A Retail Product Prediction Model Incorporating Machine Learning and Propensity Score Matching Method
Existing machine learning methods for retail product sales forecasting often rely on their own time series data and tend to ignore the correlation between the target retail product and other products. In this paper, we take cigarette product retail sales prediction as an example, and use the neighbo...
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| Vydané v: | 2024 7th International Conference on Computer Information Science and Application Technology (CISAT) s. 1030 - 1038 |
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12.07.2024
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| Abstract | Existing machine learning methods for retail product sales forecasting often rely on their own time series data and tend to ignore the correlation between the target retail product and other products. In this paper, we take cigarette product retail sales prediction as an example, and use the neighboring related alcohol sales data to predict cigarette product sales through the propensity score matching (PSM) method, KMeans++ clustering algorithm and XGBoost algorithm. Specifically, we use cigarette and alcohol sales data from Guangzhou, China for the years 2021-2022 as the study sample, and the results show 1) Compared with using only tobacco data and machine learning algorithms, the modeling accuracy improves from 82.79% to 91.34% by introducing neighboring alcohol sales data and combining PSM and machine learning XGBoost algorithms. 2) The machine learning XGBoost algorithm identifies successive important features of cigarette sales data: category, specification, price range, and revenue from neighboring alcohol sales (new features generated by the PSM and KMeans++ algorithms). The conclusions show that the PSM algorithm and the machine learning XGBoost algorithm can incorporate other retail products to predict target products and provide an empirical application for predicting retail sales based on associations with neighboring related products |
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| AbstractList | Existing machine learning methods for retail product sales forecasting often rely on their own time series data and tend to ignore the correlation between the target retail product and other products. In this paper, we take cigarette product retail sales prediction as an example, and use the neighboring related alcohol sales data to predict cigarette product sales through the propensity score matching (PSM) method, KMeans++ clustering algorithm and XGBoost algorithm. Specifically, we use cigarette and alcohol sales data from Guangzhou, China for the years 2021-2022 as the study sample, and the results show 1) Compared with using only tobacco data and machine learning algorithms, the modeling accuracy improves from 82.79% to 91.34% by introducing neighboring alcohol sales data and combining PSM and machine learning XGBoost algorithms. 2) The machine learning XGBoost algorithm identifies successive important features of cigarette sales data: category, specification, price range, and revenue from neighboring alcohol sales (new features generated by the PSM and KMeans++ algorithms). The conclusions show that the PSM algorithm and the machine learning XGBoost algorithm can incorporate other retail products to predict target products and provide an empirical application for predicting retail sales based on associations with neighboring related products |
| Author | Mo, Yuhua Xu, Liangben Kong, Weili Li, Yuankun Chen, Kaidi Liang, Xuexia |
| Author_xml | – sequence: 1 givenname: Kaidi surname: Chen fullname: Chen, Kaidi email: prettyckd@qq.com organization: Internet Research Center,China Tobacco Guangxi Industrial Co.,LTD,Nanning,China – sequence: 2 givenname: Xuexia surname: Liang fullname: Liang, Xuexia email: 1172619310@qq.com organization: Internet Research Center,China Tobacco Guangxi Industrial Co.,LTD,Nanning,China – sequence: 3 givenname: Yuhua surname: Mo fullname: Mo, Yuhua email: moyuhua95@gmail.com organization: Internet Research Center,China Tobacco Guangxi Industrial Co.,LTD,Nanning,China – sequence: 4 givenname: Yuankun surname: Li fullname: Li, Yuankun email: liyk@pbcsf.tsinghua.edu.cn organization: Tsinghua University,PBC School of Finance,Beijing,China – sequence: 5 givenname: Liangben surname: Xu fullname: Xu, Liangben email: 1253695578@qq.com organization: Internet Research Center,China Tobacco Guangxi Industrial Co.,LTD,Nanning,China – sequence: 6 givenname: Weili surname: Kong fullname: Kong, Weili email: kongweili1108@qq.com organization: Internet Research Center,China Tobacco Guangxi Industrial Co.,LTD,Nanning,China |
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| Snippet | Existing machine learning methods for retail product sales forecasting often rely on their own time series data and tend to ignore the correlation between the... |
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| SubjectTerms | Alcoholic beverages Analytical models Cigarette Release Strategy Clustering algorithms component Data models Decision making Decision-making Quality Heuristic algorithms KMeans++ Clustering Algorithm Machine learning Machine learning algorithms Machine Learning XGBoost Algorithm Prediction algorithms Predictive models Propensity Score Matching |
| Title | A Retail Product Prediction Model Incorporating Machine Learning and Propensity Score Matching Method |
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