Set the scene: you’re looking for a new item of clothing, so you navigate to your favorite online store. You hop up to the search bar to type in what you’re looking for. What results do you expect?
Highly accurate, relevant, and instantaneous results.
Customers expect high-quality results no matter what website they’re searching on, what they’re looking for, and even when they’ve made a typo or use the wrong wording.
Search relevance is the ability of your website or app’s search bar to be able to return high-quality, desirable results to customers. And, with the support of AI, you can get that kind of search relevance for your company.
The Importance of Search Relevance
Most companies are familiar with search relevance based on keywords. Whether you’ve seen it while searching for something online or have seen it from the back end for marketing and creating content for users to find, you know how important keywords are for finding and creating relevant content.
Optimizing for search relevance is critical for online retailers and organizations with large amounts of content or data on their website. A report from Forrester found that over 40 percent of customers go directly to the search bar after navigating to a website. Customers want to find the information they’re looking for and want to do it quickly. Your internal search engine is one of the first impressions customers get of your website.
And, if your search bar can’t return relevant results, customers are likely to bounce from your website and leave frustrated, looking for a new solution.
Currently, many companies rely on keywords to direct their search relevance. And, so far, keywords work. They make it so that we can find the content we need. Most of the time.
But, can we do it even better?
AI-Powered Search Relevance
This is where AI-powered search relevance comes in. AI and machine learning techniques build more accurate, cutting-edge models that improve search relevance and make it even smarter.
AI-powered search relevance uses statistical analysis to drive search results. As results get more complex and nuanced, so too must the technology powering the results.
AI-powered search includes:
- Category markers
- Searchable metadata
- Business priorities
- Geolocation of the user
- Searcher’s past behavior
- Ability to differentiate between low- and high-quality content
AI-powered search relevance algorithms are taking the user into account when returning results. These search bars personalize the results to the user, not just to the query.
AI-powered search relevance is based on algorithms that use smart parameters to return the results most relevant to the user who is searching. When the computer can automatically tune its own parameters to get even better at returning high-quality search results, this is known as Learn to Rank or LTR.
But, AI-powered search relevance algorithms are only as good as your training data.
You Need the Right AI Training Data
Teaching a search engine how to understand and interpret queries, especially those that include variations of natural language and spelling errors or typos requires massive amounts of training data. The more and better training data you have, the better your algorithm and search relevance will be.
Getting the right, high-quality training data can be a frustrating hurdle, especially for small- or medium-sized companies. There’s no need to be discouraged.
Appen works with companies of all sizes to develop high-quality training data sets and to build a search bar that returns the right results for customers. Shotzr worked with Appen to hone their training data by identifying 17,000 images that didn’t need labeling. This allowed them to focus on the images that need a label, which will improve their search relevance for stock photos.
You Must Continuously Update Your Training Data
Once your AI-powered algorithm is built and trained, it’s done, right? Not quite. Training data must be continuously updated and checked to make sure it’s continuing to return the most relevant results to customers.
AI-powered search engines use natural language processing or NLP to understand and analyze search queries. To do this, it must be trained on millions of data points, use cases, and edge cases that run from vague to precise. A good algorithm will be able to return accurate search results, even when the query isn’t clear.
To harness the full power of NLP and AI-powered search relevance, training data must be continuously updated and optimized so your customers continue to get the best results.
What AI-Powered Search Relevance Can do For Your Company
User experience or UX is a common topic of conversation around any website design or redesign. But what about searching? Your search bar and search relevance need just as much care from the UX team. The stats prove it.
- 30 percent of website visitors want to use the search function. When they do use search, they’re two times as likely to convert.
- 79 percent of customers who search and don’t find what they’re looking for will leave the site and look for a different one.
- In a study by Econsultancy, they found that visitors using search contributed 13.8 percent of revenue.
Clearly, the quality of search results is an incredibly important factor to users and in turn, your business.
