Have you ever received the “No Results” message upon entering a query in the site search bar? I bet it was pretty annoying.
Imagine that you wanted to buy a home fragrance, hoping to spare no more than ten minutes to make an order. Then the “No Results” message popped up, leaving you confused and frustrated. You had better luck with home scents, which made you realize the site’s internal search wasn’t good at handling synonyms. It made you sigh and think of other websites that were smart enough to offer you what you wanted.
Sadly, about 61% of eCommerce sites offer site search performance that is below average. At the same time, on-site searchers are almost two times more likely to convert than other website users.
Using an outdated eCommerce search engine is certainly not an option if you don’t want to lose profit every time your internal site search delivers poor results.
What to expect from a smart eCommerce search engine?
Let’s figure out which requirements an up-to-date internal website search should meet to satisfy the picky users:
Go one step further with personalized AI-powered offerings
With a smart internal search engine, you can rest assured that no searches on your site will end in no results. Instead, users will get a list of relevant products to choose from, which is great.
But what if we say that modern technologies allow you to take your site search a level further? What if users could see products they’re more likely to buy at the top of the list? Not just products the customer has looked at, but products that match the customer’s color, brand, and price segment. Thanks to AI-powered algorithms, you can provide this kind of experience to website users.
Recommendation and Search solutions are combined into one product engine
Hello Retail’s Recommendation and Search solutions are combined into one product engine which predicts which products users will probably like. This approach allows for even higher user satisfaction and six times better conversions. We do this by enabling logic to the customer journey similar to what customers experience when looking at the product page, for instance:
In that perspective, we understand what preferences trigger certain actions and can thereby personalize the consumer experience and create optimal relevance when using the site search.