Why Is Autosuggest Such an Important Component of the Intelligent Search
21. July 2017
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.
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 catalog 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 catalog. 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 catalog is searched via a
- Search algorithm, which runs through the indexed items of the product catalog 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 optimized suggestion, the
- Click-and-buy behavior 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 behavior. 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 personalized product suggestion, which is based on the individual customer, i.e., sports shoes for women, if the online shopper is recognized 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.
Autosuggest Case Study: Villeroy & Boch
In the Villeroy & Boch online store, the term “was” is entered, and as soon as the first letter is typed, a preview window opens with suggestions. Various products are displayed on the right side, which immediately lead to the product details page. In the preview window on the left side, the corresponding suggestions (i.e., categories) for the search term appear. From here, the user can navigate directly to the results list for “wassergläser” for “water glasses” or “waschbecken” for “sink”. Again, the path leads to the product details page.
When the term “keram” is entered into the intelligent search, this indicates the material “Keramik” for Villeroy & Boch. On the right side, a selection of products containing this material appears. The material “Keramik” is displayed on the left. The online shopper may click it and learn more about it, or they can access a results list with products made from this material. This demonstrates how content pieces can also be integrated into the intelligent search. Placing content pieces in the autosuggest preview window with text and a preview image is also possible, along with a direct link to the content piece.
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 catalog must be labeled and weighted, and the search algorithm must be configured. Integrating the click-and-buy behavior 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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