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.
Matching searchers with relevant content is what modern search engines are all about. A positive digital experience (one that prioritizes relevancy) builds trust, cultivates loyalty, and encourages 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 2024 CX Industry Report, the most common responses were too many choices (35%), site is slow or has errors (33%), and difficult to find what I wanted using search or browse (32%).
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, often due to poorly optimized search functionality or lack of enterprise search capabilities.
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 by leveraging techniques that include hyperparameter optimization and Bayesian optimization to improve model performance and deliver optimal search results.
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. This involves optimizing the search algorithm to maximize the cumulative gain, which is a measure of how many relevant results are returned for a given search term and how highly they are ranked. By fine-tuning the parameters, you can ensure that the most relevant result or document appears at the top of the search results.
There are many aspects to search tuning, but overall, it requires fine-tuning your search optimization 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 relevancy strategy includes 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. which can be achieved throughhyperparameter tuning and testing different parameter combinations.
Ultimately, search tuning is all about improving model performance to make user searches easy, effective, and relevant.
Relevant Reading: 6 Considerations For Replacing Your Enterprise Search Engine
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. This process involves hyperparameter tuning and finding the optimal parameter combinations to improve model performance.
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. This data can be used in machine learning research to refine the search space and improve the tuning process.
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.
Search optimization for your internal enterprise can increase employee satisfaction and result in more efficient internal business operations.
Relevant Reading: Case Study | Building a More Connected Way to Work at Dell Technologies
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.
Automating Search Tuning in Practice
Here’s what we mean – say you sell clothing on your website, and when machine learning was launched, it displayed facets in the search results in this order:
- Size
- Color
- Style
- Occasion
- Brand
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:
- Size
- Brand
- Color
- Style
- Occasion
Behavioral Data Tweaks the Model
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.
You can achieve this through hyperparameter optimization and Bayesian optimization techniques. These methods help pinpoint the optimal set of facet values that maximize a performance metric like search clickthrough rate (known as the objective function).
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 search query performance.
Relevant Reading: 7 UX Design Best Practices for Autocomplete Suggestions
Discern User Intent
Getting a better feel for what your users are really searching for is crucial in providing a better experience. Machine learning research can help with the data science process since it provides insight into user intent.
Include Business-Specific Thesaurus Entries
Thesaurus entries are valuable because they help machine learning algorithms understand the context of your business, improving semantic search capabilities.
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.
Relevant Reading: 5 Recommendation Categories Found in the Best Recommenders
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.
Relevant Reading: 10 Ecommerce Personalization Examples To Boost Profits in 2024 (+4 Benefits)
A/B Testing
Any adjustment to search should be followed by validation. A/B testing lets you compare different parameter combinations and hyperparameter values so you can see which configuration yields the best model performance and accuracy score.
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.
Relevant Reading: NPS Alternatives: How Search Analytics Add Insight
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.
Wondering what’s holding your site search back from leapfrogging the competition? Request a free site search assessment with one of our search experts!
Dig Deeper
Did you know that 91% of site visitors report having frustrating digital experiences? While the search tuning of those sites could be better, our 2024 CX Industry Report details what visitors hate when it comes to customer experience — as well as elaborates on what they do want to see. Download a free copy today.