Garmentory is an online store that handpicks and sells emerging and contemporary fashion from 500+ indie boutiques and 3,000+ emerging designers.
Garmentory selects clothes and accessories from independent boutiques and designers, and curates them on their site. The problem with sourcing items from various outlets means product descriptions vary – many retailers include promo codes and shipping information, or they fill in product descriptions differently – so data needs to be ‘cleaned’ and adapted to Garmentory’s database.
Before, Garmentory was processing this data manually, which led to high operational costs and inefficient processes. They realized that they needed to streamline their operations, especially during high seasons, when they received around 30,000 product descriptions.
Garmentory decided that automating processes using text analysis with machine learning was the answer. They would need various customized classifiers and extractors to carry out different tasks, which would need to work fast enough to handle large amounts of data.
To fill in fields in their database and on their website using specific data within product descriptions (color, material, where it was made, etc.)
To ‘clean’ features from their product descriptions (promo codes, shipping)
The technology had to integrate with their platform and needed to be up and running within a month, just in time to handle high volumes of data during the high season.
Using MonkeyLearn’s extraction and classification models, Garmentory was able to automate data processing – extracting text and classifying data from their product descriptions.
This allowed them to speed up their client onboarding process and free up team members from manual processes, which also resulted in fewer errors.
less time spent on processing products
new products auto processed every day
faster than doing it manually
“With all MonkeyLearn changes, they [site moderators] have already cut down a third of their time… It was such a quick turnaround it was refreshing.”
Software Engineer @ Garmentory
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