Research on Optimization of Taobao Product Sales Forecast Based on ARIMA model

Chengyang Li ()
Additional contact information
Chengyang Li: Shanghai Lixin University of Accounting and Finance, School of Information Management

A chapter in Proceedings of the 2024 2nd International Conference on Digital Economy and Management Science (CDEMS 2024), 2024, pp 27-37 from Springer

Abstract: Abstract Accurate sales forecast is of great guiding significance for online e-commerce. First of all, merchants can make corresponding marketing strategies and inventory plans in advance according to the predicted sales volume of goods, so as to meet the needs of consumers and achieve profits, while avoiding the stock shortage or backlog of goods, so as to improve operational efficiency and customer satisfaction, and help merchants better understand consumer demand and market trends. By analyzing historical sales data and consumer reviews, merchants can tap into consumers’ shopping habits and preferences, as well as hot items and trends in the market. This information is very important for merchants to formulate marketing strategies and adjust product positioning, which can help merchants better meet market needs and consumer expectations.

Keywords: E-commerce; sales; ARIMA; machine learning algorithms; forecast (search for similar items in EconPapers)
Date: 2024
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:advbcp:978-94-6463-488-4_4

Ordering information: This item can be ordered from
http://www.springer.com/9789464634884

DOI: 10.2991/978-94-6463-488-4_4

Access Statistics for this chapter

More chapters in Advances in Economics, Business and Management Research from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().