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May 10, 2023
Unless you’ve lived under a rock, you’ve probably heard the buzz about artificial intelligence (AI) improving efficiencies across almost every industry. Regarding product development and e-commerce, the idea of having a tool that helps manage massive amounts of product information is tough to resist. Your product descriptions call for effective data validation to help streamline the time-consuming product management process.
The good news is that AI can significantly improve the accuracy and efficiency of data validation – as long as the technology is used with human oversight. This powerful combination of your team and AI confirms accuracy and reliability by efficiently collecting up to date, detailed product data using the right PIM. Here we explain three ways Pimberly AI improves the data validation process enabling you to create reliable, compliant data and alleviate your product information management headaches.
Pimberly AI enables you to enforce rules and constraints on product data input, validate product data, and ensure it is error-free and consistent. Your predefined criteria make it easier to manage massive amounts of product details using customized validation rules such as:
You choose the rules, whether it is based on your own e-commerce platform, cataloging requirements, or specific channels and marketplaces. Rule-based validation is the best way to avoid errors, duplication, mismatched images in product descriptions, improper formatting by marketplace, and more. You can improve SEO for search engines as well as customer product searches on your eCommerce platform.
See just how technical your rules can be
Whitepaper: How Electrical and Mechanical Brands Leverage Complex Product Data
An advanced PIM system supports both your e-commerce and marketing activities using enriched product data. BUT your team needs to review product data for errors and monitor results based on content context. Pimberly uses AI to perform historical validation as part of an ongoing data enrichment process based on historical algorithms. For example, our AI uses machine learning algorithms and recognizes data patterns to flag non-compliant data. As a result, you improve customer experiences by ensuring your data conforms with customer preferences for each marketplace.
However, through machine learning, AI not only identifies patterns and learns how to recognize anomalies but also uses your team’s historic corrections in the process. For example, this could include consistency in brand tone or proper spelling for specific marketplace locations. It also learns standard terms used to avoid mislabels and other issues that may affect data quality and customer experience.
Image recognition is one of the most exciting prospects for including AI in your PIM. It can validate image data by recognizing patterns, identifying objects, and flagging inconsistencies or errors. This streamlines the process to improve insufficient product information. Your team can automate manual product data enrichment steps by adding detailed attributes based on image classifiers as part of your rule-based validation. As a result, product deployment is automated, collecting image data ready for use by your team.
In essence, once a new image is stored on your PIM, AI retrieves and classifies the image, analyzes it, maps out information based on things such as pre-defined product attributes, and then stores that information with the image in the appropriate formats. All of these steps are performed without the need for manual intervention. You can then have product coordinators double-check the data so it is ready for customer-facing applications.
AI acts as the automated conduit to streamline the data collection and organization process. The beauty of the human/AI connection is that with every human revision made through your process checks, AI absorbs that information as part of the ongoing machine-learning process. The AI then applies those newly learned changes so it can apply them to the next round of data validation.
New data is collected, new data rules are learned, and AI perpetually self-trains to improve data accuracy and quality. This is the most critical aspect of the human review process, as it optimizes AI accuracy, further reducing the time it takes to correct errors at the human end. As a result, the human workload is reduced, and your overall data validation process is optimized.
Data validation is just the tip of the iceberg
In a personalized demo of the platform, you’ll see how you can:
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