It is no coincidence that businesses are increasingly investing in site search. The benefits of a relevant and efficient search engine are undeniable.

On the customer side, a successful site search will return accurate results quickly, saving customers’ time and leading to the final steps of a purchasing process.

Up to 30% of visitors to a website use the site search box, and those who use site search are presumably more likely to buy something compared to those who just use standard website navigation, because they already have the intention of what to buy.

On the business side, site search is a huge source of shoppers' behavioral insights, giving the company valuable analytics data. This information, if used successfully, can increase the conversion rate and customer retention.

Let’s have a look at why site search relevance is important to your business and how Boost’s algorithm helps you to increase the efficiency of your site’s functionality.

What is Site Search Relevance?
Site search relevance refers to the way the results match what customers are looking for when they are searching on your website. Can your users search for exact results easily and quickly? Does your site search provide what your user needs? If your customers can find what they are looking for, you have created a successful search engine.

When it comes to evaluating the effectiveness of your site search, relevance is a vital factor. The concept of site search relevance is to show the results that are closest to the searchers' desire. There are two metrics which measure the relevance of your site search: recall and precision. Focusing on both metrics can help you maximize conversion rates.

Recall implies the proportion of relevant search results the search engine returns among the total relevant results. For example, 20 products are qualified to be displayed on the search results page if customers run a query “top hat", but only 11 are shown. The recall rate we have is 11 out of 20, or 55%.
Precision means the proportion of only relevant search results the search engine returns among the total search results retrieved. For example, of 11 results displayed for the search term “top hat”, 8 products are top hats and 3 are classic hats. Then the precision rate is 8 out of 11, or 72.7%. The inefficient Natural Language Processing (NLP) capabilities may be one of the main reasons for low precision rate.
These two metrics seem similar at first, but we hope these above examples will help you imagine how different they are. The most ideal case is when both recall and precision rates are 100%, but of course, it will not happen. In fact, precision is often at odds with recall, and vice versa. If you want to improve recall, then the search requirements will be loose, the search engine will return more erroneous results, thus decreasing precision.

On the flip side, if precision is high, then the search requirements will be tighter, your search results will eliminate well-matched ones, thus lowering recall.

For this reason, we suggest making improvements quantitatively by trying running searches on your site manually, so that you can measure the values and get a general metric for precision and recall.

If you do not want to optimize your search function manually, machine learning will help you by optimizing itself automatically based on real-time usage data. Optimizing search results with machine learning can help increase conversion rates because it refines search result relevance, thus improving recall and precision rates.

The goal of e-commerce businesses is to provide what customers need, and improve their retention. One of the essential steps in achieving is to optimize customers’ interactions with your site search so they can find exactly what they want.

Why is Search Relevance important to your website?
The search function on your website is often the first interaction between a potential customer and your online business. Therefore, you need to satisfy your customers right from the strong site search.

Making a successful and effective e-commerce site search depends a lot on the relevance. When your customers search for products on your e-store, it is essential to show results most related to what’s in their minds. If they get unsuitable results, they will be dissatisfied and less likely to come back to your website. Without relevance, search is just a mindless tool that gives back results containing search keywords.

Research shows that conversion rates through site search can reach 50% higher than average. If consumers have a bad experience with site search, like not getting the results as they want and wasting too much time waiting for the results to display, they will switch to other competitors who offer a better experience.

Here is another statistic: on average, over 96% of visitors will leave your website without purchasing anything. If you want to raise this super low rate, start with improving your site search relevance.

Boost’s algorithm to increase Search Relevance
Boost’s search engine analyses the information and returns the products, collections, pages, and blog posts that best match search terms that users enter. The search results mainly depend on the Field area and the Minimum percentage a search term must match.

Field area
Boost’s search engine looks for matched products in the fields of:

• Product Title
  Product Type
  Vendor
  Tag
  Product options
  Product SKU
  Product Description

By default, the Description is turned off. You can disable a specific search field or change the priorities of the fields to your preferred priority order.

Boost’s features to enhance your Search Relevance
We have always updated features of Boost's Product Filter & Search to optimize your shoppers' searches, so it returns products that not only match the queries, but also match the searchers' intention. The following are some recently released enhancements that you should try to leverage search relevance.

