Clothing sales were $2.78 billion since they comprise 20 percent of total e-commerce sales, reports eMarketer. This means online apparel retailers will only achieve $1.39 billion in total revenue since half of Americans are expecting to return clothes ordered online due to poor fit, according to a new study

Hello,

It's a rather complex problem: (1) predicting online sales which depend, in particular, on the weather and on strikes in transport; (2) managing returns from online sales, the volumes of which vary, depending on the products family, between 10 and 50% (for clothing) of the sales made.

The best way is, to my mind, to consider an approach by types of products (food, furnitures, toys, cultural, garments...). For some products families, consumers demand is almost certain while for others it's completely uncertain or random.

Best regards

Dear Ryan, you need to find out why 'returns' are taking place. It varies from product to product. Forecasting sales without returned goods would give false projections. Map the reasons for the 'returns' and try to address those issues as increasing returns would hurt sales in the long run.

This paper might be helpful:

"Demand forecasting with user-generated online information"

https://doi.org/10.1016/j.ijforecast.2018.03.005

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Hello, It's a rather complex problem: (1) predicting online sales which depend, in particular, on the weather and on strikes in transport; (2) managing returns from online sales, the volumes of which vary, depending on the products family, between 10 and 50% (for clothing) of the sales made.

I can suggest you the following link: https://medium.com/@RemiStudios/ai-demand-forecasting-applied-to-e-commerce-968c08c88e26

The best way is, to my mind, to consider an approach by types of products (food, furnitures, toys, cultural, garments...). For some products families, consumers demand is almost certain while for others it's completely uncertain or random.

Best regards

Hi. Ser the link below;

Hi. See the link below:

https://link.springer.com/article/10.1186/s13243-014-0008-x#article-info