Create a Positive First Impression and Provide Good User Experience
When a customer comes to your website on a mission to find something, their first experience and impression of your company will be the search results. If your search returns high-quality, relevant search results, customers are more likely to stay and look at your products.
If you have poor search relevance algorithms and return inaccurate results, customers are likely to bounce and find what they need elsewhere.
Convert and Retain Customers
Good search relevance results in happy customers. Happy customers are more likely to buy your products and to come back to buy more products. Using AI-powered search relevance techniques is a simple way to convert new customers and retain current customers.
Returning high-quality results to customer queries also helps to move customers deeper into your website efficiently, keep them on your website longer, and gives you the chance to open a chatbot before they bounce from your website. All of these can end with making a sale or making a higher dollar sale to those customers.
Makes It Easy for Customers to Find Information They Need
At its simplest, AI-powered search relevance makes it simple for customers to find the information that they need. Anyone that’s ever submitted a search query only to be stymied by the low-quality results knows just how frustrating it is. Don’t do that to your customers.
Lowers Cost of Customer Service and Customer Acquisition
High-quality search results make it simple for customers to navigate your website. How this can affect your company is that it lowers the cost of customer acquisition. When customers can visit your website and find what they need, they don’t email, call, or chat with customer service or sales representatives. This saves your company time and money.
Easier Knowledge Transfer for Internal Employees
While the most common user of a website’s search bar is likely to be customers, the search bar might also be used by employee’s to find company information or product pages. Well-crafted, AI-powered search relevance can help employees to find what they need, which allows for more cost-effective knowledge transfer.
Search Relevance Evaluation
Search relevance can have big effects on your business and revenue. So, how do you know if it’s working or not?
One of the best and easiest ways to evaluate your website’s search is through human relevance evaluation. This process works by using a sample of search terms that you expect your website to handle well. Note the top results for those queries. Then, have humans rate those search results for accuracy and relevancy.
This evaluation method will help you to determine if your search is returning relevant results and where it needs to be improved.
Search relevance is one of the many ways you can provide your customer with the best online experience with your company. By optimizing your search relevance, you can not only meet your customer expectations, you can exceed them. And, at the same time, you benefit your company with higher profits and lower costs.
Expert Insight from Vasagi Kothandapani, Appen VP of Global Accounts
Today’s online users have very high expectations in terms of receiving accurate and relevant results similar to Google or Amazon. Whether you search on Google, Amazon or any other well-established e-commerce website, you have multiple options when it comes to finding information, comparing products and finalizing the purchases. Relevancy helps to improve customer conversion and it is necessary to measure it. To measure the quality of your search we need to look at the relevance of the results.
Relevance has remained a critical underlying criterion for evaluating the efficiency of the search application. When your users make searches to find products on your website, the idea is to show results as close as to what they have in mind. The relevancy is measured primarily in two metrics: recall and precision. Optimizing both these parameters can maximize your conversions.
Recall – Recall refers to the ability of the search engine to return “all” relevant results.
Precision – Precision refers to the ability of the search engine to return only those products that are distinguished as relevant.
Optimizing an application/website’s search relevancy is a complex and ongoing process. It requires not only providing results that match users’ queries, but also providing personalized results and meeting specific business needs. Further, as users move more toward voice enabled devices and digital assistants, businesses will have to figure out how to provide a new type of interface that can speak naturally with users.
To improve relevance, engineers have been working to build in more personalization and contextualization. This includes using machine learning and natural language processing to enable more conversational search and the core of all this would be relevant training data including text, audio and video depending on the type of application.
What Appen Can Do For You
Our search relevance expertise spans over 20 years. We’ve used that time to successfully support our clients with high-quality training data for their unique search needs. Whether it’s helping Adobe Stock improve their search relevance or working with your team to ramp up in new markets, we are here to help you achieve quick delivery and scalability of your AI-powered search relevance models.
Learn more about our expertise and how we can help with your specific search relevance needs.