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You can probably imagine what suggested search functionality might say if it could talk as you’re doing a web search and entering the letters in a search query:

“Aha, I bet this is what you want.”

“Or hey, what about this?

“Here are more educated guesses in case I’m having a senior-search-engine moment.”

Now I know…you want this!

“Yawn. That’s what everyone in this demographic wants.”

Well, it might not have a smart-ass attitude, but you’d have to agree that an “intelligent” search engine mumbling about useful search suggestions as it’s humming along would be at least somewhat entertaining.

How smart is your search engine?

Suggested search is of course silent, but nonetheless productive, helping users sift through  seas of search-results page possibilities to enter queries that are guaranteed to return great results. Isn’t it rewarding when you can get what you need in the blink of an eye and enjoy a satisfying customer experience to boot? And the result is amplified if they’re using a mobile device to try to find the right web pages. According to Baymard Institute, 78% of people using search features in a mobile test depended on autocomplete options for help. Suggested search means they can stop typing (hooray), simply click the right suggestion, get to the content they want, and get on with their day of surfing social media, gaming, reading the news, or whatever activity they’re currently indulging in.

What is suggested search?

In terms of a formal definition, suggested search (or keyword query suggestions or just query suggestions) is a search feature that predicts the remaining part of the search term being entered, as well as the next word or phrase as the user is starting to key it in.

The autofill suggestion list is data driven, leveraging analytics information generated by applying artificial intelligence to input such as people’s browsing history and user search history (past searches).

Search suggestions — popular searches, trending phrases, or recommendations based on recent searches — then magically appear below the string in the search box. They can be edited in real time to factor in the addition of each new letter.

Of course, these phrases are only suggestions of potentially more-accurate or -relevant queries — prods to try a more precise query in order to retrieve more-specific results. Regardless, the searcher can select a suggestion that looks good, or, if the right item is not appearing, continue entering letters until the search engine produces the item they’re looking for.

Suggested search vs. autocomplete

If you’ve been paying attention, you may be wondering what the difference is between suggested search (or predictive search) and autocomplete. After all, don’t they sound like the same basic thing?

They do. But the term query suggestions refers to the suggestions generated from the text input.

The term autocomplete (or “dropdown”) refers to the user interface that lets a particular user interact with the predicted search items or query suggestions. An autocomplete is really just a container. In that capacity, it could instead display search results rather than suggested searches.

So there you have it, an understanding of suggested search and its sidekick autocomplete.

Amazon and Google search of course have the best search suggestions. But many other smaller and niche sites have adopted the same, if not quite as outstanding, autosuggest technology in an effort to mirror the likes of Google personalized search.

Which is a good thing, because most consumers have gotten used to the convenience of search shortcuts like having query suggestions appear in an autocomplete. If you type in queries expecting to enjoy the same kind of functionality you get with Google search results, then you’re going to notice if it’s not there and perhaps roll your eyes and leave the site in a huff.

Benefits of suggested-search functionality

When site users select from search suggestions based on autocomplete data rather than typing in their own complete queries, they’re likely to have a much more positive user experience. They can delight in modern search, personalized search results, and being made aware of trending searches, instead of, for instance:

  • Having to think about their previous searches, then try a query that might fall flat; instead, they can point to the appropriate phrase when the search engine guesses it
  • Straining to enter letters (and causing typos), especially if they’re on their mini device like an iphone or Android phone  
  • Wracking their brain about what is relevant search phrasing; instead, they can simply be on the lookout for the right snippet when it appears
  • Worrying that they’re not getting the right content (because based on their perceived user intent, they definitely are)

How do search recommendations work?

Here are some details about the process:

  • In order to select relevant suggestions from a database, syntax and AI or machine learning algorithms are applied
  • The way in which predictive search suggestions are generated can be edited to more accurately meet searchers’ needs
  • Some predictive search features use frequent search data from content management systems to refine queries by applying structured parameters and hierarchical lists, then the most relevant results are presented
  • The coding of autocomplete functionality may be different for various solutions
  • A typical implementation is to provide query suggestions with each entered keystroke 
  • When a query suggestions index is configured, it begins filling with suggestions that result from how its users interact with the search experience.

The more traffic you have, the more suggestions you can generate.

Configure to your needs

You can provide suggestions and results on the same autocomplete predictions interface, in multiple sections, if desired, each from a search of a different index or covering different facet values, for example for products, categories, brands, articles, or blog posts

You can have your software normalize and complete suggestions to remove duplicated, similar, and prefixed suggestions. You can also disqualify certain search terms. For instance, Algolia’s Query Suggestions doesn’t include a term as a suggestion if it contains non-alphanumeric characters, doesn’t meet the minimum number of results configured when querying a given source index, doesn’t meet the minimum number of letters configured, or matches any banned expressions. The top searches taken from the analytics API are aggregated, and then more normalization occurs.

Want a suggestion? Consider Algolia search

Search suggestions are not only a help for people who need to find things online, they’re a great way to optimize your ecommerce site and increase conversions.

Fine-tuning your user experience for search relevance can be daunting to figure out. One easier option: partner with a search-as-a-service partner like Algolia, which supplies your search UI, analytics, and other tools to let you create the best search experience available based on your use case and user data. 

Plus with Algolia, your site search personalization can take a step further, with autocomplete suggestions in your search bar, adding more intuitive functionality such as instant search results and federated search. These autocomplete-based features come with methods that create an immediate feedback loop that increases relevancy and speed and ultimately drives discovery for all your individual users. 

If you’re ready to find out how you can take advantage of our proven search algorithm and near-real-time search, contact us. Are you a developer? Start building out your new solution for free. Check out Algolia’s search technology today!

 

About the author
Catherine Dee

Search and Discovery writer

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