Big Data Usage in Marketing Research

Authors

  • Shi Zheng
  • Xia Jin
  • Wen Zheng

DOI:

https://doi.org/10.54097/fbem.v5i3.2029

Keywords:

Big Data, Marketing Research, Marketing Decision Making, User Behavior Track, Data Collection, Behavior analysis.

Abstract

In the marketing field, the use of big data in research can make us understand consumer deeply. In some areas of market research, big data is already established today. The social media analytics and the use of cookie data to measure internet coverage are two prominent examples. This essay combs through relevant literatures, discusses the big data uses in the marketing research and its contribution for decision-making. It presents a revision of main concepts about marketing research, the new possibilities of use and a reflection about limitations of big data in the marketing research.

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References

Cravens, D.W. and Piercy, N.F. (2008) Marketing estratégico. 8th Edition, McGraw Hill, São Paulo.

Gobble, M.M. (2013) Big Data: The Next Big Thing in Innovation. Research-Technology Management, 56, 64-67.http://dx.doi.org/10.5437/08956308X5601005

https://www.sohu.com/a/314506582_700450

Google Shopper Marketing Agency Council (2013) Mobile In-Store Research: How Is Store Shoppers Are Using Mobile Devices, 37. http://www.marcresearch.com/pdf/Mobile_InStore_Research_Study.pdf

Volker Bosch. Big Data in Market Research: Why More Data Does Not Automatically Mean Better Information. GfK MIR / / GfK Research. Vol. 8, No. 2, 2016,56-63

Chunquan Li , Yaqiong Chen , Yuling Shang.A review of industrial big data for decision making in intelligent manufacturing. Engineering Science and Technology,an International Journal.2022,29:1-16

S. Carta, A. Medda, A. Pili, D.R. Recupero, R. Saia, Forecasting e-commerce products prices by combining an autoregressive integrated moving average (ARIMA) model and Google trends data, Future Internet 11 (1) (2019) 5,https://doi .org /10 .3390 /fi11010005.

J. Zhang, A. Simeone, P. Gu, B. Hong, Product features characterization and customers’ preferences prediction based on purchasing data, CIRP Ann. 67 (1)(2018) 149–152, https://doi .org /10 .1016 /j .cirp .2018 .04 .020.

M. Gao, G. Shi, S. Li, Online prediction of ship behavior with automatic identification system sensor data using bidirectional long short-term memory recurrent neural network, Sensors 18 (12) (2018) 4211, https://doi .org /10 .3390 /s18124211.

M. Inthachot, V. Boonjing, S. Intakosum, Artificial neural network and genetic algorithm hybrid intelligence for predicting Thai stock price index trend, Comput. Intell. Neurosci. 15 (2016) 1–8, https://doi .org /10 .1155 /2016 /3045254.

A. Goyal, S. Krishnamurthy, S. Kulkarni, R. Kumar, M. Vartak, M.A. Lanham, A solution to forecast demand using long short-term memory recurrent neural networks for time series forecasting, in: Midwest Decision Sciences Institute Conference, IEEE, 2018, pp. 1–18.

J. Wang, R. Hou, C. Wang, L. Shen, Improved v-support vector regression model based on variable selection and brain storm optimization for stock price forecasting, Appl. Soft Comput. J. 49 (2016) 164–178, https://doi .org /10 .1016 /j .asoc .2016 .07.024.

F. Yoseph, M. Heikkilä, D. Howard, Outliers identification model in point-of-sales data using enhanced normal distribution method, in: 2019 International Conference on Machine Learning and Data Engineering, IEEE, 2019, pp. 72–78.

A. Goyal, S. Krishnamurthy, S. Kulkarni, R. Kumar, M. Vartak, M.A. Lanham, A solution to forecast demand using long short-term memory recurrent neural networks for time series forecasting, in: Midwest Decision Sciences InstituteConference, IEEE, 2018, pp. 1–18.

