6 Ways To Improve Product Discovery & Search Experience For eCommerce

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Product Discovery For Ecommerce! Why should brands and businesses around the world improve product discovery? In 2019, over 3.5 billion e-commerce searches resulted in $2.3 trillion in revenue for online stores, according to Shopify. Shoppers using search have a 1.8X higher conversion rate than an average visitor.

While it goes without saying that merchandising choices play a key role in good search experiences, often the problem does not lie in the assortment but rather in the quality of product data.

Here’s a simplified example: A shopper searches for a tangerine colored dress. But the search returns no relevant results. The online store just lost a customer, even though they may in fact have a range of products that have been tagged as “orange”.

High-quality product data is the foundation for product discovery, positive shopper experiences, and revenue growth.

Here are 6 ways to improve product discovery and search experience For Ecommerce:

1. Personalized Search Results

According to a Forrester study, 77% of shoppers have chosen, recommended, or paid more for a brand that provides personalized experiences. This means that product catalogs need to have product metadata that is detailed, and accurate, and optimized for discovery. Vue.ai predicts product tags, available in a standard catalog allowing for standardized product data, enabling a dynamic personalization experience.

2. Reduce Null Result Pages

Ideally, the search engine should identify every poorly worded search and return a good set of results. With standardised product meta tags, it is possible to map common erroneous tags to ensure that any keyword search returns some related products or product suggestions. 

3. Automate Zero Result Page

If the shopper is looking for a product that is currently not in the catalog, it is important to boost products or categories that are closely related to the search terms used.  With high-quality product tags attributed to each product, retailers can understand how products across categories relate to each other and provide “related products” offerings, delivering improved alternate product recommendations to what the shopper is looking for.

4. Improve Product Sorting and Filtering

Shoppers using site search have proven to have 21% higher average order value compared to customers that simply browse. Customizing search results can help shoppers narrow down results obtained for a query. 

Having product attributes as specific as outfit type, shape, style, length, and occasion in the form of search filters can make a huge impact on how well shoppers engage with the site. Creating and maintaining detailed meta tags with rich attributes power greater catalog coverage and better search experience.

5. Manage Common Typing Errors with Intelligent Guesses

Spelling mistakes, use of broad terms, differences in how people describe the same product can make product search a struggle. A search as simple as one for a “Sheer” dress instead of a “Shear dress” can drive shoppers out of the website. Ensuring products are tagged with enough robust meta tags that enable them to be mapped with synonyms or close matches provides relevant results for every search term used. 

6. Rank higher on Search Engine Results Page (SERP)

Studies suggest that over 60% shoppers start any product search on Google. Good product tags help improve product discovery on external search engines. With product data that takes into account every product’s attributes, optimize search for long-tail queries to ensure shoppers find products wherever they are, in any way they want to.

AI for Improved Product Discovery For Ecommerce

AI-powered automation tools are key for retailers to manage, organize, and enhance the quality of product data.

Vue.ai’s Automated Product Tagging solution extracts over 250+ product attributes from product images to generate product metadata that is optimized specifically for fashion and apparel, improving product discovery on website and on search engines

It classifies product assortment accurately with automated category predictions and descriptions. Its data extraction and image recognition systems generate quality metadata that can feed into diverse applications. As a result, detailed product attributes can be automatically extracted from product images. This allows for effective data standardization across all channels.

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