For more details about our search algorithm, please visit https://apps.shopify.com/product-filter-search

It is no coincidence that businesses are increasingly investing in site search. The benefits of a relevant and efficient search engine are undeniable.

On the customer side, a successful site search will return accurate results quickly, saving customers’ time and leading to the final steps of a purchasing process.

Up to 30% of visitors to a website use the site search box, and those who use site search are presumably more likely to buy something compared to those who just use standard website navigation, because they already have the intention of what to buy.

On the business side, site search is a huge source of shoppers' behavioral insights, giving the company valuable analytics data. This information, if used successfully, can increase the conversion rate and customer retention.

Let’s have a look at why site search relevance is important to your business and how Boost’s algorithm helps you to increase the efficiency of your site’s functionality.

What is Site Search Relevance?
Site search relevance refers to the way the results match what customers are looking for when they are searching on your website. Can your users search for exact results easily and quickly? Does your site search provide what your user needs? If your customers can find what they are looking for, you have created a successful search engine.

When it comes to evaluating the effectiveness of your site search, relevance is a vital factor. The concept of site search relevance is to show the results that are closest to the searchers' desire. There are two metrics which measure the relevance of your site search: recall and precision. Focusing on both metrics can help you maximize conversion rates.

Recall implies the proportion of relevant search results the search engine returns among the total relevant results. For example, 20 products are qualified to be displayed on the search results page if customers run a query “top hat", but only 11 are shown. The recall rate we have is 11 out of 20, or 55%.
Precision means the proportion of only relevant search results the search engine returns among the total search results retrieved. For example, of 11 results displayed for the search term “top hat”, 8 products are top hats and 3 are classic hats. Then the precision rate is 8 out of 11, or 72.7%. The inefficient Natural Language Processing (NLP) capabilities may be one of the main reasons for low precision rate.
These two metrics seem similar at first, but we hope these above examples will help you imagine how different they are. The most ideal case is when both recall and precision rates are 100%, but of course, it will not happen. In fact, precision is often at odds with recall, and vice versa. If you want to improve recall, then the search requirements will be loose, the search engine will return more erroneous results, thus decreasing precision.

On the flip side, if precision is high, then the search requirements will be tighter, your search results will eliminate well-matched ones, thus lowering recall.

For this reason, we suggest making improvements quantitatively by trying running searches on your site manually, so that you can measure the values and get a general metric for precision and recall.

If you do not want to optimize your search function manually, machine learning will help you by optimizing itself automatically based on real-time usage data. Optimizing search results with machine learning can help increase conversion rates because it refines search result relevance, thus improving recall and precision rates.

The goal of e-commerce businesses is to provide what customers need, and improve their retention. One of the essential steps in achieving is to optimize customers’ interactions with your site search so they can find exactly what they want.

Why is Search Relevance important to your website?
The search function on your website is often the first interaction between a potential customer and your online business. Therefore, you need to satisfy your customers right from the strong site search.

Making a successful and effective e-commerce site search depends a lot on the relevance. When your customers search for products on your e-store, it is essential to show results most related to what’s in their minds. If they get unsuitable results, they will be dissatisfied and less likely to come back to your website. Without relevance, search is just a mindless tool that gives back results containing search keywords.

Research shows that conversion rates through site search can reach 50% higher than average. If consumers have a bad experience with site search, like not getting the results as they want and wasting too much time waiting for the results to display, they will switch to other competitors who offer a better experience.

Here is another statistic: on average, over 96% of visitors will leave your website without purchasing anything. If you want to raise this super low rate, start with improving your site search relevance.

Boost’s algorithm to increase Search Relevance
Boost’s search engine analyses the information and returns the products, collections, pages, and blog posts that best match search terms that users enter. The search results mainly depend on the Field area and the Minimum percentage a search term must match.

Field area
Boost’s search engine looks for matched products in the fields of:

• Product Title
  Product Type
  Vendor
  Tag
  Product options
  Product SKU
  Product Description

By default, the Description is turned off. You can disable a specific search field or change the priorities of the fields to your preferred priority order.

Boost’s features to enhance your Search Relevance
We have always updated features of Boost's Product Filter & Search to optimize your shoppers' searches, so it returns products that not only match the queries, but also match the searchers' intention. The following are some recently released enhancements that you should try to leverage search relevance.

For more details about our search algorithm, please visit https://apps.shopify.com/product-filter-search