Meng Wei;Dong Kai .Research on user behavior track and early warning system based on big data. Cyberspace Security. ,2019,10(12):98-102.

O. Schaer, N. Kourentzes, R. Fildes, Demand forecasting with user-generated online information, Int. J. Forecast. 35 (1) (2019) 197–212, https://doi .org /10 .1016 /j .ijforecast .2018 .03 .005.

KRIEGEL H. Data Science/Data Mining[J].Digital Welt, 2019,3 (1):7-8.

YuSong. Mining Association Rules in the Big Data of Guizhou Cigarette Brand (M),2017)https://kns.cnki.net/KCMS/detail/detail.aspx?dbname=CMFD202001&filename=1017195571.nh

Kori S , Zhu Y , Yamaguchi K , et al. Analysis of user's behaviour based on search intentions for information retrieval using search engines[C]// 2015 Conference on Technologies and Applications of Artificial Intelligence (TAAI). IEEE, 2015.

WANG Su-fang. Analysis of mobile user behavior in big data environment. Information Technology and Informatization, 2020,11:250-252

ZHANG WeiLI YangZHANG JiWANG Jian-Yong .A User Trajectory Identification Model with Fusion of Spatio-Temporal Behavior and Social Relation. Chinese Journal of Computers. 2021,44(11):2173-2188.

Wang Yunan. User Behavior Analysis Method Research Based on Trajectory Data. Shenyang Ligong University,2020.DOI:10.27323/d.cnki.gsgyc.2020.000462.

LinXueyun. Research on User Behavior Trajectory. Journal of Chongqing Industrial and Commercial University(Natural Science Edition).2015,32(01):78-82.DOI:10.16055/j.issn.1672-058X.2015.0001.019.

K. Gandhi, B. Schmidt, A.H. Ng, Towards data mining based decision support in manufacturing maintenance, in: Proc. CIRP Conf., 2018, pp. 261–265.

R.H. Hamilton, W.A. Sodeman, The questions we ask: Opportunities and challenges for using big data analytics to strategically manage human capital resources, Bus. Horiz. 63 (2020) 85–95, https://doi.org/10.1016/j.bushor.2019.10.001

J. Lee, J.H. Lee, Constructing efficient regional hazardous weather prediction models through big data analysis, J. Intell. Fuzzy Syst. 16 (1) (2016) 1–12,https://doi .org /10 .5391 /ijfis .2016 .16 .1.1.

K. Fang, Y. Jiang, M. Song, Customer profitability forecasting using big data analytics: a case study of the insurance industry, Comput. Ind. Eng. 101 (2016)554–564, https://doi .org /10 .1016 /j .cie .2016 .09 .011.

H. Wang, Y. Lu, C. Zhai, Latent aspect rating analysis on review text data: a rating regression approach, in: Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ACM, 2010,pp. 783–792.

K. Song, F. Li, F. Long, J. Wang, Q. Ling, Discriminative deep feature learning for semantic-based image retrieval, IEEE Access 6 (2018) 44268–44280, https://doi .org /10 .1109 ACCESS .2018 .2862464.

DuChangShun. Research on Key Technologies of Public Opinion Sentiment Analysis for Segmented Fields. Beijing Jiaotong University. 2019.

LI Rui;ZHANG Wei-bin. Application of Data Mining Technology Based on TF-IDF Algorithm and LDA Topic Model in Power Customer Complaint Text . Techniques of Automation and Applications,2018,37(11):46-50.

Dengyingfan. Research on user analysis and behavior prediction driven by big data [D]. Beijing University of Posts and Telecommunications,2020.DOI:10.26969/d.cnki.gbydu.2020.002814.

Huangpuhancong, Xiaozhaodi, YuyongZhong. Power consumer value classification and application based on entropy weight method and improved PCA clustering algorithm[J]. Modern Electronics Technique,2017,40(07):183-186.DOI:10.16652/j.issn.1004-373x.2017.07.048.

