency using the autoregressive distributed lag model. Journal of Tourism Management Research, 20(5), 227-241. doi:10.18604/tmro.2016.20.5.12
  49. Liu, W., Wei, W., Wang, M., Tang, O., & Zhou, L. (2021). Pricing decision with conspicuous customers: quick responses versus value-added services. International Journal of Production Research, 59(6), 1691-1713. doi:10.1080/00207543.2020.1724341
  50. Liu, Y., Huang, X., An, A., & Yu, X. (2007). ARSA: a sentimentaware model for predicting sales performance using blogs. Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval, Netherlands, 607-614. doi:10.1145/1277741.1277845
  51. Mathur, L. K., Mathur, I., & Rangan, N. (1997). The wealth effects associated with a celebrity endorser: The Michael Jordan phenomenon. Journal of Advertising Research, 37(3), 67-74.
  52. Matthew, O., Osabohien, R., Fagbeminiyi, F., & Fasina, A. (2018). Greenhouse gas emissions and health outcomes in Nigeria: Empirical insight from auto-regressive distribution lag technique. International Journal of Energy Economics and Policy, 8(3), 43-50.
  53. Min, J. H. (2021, March 18). "고맙다빅데이터팀장"...이랜드봄 신상 줄 대박 ["Thank you, Big Data Team Leader"... ELAND's new spring lineup is great]. The Korea Economic Daily. Retrieved from https://www.hankyung.com/economy/article/2021031896581
  54. Monchaux, S., Amadieu, F., Chevalier, A., & Marine, C. (2015). Query strategies during information searching: Effects of prior domain knowledge and complexity of the information problems to be solved. Information Processing & Management, 51(5), 557-569. doi:10.1016/j.ipm.2015.05.004
  55. Mou, J., & Benyoucef, M. (2021). Consumer behavior in social commerce: Results from a meta-analysis. Technological Forecasting and Social Change, 167:120734. doi:10.1016/j.techfore.2021.120734
  56. Murthy, V. N. R., & Okunade, A. A. (2016). Determinants of U.S. health expenditure: Evidence from autoregressive distributed lag (ARDL) approach to cointegration. Economic Modelling, 59, 67-73. doi:10.1016/j.econmod.2016.07.001
  57. Noh, S., & Shin, J. (2021). The effect of comprehensive real estate holding tax and property tax on house prices using search frequencies. The Korea Spatial Planning Review, 110, 81-93. doi:10.15793/kspr.2021.110..005
  58. Onishi, H., & Manchanda, P. (2012). Marketing activity, blogging and sales. International Journal of Research in Marketing, 29(3), 221-234. doi:10.1016/j.ijresmar.2011.11.003
  59. Park, C. Y. (2021, December 19). 몸집키우는D2C기업...M&A 시장 큰손으로 [D2C enterprise grows...with big hands in the M&A market]. Business News Korea. Retrieved from https://www.mk.co.kr/news/stock/view/2021/12/1145249/
  60. Park, C. W. (2001). An investigation on the low-knowledge consumers' utilization of country-of-origin and advertising model information: The moderating role of information processing motivation and product type. Korean Marketing Review, 16(2), 49-68.
  61. Park, H., & Suk, K. (2016). The effects of message framing on consumer preference for price cuts and bonus packs. Journal of Consumer Studies, 27(6), 1-23.
  62. Pesaran, M. H., & Shin, Y. (1999). An autoregressive distributed lag modelling approach to cointegration analysis. In S. Strom (Ed.), Econometrics and economic theory in the 20th century: The Ragnar Frish centennial symposium (pp. 371-413). New York, NY: Cambridge University Press. doi:10.1017/CCOL521633230.011
  63. Pham, Q.-H., & Kim, C. (2018). Threshold effects of changes in fiscal expenditure on real exchange rate: Panel data evidence. Journal of Korea Research Association of International Commerce, 18(1), 43-64. doi:10.29331/JKRAIC.2018.02.18.1.43
  64. Preis, T., Moat, H. S., & Stanley, H. E. (2013). Quantifying trading behavior in financial markets using Google Trends. Scientific Reports, 3:1684. doi:10.1038/srep01684
  65. Said, S. E., & Dickey, D. A. (1984). Testing for unit roots in autoregressive-moving average models of unknown order. Biometrika, 71(3), 599-607. doi:10.1093/biomet/71.3.599
  66. Seo, Y. D. (2022, February 22). 2025년 국내 빅데이터 . 분석 시장 규모 2조 8353억 전망 [Korea's big data and analysis market is expected to reach 2.8353 trillion won in 2025]. TECHWORLD. Retrieved from http://www.epnc.co.kr/news/articleView.html?idxno=220167
  67. Sharot, T., & Sunstein, C. R. (2020). How people decide what they want to know. Nature Human Behaviour, 4(1), 14-19. doi:10.1038/s41562-019-0793-1
  68. Sun, I.-S., & Park, S.-H. (2014). Prediction of information service industry & analysis of its causal relation with wholesale & retail industries using time series analysis. The e-Business Studies, 15(6), 101-120. doi:10.15719/geba.15.6.201412.101
  69. Um, N. (2020). Effects of, criteria, and reasons for employing celebrities in TV home shopping : Explorative study through in-depth interview. The Korean Journal of Advertising, 31(7), 33-51. doi:10.14377/KJA.2020.10.15.33
  70. Um, N.-H., & Seo, H.-J. (2016). Advertising practitioners' perspectives on celebrity endorsement: Exploratory study through in-depth interview. Journal of Practical Research in Advertising and Public Relations, 9(2), 49-67. doi:10.21331/jprapr.2016.9.2.003
  71. Wang, J., & Kim, H. (2020). The effects of celebrity and goods characteristics on purchase intentions of fashion vs. non-fashion goods. Korean Journal of Human Ecology, 29(6), 905-916. doi:10.5934/kjhe.2020.29.6.905
  72. Wilson, T. D. (2000). Human information behavior. Informing Science: The International Journal of an Emerging Transdiscipline, 3(2), 49-56. doi:10.28945/576
  73. Xu, Y., & Huang, J.-S. (2014). Effects of price discounts and bonus packs on online impulse buying. Social Behavior and Personality: an international journal, 42(8), 1293-1302. doi:10.2224/sbp.2014.42.8.1293
  74. Yang, Y., & Chae, Y.-J. (2004). The effect of the congruency between brand personality and advertising model image on advertisement and brand preference. The Korean Journal of Advertising, 15(1), 65-82.
  75. Yu, A. P.-I., Chuang, S.-C., Cheng, Y.-H., & Wu, Y.-C. (2020). The influence of sharing versus self-use on the preference for different types of promotional offers. Journal of Retailing and Consumer Services, 54:102026. doi:10.1016/j.jretconser.2019.102026
  76. Yun, S. (2021). The influence of continuous usage intention on the preference for promotions at the low promotional benefit level. Journal of Consumer Studies, 32(5), 149-168. doi:10.35736/JCS.32.5.7