Title: The role of big data-based precision marketing in firm performance

Authors: Jiatong Bao; Yi Qu; Shuo Zhao; Nan Zheng

Addresses: Dongbei University of Finance and Economics, No. 217 Jianshan Street, Shahekou District, Dalian, Liaoning, 116025, China ' Surrey International Institute, Dongbei University of Finance and Economics, No. 217 Jianshan Street, Shahekou District, Dalian, Liaoning, 116025, China ' Surrey International Institute, Dongbei University of Finance and Economics, No. 217 Jianshan Street, Shahekou District, Dalian, Liaoning, 116025, China ' Department of Management, Huddersfield Business School, University of Huddersfield, Queensgate, Huddersfield, HD1-3DH, UK

Abstract: Traditional marketing is faced with the difficulty in adapting to changes. In the new business environment, big data is the cornerstone of future business and precision marketing is the main development direction of future marketing. Mining useful data from large amounts of information and applying it to a company's precision marketing will boost the competitiveness and development of the company. This paper is intended to analyse the influence of precision marketing on the corporations' operating revenue conditions, and it takes Company A Digital Marketing as an example. To achieve this purpose, this study develops definite hypotheses based on the cases and theories of precision marketing and shows precision marketing process including data collection, label analysis, page modification and test results. This study uses decision tree regression analysis using the Python 3.7 and K-means cluster analysis by SPSS 24.0 respectively to test the assumptions. Results are revealed and discussed. Finally in the results, it can be seen that for the high value customer, the time interval from browsing products to purchasing products is short and concentrated within 200 days from the last purchase. In conclusion, precision marketing increases sales and revenue for enterprises.

Keywords: precision marketing; big data; digital marketing; customer portrait; decision tree regression analysis; K-means cluster analysis.

DOI: 10.1504/IJENTTM.2022.124910

International Journal of Entertainment Technology and Management, 2022 Vol.1 No.3, pp.246 - 271

Received: 03 Aug 2021
Accepted: 31 Mar 2022
Published online: 15 Aug 2022 *

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