A platform has good discovery when your customers are able to find what they want with minimal efforts, while knowing broadly what your website is about. Most of the platforms today have a lot of content for their consumers to come across, but the issue of them meeting with the right ones has still not been solved for. Here, we will address this major problem that brands and companies face through bad search and how important it is for online businesses to function better.
The importance of enabling easy discovery for your users
A typical user journey starts with arriving at a website, where they can find what they need. The user then takes the next step to try and figure out the options before deciding on the final purchase. This is where the ‘search for the relevant product/content’ starts. Research shows that almost 60%+ users tend to use the search bar as their first medium of interaction with your platform for discovery. The rest of them prefer navigation or browse through to land on a page that might offer them relevant content/products.
Search is considered to be the easiest way to find what the user wants. They get to type in their query and ta-da! the product or listing appears. Unfortunately, more often than not, the search engines do not return the right results.
In spite of search being the most used feature for discovery, there is almost a 50% churn in users who use it. This makes site search within any platform to be one of the most critical focal points in the entire funnel.
Why is there such a steep drop?
Let’s dive a little deeper into some of the key issues here.
Site searches on string-based technology
Search engines are usually built with string matching at their core. They look for the exact letters from the user’s search query against the listings search query to give back results. The problem here is that you can never predict the necessity of the user before they search. It could be ‘Black tshirt’ one day, ‘Black teeshirt’ another day and these string engines cannot keep up with such queries. They can work only if the exact word is specified in the product description/title. If the platform has the right products with the keywords not present in the description or tags, the result turns up empty or wrong results get shown with partial match
Manually written descriptions/ Meta tags
Listing descriptions are a direct correlation to how users can find a product. Commonly, the source of the indexed searches contains titles and meta tags that help in matching a user’s search query with the title text to return results. The simple truth is that, brands cannot manually tag all the relevant keywords to products and the words can keep changing no matter how hard they try.
So, is there a way to mitigate the above problems?
In our current day and age, AI-NLP comes to the rescue and provides solutions to all the existing site-search problems. The technology behind it is capable of understanding the true meaning and context to bring back results, almost similar as to how humans can process languages. Let’s take an example of ‘Dandruff care vs Hair care’.
Here, unlike string-based engines, NLP would understand both the terms ‘Dandruff’ and ‘Hair care’. It associates them to fall under the same context spectrum to bring back products based on specific mapping. This way, relevant results are brought back to the user, irrespective of the exact term or meta tags linked to the products. NLP based engines can solve key problems in terms of relevance and can even work with moderate quality of listing data.
Zevi is a site search engine that is built with NLP at its core. It makes search a breeze for users to navigate through and for developers where they can skip through unwanted steps in their integration efforts, compared to what they face with string-based search engines. An NLP based engine can help your customers to experience product discovery in the most natural way, leading to higher engagement and revenue.
Search and discovery play a very important role in a customer’s journey. Existing search engines based on string matching cannot cope up with demanding needs of customers due to their working limitations and the quality of listing descriptions. NLP based search engines like Zevi are transforming the technology behind searches to meet customer discovery needs in the best way possible.