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Cleanlab for E-Commerce and Retail

Ensure accurate information in your website, product listings, customer reviews, and internal data. Deploy more reliable ML Models and Analytics once you have more accurate information.
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Case StudyPing An Insurance

Ping An Insurance used Cleanlab in an e-commerce application to: find 10% noise in their data labels, filter the detected bad data, and more robustly train their product classifier.
10%
reduction in label noise
If the classifier is trained with these noisy images directly, its performance could be degraded. In view of this, we attempted to find label errors in the image dataset with an open source tool cleanlab, a framework powered by the theory of confident learning. Specifically, we trained multiple ResNet50 image classifiers to compute the predicted product category probabilities for all the training samples in a cross-validation manner. Then the cleanlab tool could utilize the matrix of predicted probabilities to find noisy samples, ordered by likelihood of being an error. We removed the top 10% noisy samples from the training set.
Ping An Insurance is a Chinese holding conglomerate whose subsidiaries provide insurance, banking, asset management, financial, healthcare services.
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HOW CLEANLAB CAN HELP YOUR BUSINESS

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Better estimate the true quality of product from noisy reviews. In this example, Cleanlab Studio automatically found the given label to be incorrect and suggested the correct label of "5 stars".
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Cleanlab Studio enables data-centric AI to build accurate ML models for messy real-world tabular or text data. You can effortlessly harness AutoML for various data types, including text, image, and tabular formats (Excel, CSV, Json), allowing you to focus on the most important aspect: the data. Learn more about Cleanlab:
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Cleanlab Studio scans any image dataset for common real-world issues such as images which are blurry, under/over-exposed, oddly sized, or (near) duplicates of others, enabling you to produce high quality computer vision datasets. Learn more.
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Videos on using Cleanlab Studio to find and fix incorrect labels for:
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Detect errors in product descriptions/categorizations and issues like (near) duplicate or anomalous SKUs. Learn more.


Cleanlab Studio auto-corrects raw data to ensure reliable predictions so you can maximize customer experience.

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Case Study
Automated quality assurance for product catalogs


Cleanlab Studio seamlessly handles data with image, text, and structured/tabular features (eg. product price, size, etc) to auto-detect common issues in product catalogs including:Learn more via our Tutorial
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Cleanlab Studio was used to improve an E-commerce website, product listings, and analytics. Finding and fixing errors in product descriptions/metadata can be entirely automated, and improves: customer experience, product discoverability, SEO, advertising, as well as analytics/decision-making.

Read more: Enhancing Product Analytics and E-commerce with Cleanlab Studio

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