Perspective

Internal Search: A hidden gem to improve ecommerce sales

Your internal search engine can be of high value, in this article we cover what it takes to get the setup right

While the value of external search optimization (SEO) is widely known, we find that the internal search engine on a site often is a hidden gem of value to ecommerce owners. With Medusa, we have broadened the option space for search making it possible to shift between two of the leading search API providers, MeiliSearch and Algolia, with only a few lines of code while also developing a Search API that makes it easy to integrate with the search provider of your choice. Now, why did we spend our time optimizing your search experience with Medusa?


First of all it is widely used by the most valuable customers. To give you an idea about internal search usage, 43 percent of website visitors prefer to immediately use internal search bars when visiting a website, according to Forrester Research. Besides that, they uncovered that customers are two or three times more likely to convert than users who spend time browsing different categories and products. 


Despite its importance, many ecommerce shops fail to make full use of the potential. A striking 25% of all internal search queries end up on a “zero results” page as internal search has not been optimized. This is worrying as an unsuccessful search experience leads 12% of customers to go to a competitor’s site. 


When looking for an internal search provider, you’ll find many options to choose from. There are significant differences in the number of configuration options you have, analytics offered, and search improvement options. We encourage you to make proper research before getting into this and understand what type of engine fits the best to your needs. In order to do this, you need to understand what a good search engine contains, which we will cover below.


What does a good search engine contain?

The key parameters

There are many parameters to look out for in this regard, but our top picks would be:


Changing ranking rules

Ranking rules can help you to improve the relevance of your search. They help you to customize what results show up first when a user enters a query. For instance, you can modify the results based on the number of matching words, word exactness, or proximity. For instance, proximity refers to showing products where the query intent occurs close together and in the same order. This ensures you can customize the search experience in the best possible way to your customer.


Typo tolerance

Users often make typos. An excellent internal search should match typos to the actual search intent. Most solutions use machine learning to detect typos and match the user’s search intent with the proper search. You want to avoid displaying an empty product page when the user makes a typo. Typo tolerance will surely increase the user experience and is an important parameter to test.


Search analytics

Having access to search analytics is not only important to adjust your search setup for a better user experience, it’s also valuable information to increase sales. For example, identifying on which searches users do not find any results can reveal what products users expect to find in your shop. The following section will explore different search metrics you can track and how to leverage them.


Other useful parameters

Other useful parameters to look into include synonyms, faceted search, machine learning improved results, A/B testing, and avoiding “no results” page.

Define synonyms

Often, multiple words have a similar meaning. For instance, users in different geo-locations might use different words to search for the same product. Instead of modifying your product description, it’s better to optimize your search by defining synonyms. 


Faceted search

Faceted search, or filtered search, is a powerful feature that allows users to refine queries using filters. For instance, when looking for a Spider-Man shirt, you don’t want to find books or videos about Spider-Man. A faceted search helps the user to narrow down their search query to a specific section (facet) of products like “clothing”. For ecommerce shops, it’s a must-have feature to define facets within your product data. For Medusa this can be done using “Categories”


Search improvements using machine learning-based AI

The use of machine learning-based AI solutions has been growing significantly. For that reason, it has also entered the internal search industry to help webshop owners to return more optimized product pages for search queries based on user behavior, search history, and sales history. In short, machine learning-based AI solutions help you detect new buying patterns and increase sales.


A/B testing

A/B testing is not a revolutionary feature. You can easily do it yourself, but why would you? Some search tools offer A/B testing functionality to make it easier to test different scenarios and visualize results. For instance, you can compare conversion rate and click-through rate for two scenarios. 


Avoid “no results” pages

Some internal search providers will help you to avoid “no results” pages. In case a user searches a query that returns no results, you should still return some results. An empty page is the worst that can happen in terms of user experience. Most often, a user will leave the website after being served a “no results” page. You can either return your best-selling products or return products with a low matching percentage for the user’s search intent.


Which search metrics should you measure?

An internal search provider will most likely provide you with a bunch of insights. But which metrics do really matter to improve sales and click-through rates:


Total searches across products

How often customers search for a product is a good indicator of buyer intent. It can help the merchant understand if they want to "move around" their priority products in the product grid or maybe change the front-page showcases. Likewise, the merchant can understand what "new products" customers are interested in.


Search match to conversion

This metric is a great combination between search matches and your conversion rate. The metric tells you how often a user buys a product after finding a matching product search result. A low conversion rate indicates that the top-most returned search results do not precisely match the user’s search intent. 

However, it does not always mean that you got the search intent wrong. It can also reveal incorrect product pricing. For instance, “search to match conversion” could be a great indicator that product pricing is not well-fitting with the customer’s expectations.


New search intentions

Make sure to not only track the most frequently occuring search intents but also look at the outliers and how they evolve over time. New search intents can reveal growing trends within your industry and provide you with new selling opportunities to meet customer needs.


No result searches

First of all, you should avoid a “no results” page at all costs. However, it’s a great metric to measure. To avoid “no result” pages, you can start experimenting with relevance, semantic, and synonym settings. A well-tuned internal search can convert an imperfect user query into a relevant search result.

Some potential fixes to look at when the number of “no result” pages spikes:

  1. Identify more synonyms for products by analyzing user queries
  2. Enable autocomplete search and offer query suggestions to help users find the right products faster.
  3. Make sure your internal search can handle typos. Most internal search providers handle this out of the box.


Search ranking clicks

An internal search should measure which result a user selects for each search query. You can use this data to change the search results order. For instance, MeiliSearch allows you to change ranking results based on several criteria like proximity, word matches, typos, attributes, and exactness.

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Conclusion

What should you remember? Internal search has become a big field for ecommerce shop owners. Sales and user experience can be drastically improved when fine-tuning search results. And as research shows, 43 percent of website visitors prefer to use internal search bars when visiting a website. 


There are many competing internal search providers on the market like Algolia and MeiliSearch that provide a wide range of functionality and configurations for ecommerce shop owners to improve their internal search. 


At Medusa, we do not only give you the chance to switch between solutions like MeiliSearch and Algolia out-of-the-box; you can also use our Search API to integrate with exactly the search engine you might be interested in for your store.


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