54 million
That’s the number of search queries Google will serve in the time it will take you to read this article. That’s about 88,700 searches per second worldwide. And we aren’t even considering the hits from half a dozen other search engines millions of people use. With the sheer volume of content being created, search engines like Google, Yahoo, and Bing continually index tens of billions of websites every day, so that internet users like you and I can find the most relevant content when we need it. What’s important when you’re searching?
- Keyword match — matching your shopper’s search query to keywords within a catalog using a stemming algorithm helps the search engine suggest related/similar results.
- Faceted search — this allows shoppers the ability to refine a selection of search data with attributes such as ‘sort by’ and ‘color’. Data is sorted and grouped into tags.
- Best first — based on the assumption that the shopper wants to see the most relevant items first, constructed using a relevance score.
- Linear pagination — this option is used when the search data won’t fit on one page, such as items in an eCommerce site. The product listing page can be designed to list products via inline paging and progressive loading.
- Autocomplete/autosuggest — this allows a shopper to save time by either filling in a partial query or suggesting or populating terms after the shopper has typed an initial amount of characters into the search bar.
Relevance

Personalization

Autosuggest
What kind of product recommendations do you want your shoppers to see when they land on your eCommerce site? How would you like them to see the right product suggestions for their queries? Suggestions are a key part of any product discovery system. The fundamental purpose of a good site search system is to help your shoppers find what they’re looking for. So, one of the ways to achieve this is to include an effective product recommendations system within your search engine. They not only help shoppers find items but also choose the most relevant search terms. Traditional recommendation systems are made up of a static list of product suggestions for specific ‘anchor’ products. How can we improve the quality of our suggestions? Suggestion systems depend on the type of business your eCommerce site is based on, for example, suggestions will be different if your store sells computer hardware as compared to fashion. When I typed in ‘Mo’ to search for Monitors on Amazon, I got suggestions like ‘Mouse’, ‘Mosquito Killer Machines’, ‘Mobile holders’, ‘Mop stick rod’, and ‘Mouthwash’ among others, even when I was searching for LCD Monitors in the same session. We can improve the search if we avoid partial suggestions when a threshold is not defined or when we don’t have sufficient information to build helpful suggestions. A good data analysis is the first step for a good suggestion system. A systematic study of search terms and the action that follows can possibly help you predict the shopper’s intent based on the search terms they use. For instance, the search engine should have picked up on my intent to buy a computer monitor and recommended only computer monitors. Armed with knowledge like this, merchants could have used sophisticated merchandising algorithms to determine the order of results. The right suggestions have the potential to influence a shopper’s purchase decisions just by virtue of the fact that your site is suggesting them.In Pursuit of Search Happiness
In a nutshell, eCommerce search is a way to get what we want when we want it. But when it comes to shopping online, not everybody is ready to buy the first thing they see because they may still want to evaluate whether it serves their needs. Online shopping is popular because of its sheer convenience. Shoppers can search for anything from virtually anywhere. As an online merchant with a large catalog, getting search to work right is especially important.
- Who are my customers?
- How can I sell more of my products?