Online Platform Customer Shopping Repurchase Behavior Analysis
Sustainability (Switzerland), ISSN: 2071-1050, Vol: 14, Issue: 14
2022
- 1Citations
- 40Captures
- 1Mentions
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
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Sustainability, Vol. 14, Pages 8714: Online Platform Customer Shopping Repurchase Behavior Analysis
Sustainability, Vol. 14, Pages 8714: Online Platform Customer Shopping Repurchase Behavior Analysis Sustainability doi: 10.3390/su14148714 Authors: Chong Ji Wenhui Zhao Hui Wang Puyu Yuan With
Article Description
With the rapid development of the world economy and the progress of modern science and technology, e-commerce has gradually spread to the public. For the online shopping platform, the number of online stores has increased rapidly, especially so in recent years. Mastering the rules of customers’ shopping behavior will help the stores to stand out amidst such a fiercely competitive environment. Taking the cosmetics industry in online shopping as an example, this paper studies the purchase behavior of online platform customers. Through the analysis of order data, it is found that the number of customers’ repurchase times and the corresponding number of people conform to the law of power-law distribution. On this basis, the customer attributes of repurchase behavior are analyzed and demonstrated, and the influences of different factors, such as region, postage, and usage of clients, on the customer repurchase rate and the relationship between the number of orders and the number of days between repurchase are revealed. The analysis results can provide better sustainable operation decision support for online platform operators and improve the overall repurchase rate and benefits of stores.
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