How do you ensure the best findability and discoverability across your digital experiences? Search tuning.
Search is a significant part of your user’s journey on your digital experience. In fact, Forrester research found that 43% of site visitors will use the search box immediately. Today’s fast-paced, experience-driven world means a positive digital experience is just as, if not more important, to users than in-person experiences with your brand. A positive digital experience can build trust, cultivate loyalty, and encourage users to stay on your site — and away from your competitors.
So, what makes for a positive digital experience? Well, we can start by looking at the other end of the spectrum. In Coveo’s 2023 Website Relevance Report, the most common response (76% of respondents) to the question “What makes for a poor digital experience?” was “not being able to find information on one’s own.”
This highlights how important accurate, relevant search results are to the overall digital experience. It also shows how frustrating it can be when users are unable to find what they are searching for easily.
Let’s dig into the why’s, what’s, and how’s of search tuning. And how a machine learning model can do the lion’s share of the work.
What Is Search Tuning?
Also known as search engine optimization, tuning your search engine means adjusting parameter settings to provide the best search experience for each user.
There are many aspects to search tuning, but overall, it requires fine-tuning your search algorithm to retrieve more relevant results based on keyword matching, user data and behavior, preferences, popularity, and more.
The process includes things like query processing, or analyzing user search queries to determine how your search algorithm identifies and extracts keywords, interprets user intent, and how syntax or characters are handled.
It’s also important to optimize indexing to ensure your search is pulling results from up-to-date indexes in an efficient way. And we can’t forget the very-important search bar experience. Search tuning also involves creating a good search experience from the start with things like autocomplete, spell check, easy facet and filter navigation, etc.
Ultimately, search tuning is all about making user searches easy, effective, and relevant.
What Is Relevance Tuning?
Perhaps one of the most important aspects of search tuning is relevance tuning. Relevance tuning is the specific optimization of the relevance of search results through both manual processes and machine learning.
Relevance tuning often relies on feedback from users or leveraging past user queries to optimize the relevance of search results. You can do this by refining relevance models/algorithms which assess the relevance of results based on keyword matching, user feedback, and context.
It’s also important to leverage user feedback or data to train your search algorithms. This could be explicit feedback (like user “was this helpful” ratings) or implicit feedback via user behaviors like clicks, views, etc.
It’s vital to incorporate personalization into the search experience based on user data like demographics, browsing history, and search history to ensure that the most relevant results are populated for each individual user.
With 87% of shoppers beginning their product searches online, the value of search optimization can’t be stressed enough. Search and relevance tuning for your customer-facing search will ultimately create a better digital experience with your brand, improve the customer experience, and create more opportunities to increase conversions on your site.
Through search optimization for your internal enterprise, it can increase employee satisfaction and result in more efficient internal business operations.
How Machine Learning Automates Search Tuning
The great thing about implementing AI and machine learning into your search engine is that it can tune search for you over time. For example, it can tune facet relevance to create a better search experience. Machine learning algorithms leverage queries and actions performed by previous users (like clicked results or facet selections) to make the most relevant facets appear at the top for a given query.
You sell clothing on your website, and when it launched, it displayed search facets in the following order:
After a while, it began applying insights from past customer behavior and determined that users most often sort their search results by size and brand.
The search page now displays facets in the following order:
Your algorithm can also learn to reorder parameter values to make the most popular facets appear at the top for user selection. To do so, the machine learning models use the search events performed by previous users who have selected certain facet values for a specific query. Additionally, algorithms can automatically select possible values according to the end-user query. They can learn from your end-users behaviors to understand which facet values are the most relevant according to their current browsing task. Then they’ll automatically select those when certain query criteria are met.
While this kind of machine learning is extremely valuable in creating a more relevant search experience for your users, it’s also worth noting that it’s still important to enable humans to adjust their search as needed to enable additional machine learning and to account for occasions where users queries don’t follow the typical user patterns.
Best Practices For Search Tuning
Manual search tuning and machine learning work together to provide the best results. By following these best practices, you can help your machine learning model improve results quickly and continue to create a more relevant user experience.
Offer an Intuitive UI: It’s important to design a user-friendly search experience where it’s simple to enter queries and navigate search results.
Leverage Autocomplete and Spell Check: Implementing spell correction will help with query performance.
Discern User Intent: Getting a better feel for what your users are really searching for is crucial in providing a better experience.
Include Business-Specific Thesaurus Entries: Thesaurus entries are valuable because they help machine learning algorithms understand the context of your business.
Limit Stop Words: Stop words are words you don’t want your search algorithm to pay attention to.
Include Helpful Feature Results: Feature results help to improve the visibility of new items, promote sale items, or promote content you want users to see.
Enable Partial Match: When dealing with keywords, you don’t want your search algorithm to only search for full keyword matches.
Adjust Ranking Weights: Results ranking is to adjust the weighting of certain ranking factors, including the tuning of existing ranking factors to help your algorithm pull more relevant results.
Optimize Ranking Algorithm/Model: Your ranking model influences a lot of results at once, so it’s important to optimize it to yield the most relevant results. Here are a few ways to do this:
- Use ranking rules for legitimate reasons
- Use ranking rules sparingly
- Make ranking rules just specific enough
Implement Personalization: This can help each individual user experience more relevant results.
A/B Testing: Any adjustment to search should be followed by validation.
Continuous Improvement: Search tuning is an ongoing process. It’s vital to always be monitoring and improving the search experience based on user behavior, performance metrics, and feedback.
How Relevant Is Your Search?
As you can see, much more goes into search than just leaning on the built-in search feature in any OOTB platform. You can extend and enhance the relevance of your user experience for many use cases by leveraging a unified index and machine learning.
Did you know that 92% of site visitors report having frustrating digital experiences? While the search tuning of those sites could be better, our 2023 Website Relevance Report details what visitors hate when it comes to site search — as well as elaborates on what they do want to see. Download a free copy today.