Research on APP Intelligent Promotion Decision Aiding System Based on Python Data Analysis and AARRR Model
Abstract With the development of AI technology and the current situation of the epidemic, online shopping is increasingly popular all over the world. The mobile phone terminals also occupy a larger share, and the use of various APPs has also emerged rapidly in line with the trend of the times. How b...
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crioppubl:10.1088/1742-6596/1856/1/012063 2024-09-09T19:26:25+00:00 Research on APP Intelligent Promotion Decision Aiding System Based on Python Data Analysis and AARRR Model Liao, Juan Ruan, Yunfei 2021 http://dx.doi.org/10.1088/1742-6596/1856/1/012063 https://iopscience.iop.org/article/10.1088/1742-6596/1856/1/012063 https://iopscience.iop.org/article/10.1088/1742-6596/1856/1/012063/pdf unknown IOP Publishing http://creativecommons.org/licenses/by/3.0/ https://iopscience.iop.org/info/page/text-and-data-mining Journal of Physics: Conference Series volume 1856, issue 1, page 012063 ISSN 1742-6588 1742-6596 journal-article 2021 crioppubl https://doi.org/10.1088/1742-6596/1856/1/012063 2024-07-01T04:13:09Z Abstract With the development of AI technology and the current situation of the epidemic, online shopping is increasingly popular all over the world. The mobile phone terminals also occupy a larger share, and the use of various APPs has also emerged rapidly in line with the trend of the times. How big data technology is used to analyze the effective data generated by APPs? The paper conducts research from the following three aspects: (1)Finding out customer’s behavior patterns from the analyzed e-commerce data, and analyzing the reasons for customer churn rate and providing effective marketing strategies for merchants; (2) according to the time dimension, from different time dimension of customer behavior rules, find out customer purchase rules for merchants; (3) from the point of view of commodities, finding out customers’ preferences for different commodities, and give different marketing schemes for commodities with different preferences. Through the three aspects of the study, we use Python for data analysis, combined with AARRR model and traditional expert system, the arctic finally sets up an intelligent decision aid system recommended by APP, the system provides different marketing schemes for merchants. The experiment shows that through data analysis and simulation of the use of an APP, in the experimental environment, the APP can better serve customers under the same conditions, and the transaction rate has increased by 7.3%. Article in Journal/Newspaper Arctic IOP Publishing Arctic Journal of Physics: Conference Series 1856 1 012063 |
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Abstract With the development of AI technology and the current situation of the epidemic, online shopping is increasingly popular all over the world. The mobile phone terminals also occupy a larger share, and the use of various APPs has also emerged rapidly in line with the trend of the times. How big data technology is used to analyze the effective data generated by APPs? The paper conducts research from the following three aspects: (1)Finding out customer’s behavior patterns from the analyzed e-commerce data, and analyzing the reasons for customer churn rate and providing effective marketing strategies for merchants; (2) according to the time dimension, from different time dimension of customer behavior rules, find out customer purchase rules for merchants; (3) from the point of view of commodities, finding out customers’ preferences for different commodities, and give different marketing schemes for commodities with different preferences. Through the three aspects of the study, we use Python for data analysis, combined with AARRR model and traditional expert system, the arctic finally sets up an intelligent decision aid system recommended by APP, the system provides different marketing schemes for merchants. The experiment shows that through data analysis and simulation of the use of an APP, in the experimental environment, the APP can better serve customers under the same conditions, and the transaction rate has increased by 7.3%. |
format |
Article in Journal/Newspaper |
author |
Liao, Juan Ruan, Yunfei |
spellingShingle |
Liao, Juan Ruan, Yunfei Research on APP Intelligent Promotion Decision Aiding System Based on Python Data Analysis and AARRR Model |
author_facet |
Liao, Juan Ruan, Yunfei |
author_sort |
Liao, Juan |
title |
Research on APP Intelligent Promotion Decision Aiding System Based on Python Data Analysis and AARRR Model |
title_short |
Research on APP Intelligent Promotion Decision Aiding System Based on Python Data Analysis and AARRR Model |
title_full |
Research on APP Intelligent Promotion Decision Aiding System Based on Python Data Analysis and AARRR Model |
title_fullStr |
Research on APP Intelligent Promotion Decision Aiding System Based on Python Data Analysis and AARRR Model |
title_full_unstemmed |
Research on APP Intelligent Promotion Decision Aiding System Based on Python Data Analysis and AARRR Model |
title_sort |
research on app intelligent promotion decision aiding system based on python data analysis and aarrr model |
publisher |
IOP Publishing |
publishDate |
2021 |
url |
http://dx.doi.org/10.1088/1742-6596/1856/1/012063 https://iopscience.iop.org/article/10.1088/1742-6596/1856/1/012063 https://iopscience.iop.org/article/10.1088/1742-6596/1856/1/012063/pdf |
geographic |
Arctic |
geographic_facet |
Arctic |
genre |
Arctic |
genre_facet |
Arctic |
op_source |
Journal of Physics: Conference Series volume 1856, issue 1, page 012063 ISSN 1742-6588 1742-6596 |
op_rights |
http://creativecommons.org/licenses/by/3.0/ https://iopscience.iop.org/info/page/text-and-data-mining |
op_doi |
https://doi.org/10.1088/1742-6596/1856/1/012063 |
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Journal of Physics: Conference Series |
container_volume |
1856 |
container_issue |
1 |
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012063 |
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1809896031800786944 |