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Autocomplete is a feature that allows users to enter text into a search field and receive suggestions in real time based on the search query the user is typing into the field. This functionality is often used to help people complete forms or search for specific information.

The term “autocomplete” originated from the fact that it automatically completes a form for you without requiring a person to manually fill out each field.

However, it’s important to note that autocomplete doesn’t always mean that the data entered is stored on the server; sometimes, it’s just a suggestion given by the browser.

In addition to helping users complete forms, autocomplete can save time and effort by predicting what a user wants to search for next.

For example, if you’re searching for a specific product, you might want to see the names of similar products that you’ve searched for recently. This is where predictive search comes in.

When someone enters a search query, the system looks at recent queries and uses this data to predict what terms a user might want to search for next. If you use the same query over and over again, the system will learn about your interests and preferences. As a result, it’ll start suggesting related terms to you.

For example, if you search for the “best laptop,” the system will know that you are currently interested in laptops. So it will suggest different brands and models you might be interested in.

How does the autocomplete function help the user?

Autocomplete makes searching for information much easier. Instead of typing out all of the letters of a word, you simply start typing, and it will finish your query. This saves time and reduces the risk of misspellings or typos.

Users don’t have to type everything into the search bar; they just enter some initial text and let the system do the rest. For example, if I’m looking for a restaurant near me, I might type “restaurant near me.” Then, the system automatically completes the phrase for me and offers suggestions such as “nearby restaurants,” “restaurants nearby,” and “restaurants close to me.”

Autocomplete also increases the likelihood of finding what the user wants.

If I’m looking for a local mechanic, I’ll probably use the term “mechanic.” But there are many different types of mechanics, including automotive mechanics, plumbing mechanics, and computer repair mechanics. So, when someone types “mechanic,” the system narrows down the list of suggested terms to include only those related to automobiles.

Finally, autocomplete makes searching for things faster. When I type “restaurant,” the system immediately returns dozens of options.

However, if I type “restaurnt,” the system doesn’t know what I mean. So, it looks up the meaning of each suggestion individually and presents me with a list of possible matches. In contrast, if I type “rstaurant,” the system knows exactly what I meant because it looked up the definition of “restaurant” earlier.

Visual of autocomplete function

3 things you can do to create a useful predictive search experience

The rise of mobile devices has led many businesses to adopt predictive search technology. Autocomplete, voice searches, and contextual search are just some examples of what we’re talking about here.

But while these technologies are great tools for helping people find exactly what they want, they come with unique challenges. In this article, I’ll explain three things you should keep in mind when designing a useful predictive search experience.

1. Make sure your predictions are accurate

When someone types into a search box, there are several factors that go into determining whether or not a prediction appears. These include the quality of the data being searched, the relevance of the predicted text, and the accuracy of the prediction itself.

If your predictions aren’t accurate enough, they won’t show up in the list. And if they don’t show up in the first place, they might confuse searchers and make them less likely to convert.

2. Consider how your predictions affect the usability

As mentioned above, predictive search technology can help users quickly find information without entering keywords manually. However, incorrect usage could actually hurt your conversion rates.

For example, if you predict “pizza,” but the user enters “pepperoni pizza,” your system might think he wants pepperoni pizza and suggest a different restaurant entirely. This could lead him to abandon his original intention altogether and choose another option.

To avoid this problem, make sure your predictions are relevant to the query. Also, ensure that your predictions are displayed immediately. Otherwise, the user might change his search terms, making your predictions irrelevant.

Finally, make sure that your predictions provide value beyond simply suggesting restaurants. You can do this by adding additional information such as reviews, menus, photos, etc.

3. Optimize your predictions to boost conversions

If you’ve ever used Google’s autocomplete feature, you may have noticed that sometimes it suggests words that seem completely unrelated to the current query.

For instance, if you type “michigan,” Google will often return suggestions like “mickey mouse,” “michigandrews,” and “micheal jackson.” While these suggestions may be funny, they’re unlikely to help you find anything. Instead, you’d probably prefer something more along the lines of “Michigan State University.”

To get better results, you have to analyze your site content and see which words are most frequently associated with your brand. Then, add those words to your autocomplete predictions.

Autocomplete helps customers find exactly what they want faster, which increases conversions. Users expect an immediate response to their queries, so if you don’t provide them with an autocomplete feature they’ll look elsewhere. If you do offer it, make sure you use the correct technology.

When choosing a predictive search tool, consider the following: What features does the platform offer? How easy is it to integrate? Is it scalable? Does it support multiple languages? Can I customize my own predictions?

To get the best results, you should always test your predictions before rolling out any changes to your website.

A good way to do this is through A/B testing. In other words, you’ll create two versions of your page (one with predictions, one without) and then compare the results. If you notice a significant difference between the two pages, you know that your predictions were effective.

As well as testing, you should always keep track of your predictions. Once you start using predictive search tools on your site, you’ll need to keep track of how well they perform.

The best way to do this is by logging into your account and viewing your data in real-time. This way, you’ll be able to see whether or not your predictions are improving your overall conversion rate.