How AI helps to optimize e-commerce product content

With online sales growing faster and the e-commerce landscape changing with technological innovations, traditional retailers are increasingly investing in omnichannel strategies and doubling their efforts in order to meet consumer demands. An effective way to keep pace with e-commerce giants and stay relevant in the marketplace is to offer high-grade product discovery and selection. This requires providing detailed product content with product-specific attributes, along with semantic search.

The current product content problem

As more retail businesses are moving towards e-commerce, the need for quality information and powerful search platforms has become crucial in order to entice shoppers and help them make effective purchase decisions. However, this is a challenge as they are unable to easily deliver complete product content.

Retailers rely on the suppliers to provide all the coordinating images, videos, attributes, etc. for each of the products. Suppliers use various methods to provide content such as printed or digital catalogs or in different formats like Excel, PDF, etc., making it difficult for retailers to properly source and extract the right data required for the right product. In some cases, retailers even purchase content from third-party providers or online databases. However, the challenge here persists, as most of the time, content differs from suppliers to third-party providers and validation of the information becomes tedious.

Besides the price of a product, detailed product information along with superior quality-images, videos play an important role in a consumer’s buying decision. 

There are numerous technological challenges while extracting content from the product images – some including region segmentation, diverse product backgrounds, natural settings, typography and fonts, lighting conditions, and low-quality images. For instance, inconsistent product image sizes would limit the system to capture the product details completely from all the images.

Impact of poor quality data

Missing information and uncertainty are two leading factors for consumers to abandon their shopping journey. Consumers tend to leave their shopping journey when they sense that the product does not have clear or complete information. This could range from unclear product descriptions to missing or inaccurate product attributes such as size, materials used, ingredients, etc. or even product reviews.

While there is no definitive rule stating an optimal number of product images or videos or a recommended character limit for product information, the quality of product images and videos have a direct impact on the ability of the e-commerce business to generate sales. With complete and comprehensive product information (description along with attributes like size, or weight, etc.) and high-quality images and videos would enable shoppers with the information they may need to make a purchase decision.

Effective Extraction of Product Content

With IceCream Labs CatalogIQ, retailers can effectively address the problems they face while onboarding product content to their catalogs. Leveraging machine learning algorithms, Optical Character Recognition (OCR) systems, and Natural Language Processing (NLP) techniques, it can effectively extract the right information needed for the retailer to optimize their content as well as maintain their content health. Some of its capabilities include:

CatalogIQ extracting content from a product

Attribute Extraction: ​

Images would be clicked from all angles of the product and would be fed into the machine. Leveraging NLP techniques, brand attributes such as brand name, sub-brand, tagline, flavor, net weight/volume, and calorie information would be extracted.

Brand Name Detection (Logo detection): 

Leveraging OCR, the product image is scanned for text and the output is further sent to an NLP engine specifically to identify text logos (ex: for brand logos like Zara). If the text is not detected, image processing is further applied using the brand name parameters (ex: for brand logos like Nike)

Standard Certification Detection:

In this step, a preset database with standard food certification parameters is applied to detect and extract food certification labels such as “gluten-free”, “non-GMO”, “100% organic”. Here, the images are scanned using these parameters. This is similar to how the Brand Name detection functions.

nutritional label data extraction

Nutrition Facts Extraction:

Using OCR and region segmentation, nutritional facts text is extracted. This text is further corrected using a predefined vocabulary to streamline the content. A rule-based approach is then applied to the corrected text to extract nutritional values.

Product label images are a trusted source of product information for consumers. AI can ensure that the process would improve the quality of the information and maintain data consistency across all product pages. Retailers can further benefit from this as it would alleviate the burden of validating product data provided by various suppliers, online databases or third-party providers and can provide additional information that is critical for product discovery like brand or certification logo information.

The future of Product content

Applications leveraging AI and machine learning have projected tremendous potential for applying process automation to reduce data inconsistency and enhancing data quality and thereby, improving the product data extraction processes.

At IceCream Labs, we strive to address the challenges that businesses face in e-commerce using AI and machine learning. Are you ready to enhance your product content and take your e-commerce business to the next level? Reach out to us at for an AI-based solution for your business.

Related e-commerce articles –