Product catalogs with inaccurate or very little information can leave customers frustrated. It can result in poor purchasing decisions, product returns, a loss of revenue and customer trust, as well as an impact on brand loyalty.
Enriching product catalogs in real-time with specific, relevant information is the solution to this problem. Enriching the product catalog is not only for providing enough product information and improving customer’s shopping experience. It is also for boosting the effectiveness of the content recommendation engine.
When product attributes such as descriptions, specifications, and images are enriched and detailed, they can make a significant contribution while recommending products. This helps to meet the unique needs of customers while also increasing satisfaction, loyalty, and sales.
Leveraging LLM for Enriching Product Catalogs
Recent advances in large language models (LLM), such as ChatGPT, have enabled content recommendation engines to function more efficiently. It eases the process of catalog creation and maintenance through:
- Description Creation: LLMs analyze a product’s color, size, and material to create detailed and engaging descriptions to reflect customer’s interests.
- Improved Search Accuracy: It can understand complex and vague search queries and match them with the product catalog.
- Enriched Product Attributes: The catalog emphasizes a product’s unique features to improve the product-finding experience and personalization.
- Simplified Catalog Management: An AI recommendation engine can assist in the creation of new product attributes as well as the easy implementation of changes such as product updates and reclassification.
Enhancing Content Recommendations with an Enriched Product Catalog
An enriched product catalog has a great contribution to providing personalized shopping experiences. The product catalog powered by LLM can influence the content recommendation engine in multiple ways. Here’s how enriched product catalogs enhance the AI recommendation engine.
1. Customizations for Different Customer Needs
The enriched product catalog allows the content recommendation engine to meet the content need even though it is specific and distinct. It will consider minute features such as the use of ‘recycled material’ in shoes or ‘semi-sheer transparency’ in dresses to provide recommendations.
This guarantees that buyer’s special needs like environmentally friendly products or aesthetic standards, are met. The overall satisfaction will increase when businesses consider their customers’ interests and values.
2. Offering Data-driven Recommendations
The enriched product catalogs enable the content recommendation engine to maintain a coherent approach to the customer. It means that the recommendation engine always has the best and most current product information regardless of changes in client behavior.
Moreover, the enriched product catalog provides a unified view of customer preferences. This feature enables AI recommendation engines to deliver highly targeted product suggestions. This approach helps the organizations to make more informed decisions and provide relevant recommendations.
Therefore, businesses can ensure that the content recommendation engines meet customer needs across the channels improving the shopping experience.
3. Enhanced Product Discoverability
The content recommendation engine can recommend products for the long-tail keywords easily if the product catalogs are included. This includes descriptions like product features, material, or unique characteristics. The probability of conversion is high regarding these queries since long-tail queries mean that the customer is ready to convert.
This is especially important when the product or category is specific to a certain niche and may easily go unnoticed. For instance, if a buyer is searching for a “semi-sheer black dress”, a content recommendation engine shows clothes that are black, and semi-sheer in density. Thus, the customers can ensure that their unique needs are met.
4. Increased Conversion Rate
When a content recommendation engine has a detailed product catalog, it can offer clients the most appropriate recommendations. Because when the recommendations are entirely customized to the client’s individual preferences, they are less hesitant and more confident in making a purchase.
For instance, if a consumer is in search of running shoes, the probability of conversion is likely to go high if he is provided with all the details. It includes the material of the shoe, fit, and durability. Such product transparency makes clients make better decisions leading to the conversion of visitors to buyers.
5. Effective Upselling and Cross-selling
The enrichment of the product catalog can assist the content recommendation engines in providing better cross-selling and up-selling recommendations. It guarantees that all product attributes are current and the suggestions are appropriate to the needs of the consumer.
For instance, consider that a retailer wants to cross-sell batteries to a customer who bought a camera. If the catalog for batteries does not contain enough details, he cannot recommend the battery that best fits the camera. Product data enrichment overcomes this issue and guarantees that all the essential information is provided to the content recommendation engine.
Bottom Line
Delivering exceptional customer experience is essential to drive business growth. The enriched product catalog can aid a business greatly in this journey. It empowers content recommendation engines to provide more personalized and relevant recommendations.
As of now, product catalogs are enriched with AI and LLM models. But in the future, it may leverage technologies like augmented reality and virtual reality to provide a more immersive shopping experience. Organizations can also use integrated user-generated content to enrich product information and build social proof.
As technology evolves, businesses need to embrace it to provide richer, more engaging shopping experiences that resonate with the customers of tomorrow.
