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Zur Übersicht Startseite / Blog / Why Is Autosuggest Such an Important Component of the Intelligent Search

Why Is Autosuggest Such an Important Component of the Intelligent Search

21. Jul 2017 | Updated on 12. November 2020 | 04:00 | Reducing Bounce Rate

Intelligent search plays a key role in any online store. That's because it helps to steer the online shopper in the right direction, taking them quickly and easily to the product they are looking for. One major function of the intelligent search is autosuggest. With autosuggest, the online shopper can navigate from the search function toward the product details page. This blog looks into the background of the autosuggest feature.

 

In this blog article, we will cover the following topics:

What Is Autosuggest and Why Is It So Important?
How Does Autosuggest Work
Autosuggest Example at Tableware24
Conclusion: Autosuggest Offers a Wide Range of Services

 

What Is Autosuggest and Why Is It So Important?

Autosuggest (also known as typeahead) is a function of the intelligent search that proposes suggestions to the user. Corresponding words and products from various areas (i. e., product, category, content, etc.) are automatically displayed in a preview window below the search field. Consequently, as more of the word or search word combination is written out, increasingly precise suggestions can be displayed in the preview window. As the user types, the product catalogue is searched in milliseconds using a search algorithm, and the suggestion list is displayed. Hence, autosuggest is an important feature as it greatly contributes to the user orientation in the online shop.

Consider for example a costumer entering a local store. They will look around and orient themselves by using the goods displayed to find their way to their desired product. In an online shop, the autosuggest search meets an equivalent purpose.

 

How Does Autosuggest Work

The basis for an autosuggest search is the:

  • Product catalogue. Certain attributes from the feed are indexed and weighted. In this way, autosuggest can work incredibly fast. Labels are decided based on empirical values, but they can be changed at any time. The product catalogue is searched via a
  • Search algorithm, which runs through the indexed items of the product catalogue in a matter of milliseconds and issues the final result in the preview window. The criteria used for the search algorithm is discussed in advance with the store owner. For an optimised suggestion, the
  • Click-and-buy behaviour of online shoppers plays a role. The search algorithm accesses a knowledge base and initially displays the suggestion in the preview window, demonstrating the most likely click-and-buy behaviour. For example, if the search is for running shoes with similar spellings (i.e., Adidas and Asics), the search algorithm finds how many products from the respective brands are in their range, and which is most popular for online shoppers. This is then output for the customer. It is also possible to issue a personalised product suggestion, which is based on the individual customer, i. e., sports shoes for women, if the online shopper is recognised as a woman. In certain cases, it is necessary to check which solution makes most sense for the online store. An integrated tracking code, which stores anonymous data on the online shopper in the knowledge base, is required to retrieve information.

By clicking on the autosuggest preview window, the online shopper is directed straight to the product details page, a corresponding results list or to a content piece related to the search request. This means that the desired item or additional information can be found quickly.

Do you have an autosuggest search in your online shop that automatically adds search terms and allows the online shopper to be redirected directly to product detail pages, results lists or content articles? What experiences have you had with this? Tell us about it!

 

Autosuggest Example at Tableware24

If the term “best” is entered in the online shop of Tableware24, a preview window with suggestions opens after the first letter. Various products are displayed on the right-hand side, which immediately lead to the product detail page. On the left side of the preview window, the matching suggestions (e.g. category and product type) for the search term are displayed. Here you can navigate directly to the list of results for “Besteck” (cutlery) or “Besteck-Set” (cutlery set). The path then leads back to the product detail page.

The screenshot shows the autosuggest function of the online shop Tableware24 for the search term "beste".
Autosuggest function of the online shop Tableware24 for the search term “beste” (Source: screenshot of tableware24.com)

If the term “vas” is entered into the intelligent search, products, contents and search suggestions appear at Tableware24. A selection of different vases appears on the right-hand side. These can be selected, for example, according to the click-and-buy behaviour of the respective online shopper. On the left side, there are links to contents, but also to different brands. The online shopper has the possibility to click on “vasen” (vases) and learn more about it as well as to get to different brand result lists. In this way, content articles and brands can also be integrated into the intelligent search. Placement of content articles in the autosuggest preview window with text and preview image is also possible as well as direct forwarding to the content article.

The screenshot shows the autosuggest function of the Tableware24 online shop for the search term "vas".
Autosuggest function of the online shop of Tableware24 for the search term “vas” (Source: screenshot of tableware24.com)

Conclusion: Autosuggest Offers a Wide Range of Services

Autosuggest is an integral part of the intelligent search. It provides an essential service for the online shopper from the moment they enter a search term, namely support in the orientation and navigation to the corresponding product details page. For the autosuggest to work appropriately, tailoring it to the online store is key. The product catalogue must be labeled and weighted, and the search algorithm must be configured. Integrating the click-and-buy behaviour is the final touch, which ensures that the online shopper is shown a sorted preliminary selection. In this way, autosuggest helps to prevent the customer from leaving the store, which in turn increases the rate of conversion. The autosuggest function is therefore a crucial component of the intelligent search.

 

 

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Daniela Ilincic

Head of Marketing

Daniela Ilincic is Head of Marketing at epoq. Her background is in digital marketing with a focus on SEO and content marketing. She has built up the digital sales channel at epoq, which she continues to optimise with her team. In addition to her work, she enjoys communicating market-relevant information on digital topics.

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