Abstract
In-depth analysis of corporate marketing data is conducive to companies making sound marketing decisions in blockchain. This paper proposed a marketing data analysis method based on contour curve for the deep analysis of marketing data. Firstly, the analysis of marketing data contour, standard deviation, frequency and monotonicity is given. Then, based on the above parameter analysis, the total, mean, kurtosis and skewness of the marketing data contour are given. And the concentration degree was analyzed. Finally, the kurtosis, skewness and concentration of the marketing data were analyzed experimentally through simulation experiments. The analysis of the three abstract scales can play a more positive effect on the formulation of the sales strategy.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Guan, S., Meng, X., Li, Z., et al.: Big data study on the current situation, problems and countermeasures. J. Intell. 34(5), 98–104 (2015)
Guo, P., Wang, K., Luo, A., et al.: Computational intelligence for big data analysis: current status and future prospect. J. Softw. 26(11), 3010–3025 (2015)
Cambria, E., Wang, H., White, B.: Guest editorial: big social data analysis. Knowl.-Based Syst. 69, 1–2 (2014)
Huang, Y., Kou, G.: A kernel entropy manifold learning approach for financial data analysis. Decis. Support Syst. 64, 31–42 (2014)
Kumar, N.M.S., Eswari, T., Sampath, P., et al.: Predictive methodology for diabetic data analysis in big data. Procedia Comput. Sci. 50, 203–208 (2015)
Krzywicki, A., Wobcke, W., Kim, Y.S., et al.: Collaborative Filtering for people-to-people recommendation in online dating: data analysis and user trial. Int. J. Hum. – Comput. Stud. 76(C), 50–66 (2015)
Woods, M., Paulus, T., Atkins, D.P., et al.: Advancing qualitative research using qualitative data analysis software (QDAS) reviewing potential versus practice in published studies using ATLAS.ti and NVivo, 1994–2013. Soc. Sci. Comput. Rev. 34(5), 597–617 (2016)
Oneto, L., Bisio, F., Cambria, E., et al.: Statistical learning theory and ELM for big social data analysis. IEEE Comput. Intell. Mag. 11(3), 45–55 (2016)
Zhang, L., Zhang, Y.: Big data analysis by infinite deep neural networks. J. Comput. Res. Dev. 53(1), 68–79 (2016)
Roda, F., Musulin, E.: An ontology-based framework to support intelligent data analysis of sensor measurements. Expert Syst. Appl. 41(17), 7914–7926 (2014)
Thorp, K.R., et al.: Proximal hyperspectral sensing and data analysis approaches for field-based plant phenomics. Comput. Electron. Agric. 118(C), 225–236 (2015)
Bock, H.H., Gaul, W., Okada, A., et al.: Advances in data analysis and classification. Data Anal. Mach. Learn. Knowl. Discovery 5(4), 356–364 (2015)
Jain, A., Bhatnagar, V.: Crime data analysis using pig with hadoop. Procedia Comput. Sci. 78, 571–578 (2016)
Acknowledgment
This work was supported by Guiding Project of Quzhou Science and Technology Plan in 2018 (2018004).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Wang, Y., Li, J. (2020). Research on Marketing Data Analysis Based on Contour Curve in Blockchain. In: Zheng, Z., Dai, HN., Tang, M., Chen, X. (eds) Blockchain and Trustworthy Systems. BlockSys 2019. Communications in Computer and Information Science, vol 1156. Springer, Singapore. https://doi.org/10.1007/978-981-15-2777-7_46
Download citation
DOI: https://doi.org/10.1007/978-981-15-2777-7_46
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-2776-0
Online ISBN: 978-981-15-2777-7
eBook Packages: Computer ScienceComputer Science (R0)