Lamoda has implemented its own machine learning model that identifies defects between products. The tool doubled the speed of processing returns after customers tried it out, the company said.
Subscribe to RB.RU on Telegram
Now all products in the warehouse that are returned by customers are first verified through the ML model. Performs a daily score of the positions received and assigns one of three statuses:
- Everything is fine with the item, it is sent for storage, displayed and sold more.
- There may be problems with the item; A quick review by a warehouse specialist is necessary.
- There are definitely problems with the item, it is sent to quality service for inspection by a specialist. If the condition of the product no longer allows it to be sold, it will be withdrawn from circulation and sent to a charity.
The machine learning-based defect prediction model is Lamoda’s own development. To create and integrate the model, 20 million rows of historical data were used and more than 60 characteristics about the product and order were taken into account, including characteristics obtained from other ML models.
Before the implementation of the ML model, employees identified defects in clothing and shoes after manually checking each item.
- Lamoda appeared in 2011 and is now one of the largest online platforms in Russia and the CIS for the sale of products from the field of fashion, beauty and lifestyle. The site contains more than 10 million products from 4 thousand local and global brands.
- The company has been actively implementing tools using machine learning models since 2023. Thus, in March 2023, an AI service for selecting clothes appeared, in May, the possibility of receiving offers in the form of capsule images collected by AI, and in August, a virtual clothing try-on service.
Author:
Anastasia Marina
Source: RB
I am a professional journalist and content creator with extensive experience writing for news websites. I currently work as an author at Gadget Onus, where I specialize in covering hot news topics. My written pieces have been published on some of the biggest media outlets around the world, including The Guardian and BBC News.
