If you want to look for something on an eCommerce site, you most likely end up on a search bar. As you move ahead, the page will display the type of product, pricing, or product visuals that you are looking for. It is the first step in the consumer journey funnel. Search functionality displays all the information related to your product, as a result, you get to explore and compare everything in one place. As an eCommerce business owner, if you don’t have a strategy for the top end of the funnel certainly consumers will never reach the end of the funnel.
Even though e-commerce stores have diversified themselves to integrate every possible product sold today. Their generic keyword-based searches have failed to stay competitive. Consequently, creating an annoying search experience that limits the ability of customers to find what they need.
Challenges with the eCommerce Search
In a competitive digital world, eCommerce is like a treasure chest where you find almost anything you need with just a click of a button. On the contrary, the onsite search challenge is finding the exact thing one wants to buy. The adoption of the latest technologies in eCommerce has seen exponential growth in just a couple of years. Still, the eCommerce search tab has evolved little from the primitive keyword queries of the web’s earliest days.
So why do some eCommerce search tabs give relevant information while others fail? Because many eCommerce search engines still use textual search to scan the product queries.
A textual or Syntactic search is excellent at finding keywords from a set of organized data. But it underperforms at –
- Exploring for long-tail keywords and understanding misspelled words
- Understanding natural language expressions or slang
- Distinguishing product descriptions from product names
You can also read: Mcommerce: Shifting Consumer Behavior & Personalization
Semantic Search To the Rescue
So what are most eCommerce sites missing in their search bars? Semantic search or understanding the searcher’s intent within a specific context. For instance, if you search for the term “corona”. The text-based search engine would have given the result of a beer bottle, but a semantic search engine knows the relevance and will provide you with covid test kits or covid virus-related books. But how does a search engine understands the subtle differences? By learning from past results and creating links between entities, a search engine can make use of the contextual meaning of terms as they appear in the searchable database to generate more relevant results.
Moreover, It allows the user to ask natural language questions, as opposed to adapting our language for computers to understand: ‘find a book with a mysterious plot?’ vs. ‘mysterious plot book’. In the second case, the user only types the keywords which do not have any verbs or unnecessary words. And a semantic search understands both well.
Putting this all together, the explosion of a spectrum of products in e-commerce sites has led to search interfaces being unable to cope as a differentiating factor. Generic text keyword search engines cannot compare to semantic searches. Customers are accustomed to the personalized and intelligent searches on the modern Web. Finally, an evolved e-commerce experience is a reminder of just how far website search engines have come from their early days and how far the rest have to catch up.
