The search bar is a powerful conversion tool for any eCommerce site. But the traditional text-based search has a lot of limitations. For example, a simple spelling mistake or a misinterpreted query can often return wrong or no results.
Semantic search addresses these issues using Natural Language Processing and deep learning techniques. It’s superior to text-based search engines because semantic search results are more relevant to the customer’s search.
Several providers like Celebros and Inbenta can integrate Semantic search to your eCommerce site. But a large part of your success with this technology depends on configuring the in-site search engine.
Here I present some tactics that will help you get the most out of semantic search and convert your customers:
- Use intelligent spellcheck and autocomplete – our customers will make one or two spelling mistakes while searching for products. Traditional search engines usually can’t suggest alternate spellings, or the suggestions might not be relevant. Analyzing previous purchases and search history semantic search can provide relevant suggestions to common spelling mistakes.Another neat thing you can do is show related search terms and provide autocomplete suggestions in the search box. For example, if your customer searches for a TV, TV wall, mount, and TV stand are two related search terms.
Image source: Alibaba.com
Can’t you do these things with a traditional search? Yes, but text-based search engines need to be manually optimized, unlike semantic engines. This takes a lot of time, and it sometimes becomes impossible to select related queries for each product.
Semantic technology learns about the search terms by monitoring customer habits, like – what the customers usually buy or view after searching using a particular set of words or phrases.
- Implement advanced filtering options – A semantic search engine can usually identify names and qualifiers within a group of words. You can utilize this capability by providing advanced product filtering options to your customers. For example, when a customer searches for red, the product filter should show the product categories with red-colored items. Alternatively, a search for shirts should bring filters such as men vs. women, color, brand, etc.
- Show relevant products using NLP – Natural Language Processing techniques help your search engine understand search phrases better. For example, suppose a customer types dark chocolates under $5. In that case, the search engine should know that the result page should only show a specific type of chocolates under a specific price range.
Image source: realfoods.co.uk
- Show top selling product images related to a search term – Configure your search bar to show bestselling product images related to a search. While it can also be done manually, modern semantic search services will automatically keep and update bestselling products and their relation to certain keywords. When customers see bestselling product images in the search box, some are likely to buy from those products right away.
- Outperform your competitors by implementing voice-based search – One of the most powerful features of a semantic search engine is its ability to interpret human voice. Mobile users are increasingly becoming dependent on mobile-based voice search. If your potential customers use their voice to search for products, your site will quickly provide them with accurate results. It’s a great way to stand out from your competitors.
Over to you
Semantic search powered by AI can change how people searches and find products on eCommerce sites. There’s no doubt that it will help you convert your potential customers in new ways. You can also analyze your customer search habits to discover their preferences and eventually present them with a more personal and helpful on-site search experience.
Author Bio: Liakat Hossain is a content marketing professional at WebAlive Ecommerce Design Company. He has been helping businesses grow by developing search and content marketing strategies since 2011. Connect with him via Twitter.