How to clean messy product data for your e-commerce store

Only 2.86% of online retail visits become conversions. Staggering. You probably never imagined this rate to be so low. It looks like e-commerce stores from all over the world are doing something wrong. 

Many of them underestimate the power of product data.

No matter if you have decided to start selling your products on Instagram or simply list them on another marketplace, you need to have data on your products. And, it’s not just about having any kind of data. It’s about having relevant and quality product data. If you add poor data to your product data feed, your results will also be poor.  Results depend on various factors, like the accuracy of your ad target, the attractiveness of the title, and the relevancy of the product description.

And all these factors will bring results if they can rely on accurate product data. This is important because the competition is harsh, and your customers will easily go to the next seller if they can’t find what they need in your product information. For fashion resale platforms, leveraging AI solutions for fashion resale can help ensure that your product data is accurate and up-to-date, giving you a competitive edge.  But, how to achieve this? How to win over competition, create a seamless customer experience, and maximize results? 

The answer is—clean product data.

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What's clean product data?

Clean product data is something that’s achieved through good PIM (Product Information Management). Product information management is a process of creation and maintenance of product descriptions for product catalogs. It’s a market worth $7 billion and expected to reach $11.4 billion by 2024, due to the growing adoption of PIM solutions from companies that don’t want to suffer from data inconsistencies and poor data syndication.

PIM makes your product catalog processes more efficient, providing seamless customer experience and boosting conversion rates. At the same time, it reduces your time-to-market and makes your expansion to new markets much easier as it can handle data in different languages. 

With good PIM, you get accurate, complete, and standardized product data, meaning you get clean product data. Your product feed doesn’t have missing information and contains correct and healthy product data.

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How to manage product data effectively? 

Product information is traditionally managed through several platforms, making it uncentralized and unstandardized. Spreadsheets, enterprise applications, and digital asset management systems all contain product data that’s difficult to manage. Moreover, this approach increases costs, time-to-market, and the possibility to make a mistake. 

The solution to these challenges is an AI-based automatic tagging engine that replaces manual work and reduces costs. This engine can auto-tag an entire catalog in just a few minutes—only by analyzing its images. Thanks to advanced Deep Learning algorithms, automatic tagging tags photos in the product catalog based on their characteristics. These algorithms completely automate the data management process and require no human intervention, generating metadata for catalog assets.

Automated product tagging scans a particular image, recognizing clothing in images just like a human brain. This way, every image gets attached attribute labels which represent deep insights about the product and its characteristics. The AI algorithm learns from the tags and collects data about how they are used, who is using them, and how they are connected to other tags.

This process makes catalog management well-organized, allowing e-commerce stores to monitor sales, discover their bestsellers, get rid of unpopular products, and manage stocks efficiently. Automatic tagging makes product data clean by organizing products according to their attributes, like style, design, brand, color, etc. By providing comprehensive product data, this tagging not only streamlines catalog management but also enhances the effectiveness of a shopping recommendation engine, ensuring that customers receive personalized suggestions based on their browsing behavior. The data can be maintained clean by adding more tags over time, keeping the catalog up-to-date with the latest fashion trends. These tags can help e-commerce stores dive deep into their sales analytics and have a greater impact on their visitors’ buying decisions. 

Suggested read:: Fashion Retailers, Manage Your Product Data Before It Manages You

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Why do you need clean product data? 

Well, you probably won’t get good results if your product feed is messy.

Clean product data can help you improve your e-commerce store in many ways: 

• Standardized product data. Clean product data means standardized data. You will have one term for a certain article and use it every time this article is mentioned, instead of mixing different terms in different channels. With accurate product data, your ecommerce site search will deliver more relevant results, enhancing the overall shopping experience.

• Easier channel distribution. Now that the information is standardized, it can be easily sent to channels without any confusion.

• Better management. When a data feed is accurate and standardized, it can be easily managed and optimized. This is especially useful when you need to adjust the feed for a certain channel, perform A/B testing, and create search filters.

• Increased conversions. With clean product data, shoppers will get accurate and quality data. This will bring you more conversions as they will have their questions answered and will know what to expect from your product. 

• Improved customer experience. Accurate product data meets the product expectations of your shoppers. If you educate them well on your product, they will know exactly what to expect and won’t be surprised when they get the order. 

• Faster time-to-market. Clean data and automated catalog processes will allow you to bring your product to the market immediately. You won’t have to wait for your employees to add data manually. 

• Increased visibility. A clean product feed ranks better because search engines find accurate information. As Google’s AI-based algorithm understands the content context, e-commerce stores with healthy data will have better chances in ranking higher than those with messy product data. 

• Valuable predictive analytics. The Deep Learning algorithms of the automatic tagging engine allow you to understand the performance of your products. They bring you important insights about your customers’ preferences, as well as their geographic and demographic characteristics. This will help you predict trends and get ready for shoppers who will come in the future. 

Cleaning up messy product data through automatic tagging brings you many benefits beyond just cleaning the database. It also structures your products consistently, creating a logically-organized product feed. Your offer is clear and precise, as you eliminate incomplete and inaccurate product data.

Your customers are well-educated and know exactly what they are purchasing.

Your products are easily discoverable as accurate data leads searchers directly to your products. 

Pixyle.ai’s automatic tagging engine can keep your data clean by automatically scanning your products’ images in the blink of an eye. Our AI-based technology adds accurate and rich descriptive tags to your products, providing relevant and healthy data for your e-commerce store.

Make every visit count by giving your users exactly what they want. 

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