skip to main content
10.1145/3659211.3659238acmotherconferencesArticle/Chapter ViewAbstractPublication PagesbdeimConference Proceedingsconference-collections
research-article

Retail Potential Evaluation Model Based on Business Circle Population Characteristics and Decision Tree Classification

Published: 29 May 2024 Publication History

Abstract

This study aims to evaluate the sales potential of cigarette retail customers through business circle population characteristics and decision tree classification model, and further predict the sales volume of cigarette retail customers. Firstly, this paper introduces the PCA method to construct a five-category core business circle index system and further uses decision tree binning and scoring card methods to score the sales potential of each cigarette retail customer according to the above-mentioned five-category core business circle index system. We found that by adding the retail customer potential score constructed in this paper as a prediction feature, using the XGBoost algorithm to predict the sales volume of cigarette retail customers can reduce the root mean square error (RMSE) from 5.978 to 5.154, and the accuracy rate increases from 84.37% to 89.27%. The conclusion of this paper implies that the constructed retail customer potential evaluation system and scoring system can better guide future cigarette distribution.

References

[1]
Qin Rong, Wang Jibin, Liu Jun, Wang Jue & Jiang Lengwei. 2016. “Discussion on China's Tobacco Industry Promoting "Internet Plus" Action Plan and Supply-side Structural Reform”, Market Forum, 9:64-69. https://doi.org/10.1016/j.jclepro.2017.06.093.
[2]
Deng Chao, Liu Song, Wang Ludi, Gong Qiang, Gao Lin, Zuo Shaoyan, Gu Zuyi & Liang Hailing. 2021. “A method for building an intelligent cigarette delivery model based on a deep neural network”, Tobacco Science and Technology, 54(02): 78-83. 10.16135/j.issn1002-0861.2020.0349.
[3]
Feng Zhe & Wang Zhigang. 2018. “Establishment of Cigarette Launch Decision Model Based on Support Vector Machine”, China Economic and Trade Guide (Middle), 32:103-105. https://kns.cnki.net/kcms2/article/abstract?v=3uoqIhG8C44YLTlOAiTRKibYlV5Vjs7i0-kJR0HYBJ80QN9L51zrP26XzS7o0dn2pyQp5oyR1qnxZCHoEXZwowA-TFabLvt8&uniplatform=NZKPT.
[4]
Huang Hengbo, Chen Haiyong & Guo Weibin. 2020. “Research on high-end cigarette delivery strategy based on recommendation algorithm”, China New Communications, 22(17):231-232. https://kns.cnki.net/kcms2/article/abstract?v=3uoqIhG8C44YLTlOAiTRKibYlV5Vjs7i8oRR1PAr7RxjuAJk4dHXojWA7WVm7JHxQrDVC5W4y85wnuP7swBUroS0pGfn_aug&uniplatform=NZKPT.
[5]
Berg C J, Melena A, Wittman F D, The reshaping of the e-cigarette retail environment: its evolution and public health concerns[J]. International journal of environmental research and public health, 2022, 19(14): 8518. https://doi.org/10.3390/ijerph19148518.
[6]
Wei T C, Chen H, Mo Y H. Research on intelligent cigarette purchase model based on multi-source data[C]//International Conference on Artificial Intelligence and Intelligent Information Processing (AIIIP 2022). SPIE, 2022, 12456: 344-352. https://doi.org/10.1117/12.2659384.
[7]
Jiao L, Yang H, Liu Z, Interpretable fuzzy clustering using unsupervised fuzzy decision trees[J]. Information Sciences, 2022, 611: 540-563. https://doi.org/10.1016/j.ins.2022.08.077.
[8]
Amirabadizadeh A, Nakhaee S, Mehrpour O. Risk assessment of elevated blood lead concentrations in the adult population using a decision tree approach[J]. Drug and Chemical Toxicology, 2022, 45(2): 878-885.
[9]
Chen T, Guestrin C. Xgboost: A scalable tree boosting system[C]//Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining. 2016: 785-794. https://doi.org/10.1145/2939672.2939785.
[10]
Sagi O, Rokach L. Approximating XGBoost with an interpretable decision tree[J]. Information Sciences, 2021, 572: 522-542. https://doi.org/10.1016/j.ins.2021.05.055.
[11]
Chen T, Guestrin C .XGBoost: A Scalable Tree Boosting System[J].ACM, 2016.
[12]
Lou X, Van d L S, Lloyd S .AIMBAT: A Python/Matplotlib Tool for Measuring Teleseismic Arrival Times[J].Seismological Research Letters, 2013, 84(1):págs. 85-93.
[13]
J. Demšar, Curk T, Erjavec A,et al.Orange: Data Mining Toolbox in Python[J].Journal of Machine Learning Research, 2013, 14(1):2349-235. https://www.jmlr.org/papers/volume14/demsar13a/demsar13a.
[14]
Shakeri M T, Nezami H, Nakhaee S, Assessing heavy metal burden among cigarette smokers and non-smoking individuals in Iran: cluster analysis and principal component analysis[J]. Biological trace element research, 2021: 1-9. https://doi.org/10.1007/s12011-020-02537-6.

Index Terms

  1. Retail Potential Evaluation Model Based on Business Circle Population Characteristics and Decision Tree Classification

        Recommendations

        Comments

        Information & Contributors

        Information

        Published In

        cover image ACM Other conferences
        BDEIM '23: Proceedings of the 2023 4th International Conference on Big Data Economy and Information Management
        December 2023
        917 pages
        ISBN:9798400716669
        DOI:10.1145/3659211
        Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

        Publisher

        Association for Computing Machinery

        New York, NY, United States

        Publication History

        Published: 29 May 2024

        Permissions

        Request permissions for this article.

        Check for updates

        Qualifiers

        • Research-article
        • Research
        • Refereed limited

        Conference

        BDEIM 2023

        Contributors

        Other Metrics

        Bibliometrics & Citations

        Bibliometrics

        Article Metrics

        • 0
          Total Citations
        • 11
          Total Downloads
        • Downloads (Last 12 months)11
        • Downloads (Last 6 weeks)2
        Reflects downloads up to 08 Feb 2025

        Other Metrics

        Citations

        View Options

        Login options

        View options

        PDF

        View or Download as a PDF file.

        PDF

        eReader

        View online with eReader.

        eReader

        HTML Format

        View this article in HTML Format.

        HTML Format

        Figures

        Tables

        Media

        Share

        Share

        Share this Publication link

        Share on social media