eCommerce site search: Solutions, Statistics and best Practices

Site search is a must-have feature in any website, especially an eCommerce website. eCommerce site search helps users easily find their wished products. A well-functioned eCommerce site search engine may help businesses their raise conversion rate.

eCommerce site search solutions

An eCommerce site search is a search engine that is either built-in or integrated into your online store, and its job is to find the products the user is looking for by matching the user’s search keywords with the products in your store. Users can easily search their target products through text, voice, and scan code, etc.

The eCommerce site search platforms provide two search methods. One is to use the search box to search directly in the text, and the other is to search based on product categories.

eCommerce site search engine method

Types of eCommerce search engines

Quick whale e-commerce editor checked the eCommerce homepage of eBay, Etsy, Amazon and found that the search engines of such e-commerce platforms are similar. They all provide two search methods. One is to use the search box to search directly, and the other is to search by product.

The mainstream query term of the search box is still text-based. This is because most of the content crawled and indexed by search engines is also organized in text. Taobao and Jingdong’s Query has been extended to pictures, and you can upload pictures to search for the same item.

In addition, we see that there are hot search terms below or suggested in the search box. This setting has two purposes: one is to reduce user input operations; the other is generating demands for customers.

Category joint search has also been added to the search box. Taobao is a general screening of product sources, while Amazon and Dangdang are specific to personalized categories, which can be fully matched with category keywords, combined keywords and category double search.

When entering keywords in the search box, the system will match a query list, or some classification suggestions, so that users can provide accurate queries and classification ranges to the retrieval system, reducing the number of repeated searches by users.

Additionally, users also search by product category on eCommerce websites. When it comes to categories, it is necessary to involve the category attribute system. Generally speaking, the category system is divided into a front-end category system and a back-end category system.

Query search and category search of eCommerce site search engines are often accompanied by filtering functions

Generally, when shopping on the website, we search for a keyword, such as “towel”, and then all relevant brand, material and other classification choices will be presented to us, and the search scope can be narrowed according to the necessary conditions.

The filtering methods include classification filtering, label filtering, price range filtering, geographic filtering, inventory filtering, and whether it is self-operated. Also, the eCommerce site search engine supports sorting in various dimensions, including the sorting of attributes such as sales volume, credit, and price, and supports a wider range of searches.

On the filtering page, there is still a search box. When scrolling to view products, the search area will float on the page. Compare Etsy, eBay and Amazon. Do you feel familiar with the location of the search box and category? Right? Yes, you thought of it, that is the “F-type” layout and “heat map”.

According to the predictable behavior of users browsing the web, allowing users to quickly lock search engines within a few seconds, which shows how important search engines are on eCommerce platforms.

Ecommerce Site Search Best Practices

Smart autocomplete suggestions

An eCommerce site search suggestion method includes the following steps:

• Build query suggestion index:

Preferably, the method for constructing the query suggestion index in this step is:

+ Get relevant keywords;

+ Assign an ID set to similar keywords according to the category of the keywords, and assign an ID value to each keyword in the ID set of the same keywords;

+ Establish index data for each ID value and store it. The priority calling principle of each ID value is called sequentially according to the popularity of the keywords represented by the ID value from high to low.2. Filter invalid query suggestions;

• For the query suggestions filtered in step 2, sort them according to the popularity of each query suggestion from high to low;
• Output query suggestion sort results.

Show the search box more clearly

The search box is a combination of the input area and the submit button. Some people may think that the search box design is not important. But because the search box has become the most used design element in content-based websites, its usability has become especially important. The most important rule in search box design is to make it discoverable. If the search is an important feature in your application or website, it should be prominent enough because it is the fastest way to discover content.

Allows for errors and autocorrect

Perhaps the most serious problem in the search function is that there is no automatic correction. The more users need to re-enter their queries, especially on mobile devices, the more likely they are to give up and find their answers on another website. Therefore, you should allow your search function to detect common text errors in search queries and let it automatically correct them. Whenever possible, provide as few zero-result pages as possible, and automatic correction can well limit the appearance of empty result pages. Remember, our goal is to make the user experience journey between searching and getting answers as easy as possible.

Use machine learning to deliver personalized results and solve practical problems.

Identify similarities between words in search queries

Machine learning can not only use query data to identify and personalize users’ subsequent queries, but it also helps create data patterns that can form search results for other users. Google Trends is a good example. A phrase or word that does not mean anything at first (for example: “planking” or “it’s lit”) may produce meaningless search results. However, as time goes by, its wording increasingly improves, and machine learning can show more accurate information. With the development and change of language, machines can better predict the meaning behind what we say and provide us with better information.

Improve the ad quality and target users

According to the eCommerce site search statistics of Google’s US patents US20070156887 and US9773256, we can use machine learning to improve “other weak statistical models”. This means that machine learning systems directly affect ad ranking. “Bid amount, the advertising quality of your auction time (including the expected click-through rate, ad relevance and landing page experience), the threshold of the ad level, and the context of personnel search” Enter the system verbatim through keywords to determine Google for each The threshold for keyword consideration.

Synonym recognition

When you see search results that do not contain keywords in the code snippets, it may be because Google uses RankBrain to identify synonyms. When you are studying for a doctorate, you will see various “doctor” or “doctoral” results because they are interchangeable to many degrees.

Magenest — Your best eCommerce customization partner

For the eCommerce site search function is crucial in any online store, business owners need to make sure that it functions correctly in your website. However, the default search engine may not be fully-functioned. So, it’s time for you to find some optimization and customization solutions! Magenest is honored to be one of the best Solution Partners of Magento. With a tailored eCommerce one-stop solution, we will empower your eCommerce success.

Source: https://magenest.com/en/ecommerce-site-search/

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