ZhuBangZhu. Evaluation and Improvement of Customer Loyalty Based on the PCA/DEA Model [J]. Journal of Wuhan University of Technology (information and Management Engineering Edition),2008(01):109-113.

S. Tanuwijaya, A. Alamsyah, M. Ariyanti, Mobile customer behaviour predictive analysis for targeting Netflix potential customer, in: 2021 9th International Conference on Information and Communication Technology, ICoICT,IEEE, 2021, pp. 348–352.

T. Trzci´ nski, P. Rokita, Predicting popularity of online videos using support vector regression, IEEE Trans. Multimed. 19 (2017) 2561–2570, https://doi .org /10 .1109 /TMM .2017.2695439.

W. Liu, Z. Duanmu, Z. Wang, End-to-end blind quality assessment of compressed videos using deep neural networks, in: ACM Multimedia, 2018,pp. 546–554.

H. Dou, W.X. Zhao, Y. Zhao, D. Dong, J.-R. Wen, E.Y. Chang, Predicting the popularity of online content with knowledge-enhanced neural networks, in:ACM KDD, 2018.

QiuHaiBin. Research on product competitor identification and sales volume prediction based on consumer UGC[D]. Jilin University,2019.

Tang Xiaobin;Dong Manru;Xu Rong. Design and Application of Text Mining Technology for CPI Prediction Based on Big Data [J]. Statistical Research,2021,38(08):146-160.DOI:10.19343/j.cnki.11-1302/c.2021.08.012.

Ouyang Mengqian;Zhou Xianbo;Zhu Junmei. Apply Internet Search Index to Predict CPI in the Big Data Era——Based on Joint Application of LASSO and Kernel Partial Least Squares [J]. Quarterly Journal of Finance,2020,14(02):112-136.

Hujianwei,Qiuhaiyan. Web crawler technology promotes the integrated development of big data and CPI investigation [N]. China Information Daily,2019-09-12(004).DOI:10.38309/n.cnki.nzgxx.2019.001360.

LiAng. Research on the influence of big data on CPI statistics and method improvement [J]. China Management Informationization,2019,22(02):193-194.

Alexandre Borba Salvador, Ana Akemi Ikeda. Big Data Usage in the Marketing Information System. Journal of Data Analysis and Information Processing, 2014, 2, 77-85

S. Kumar, K.K. Mohbey, A review on big data based parallel and distributed approaches of pattern mining, J. King. Saud. Univ. Comput. Inf. Sci. doi:10.1016/j.jksuci.2019.09.006.

T. Ahmad, H. Chen, W.A. Shah, Effective bulk energy consumption control and management for power utilities using artificial intelligence techniques under conventional and renewable energy resources, Int. J. Electr. Power Energy Syst.109 (2019) 242–258, https://doi .org /10 .1016 /j .ijepes .2019 .02 .023.

Schultz, D. (2012) Can Big Data Do It All ? Marketing News, November, 9.

Ling Tang , Jieyi Li , Hongchuan Du , Ling Li ,∗ , Jun Wu , Shouyang Wang. Big Data in Forecasting Research: A Literature Review. Big Data Research.2022,27:1-30

J. Wang, B. Zhang, Quality of environmental information disclosure and enterprise characteristics, Manag. Environ. Qual. An Int. J. 30 (2019) 963–979,https://doi .org /10 .1108 /MEQ -11 -2018 -0194.

Nunan, D. and Domenico, M.Di. (2013) Market Research and the Ethics of Big Data Market Research and the Ethics of Big Data. International Journal of Market Research, 55, 2-13.

ESOMAR. http://www.esomar.org/utilities/news-multimedia/video.php?idvideo=57C. J. Kaufman, Rocky Mountain Research Lab., Boulder, CO, private communication, May 1995.

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Published

19-10-2022

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Articles

How to Cite

Zheng, S., Jin, X., & Zheng, W. (2022). Big Data Usage in Marketing Research. Frontiers in Business, Economics and Management, 5(3), 242-248. https://doi.org/10.54097/fbem.v5i3.2029