Score Your Product Catalog with Generative AI

See How Your Product Catalog Matches Up Against the Competition

It all starts with data. How’s yours?

Ecommerce product discovery, search, recommendations, and merchandising all start with product data. Algorithms crunch performance and customer data to personalize product displays that are most likely to engage and convert. Unfortunately, many product catalogs are lacking critical product attributes and information leading to suboptimal results.

Many product discovery solutions include merchandising features to create manual workarounds at the catalog and presentation levels, but these time-consuming tools are merely band-aid solutions and impossible to impose at scale across large catalogs of tens or hundreds of thousands of SKUs. This is a difficult ask and, as noted in a recent interview with J. Crew CIO, Danielle Schmelkin, “It is not humanly possible to get it right at scale and with the speed you need.”

The good news for retailers is that new vertical market solutions are leveraging the latest in generative AI to go beyond the catalog. Enriching product titles, descriptions, categories, and attributes quickly and at scale leads to increased generative and search engine optimization and better product discovery results resulting in increases up to 4% in conversion rate!

While some retailers recognize gaps in their product data, many learn of them only when poor results are manifested. According to Kevin Jackson, ecommerce data analyst, “Retailers are quick to criticize their product discovery platforms for apparent poor results when most problems relate to product catalogs errors and omissions. For example, a furniture retailer lamented that several arm chairs were returned in search results for “sofas” and were surprised to learn their catalog categorized the displayed chairs as “sofas”. The problem wasn’t the platform, it was the data!”

This is one of many examples of how retailers don’t know what they don’t know. Nevertheless ensuring accurate and reliable product information need not be a daunting task, for not only can gen AI enrich product catalogs, it can score them too! New product catalog scoring tools check for product catalog:

Product Catalog Accuracy

Generative AI models can enhance the accuracy of product catalogs by generating precise and detailed product descriptions, auto-correcting errors, and ensuring consistent data entry.

For example, consider a scenario where a retailer selling a diverse array of products including apparel and home goods. Generative AI can generate product descriptions that are consistent in style and format across the entire catalog, minimizing manual discrepancies and human error. Gen AI can likewise enrich descriptions by automatically integrating key specifications and features derived from structured and unstructured data.

Product Catalog Completeness

Generative AI models can make recommendations for filling in the gaps for missing product information based on existing data and patterns found in related listing. AI can even assist in completing new items by understanding partial entries combined with historical data patterns.

For example, consider a scenario where a retailer manages a vast inventory and some electronic items, such as smartphones, have incomplete product attributes including no screen size, battery capacity, or camera features. AI can employ web scraping techniques to gather comprehensive product details from reliable online sources such as manufacturer websites or industry databases. AI can go even further beyond the catalog by categorizing products based on available attributes and fill in the blanks by generalizing common characteristics from similar items.

Product Catalog Consistency

Generative AI models can standardize product catalogs by automatically correcting inconsistencies, such as variations in units of measurement or spelling errors, helping maintain a consistent format across the catalog. Gen AI can also identify and eliminate duplicate entries ensuring that each product is listed only once. It can even compare product attributes to detect possible duplicates, even those that are not exact matches.

For example, consider a scenario where a retailer sells clothing and has a product catalog that includes various garments like shirts, pants, and dresses. Over time, different sellers and data entry personnel have added products to the catalog, leading to inconsistencies in how sizes are recorded. Some entries use measurements in inches (e.g., waist size: 32″), while others use centimeters (e.g., waist size: 81 cm), and some might use general size labels like S, M, L. After processing, the AI updates the product catalog to reflect the standardized sizes for all entries. This ensures consistency so customers see uniform sizing information across all similar items.

Product Catalog Relevance

Generative AI can significantly enhance the relevance of a product catalog by personalizing product recommendations and ensuring that users are presented with the most pertinent products for their needs and preferences. Generative AI models analyze vast amounts of data, including purchase history, browsing patterns, and customer feedback to predict what products a customer might be interested in.

For example, consider a scenario where a retailer sells outdoors and hiking related products. The AI analyzes the customer’s frequent purchases of hiking backpacks and boots, time spent viewing various brands, and highly-rated reviews from other similar customers and then generates a personalized list of hiking-related products such as a new line of eco-friendly jackets, the latest hiking gadgets, or limited-edition trail shoes that are quite popular among similar shoppers

Product Catalog Natural Language Processing

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Product Catalog Structured Data Implementation

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Product Catalog Content Quality

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XXXXXXX.  By implementing these AI-powered strategies, eCommerce platforms can ensure that their product catalogs are not only accurate but also user-friendly, ultimately improving customer experience and boosting sales performance.