“Search relevance” is crucial to many businesses’ success. It measures how closely a company’s internal search engine results meet its users’ needs and intentions. Offering the right search results can be the difference between making a sale and losing a customer. Using AI data services, a company can ensure its internal search engine offers search results that are relevant to users based on users’:

• Keywords: words they use in their query

• Context: where the user lives, their device, search history, etc.

• Intent: what service or product the user is seeking

Search relevance is also an evaluation of the content offered in search results, including its:

• Quality: the depth, accuracy, or originality

• Freshness: how recently it was created

• Reactions: how the user interacts with it (click-through rates, etc.)

Read our blog to understand the specific ways search relevance impacts customers, plus best practices for using AI data services to improve it.

How Search Relevance Impacts Customers

Search relevance isn’t just a “nice-to-have” anymore. This is due to a few factors. Firstly, user experience is vital in the customer journey. Many websites are now routinely augmenting and auditing their user experience to win on SEO. Other websites should follow suit to be competitive, even if they don’t want to rank high in Google or Bing’s search results. Search relevance is a notable factor in user experience. Potential customers want to easily navigate a website and find the information, products, etc., they seek. They simply expect excellent search relevance, whether consciously or not. With so many website options to visit online, it’s too easy to lose a visitor and possible customer due to suboptimal search relevance.

The second way search relevance impacts customers is regarding what they buy. With high search relevance, a customer is served results that match exactly what they are seeking. Or, perhaps they’re also served results that might be unexpected, but equally appealing. In both cases, the customer is more likely to make a purchase (or two or three). Search relevance can be key to enticing a sale (and maybe future sales).

Tactic 2: Find a vendor for data annotation. Data annotation labels data to help train and refine search engines’ algorithms. These services help train continuously, so search relevance stays strong for users. These are the areas in which human annotators help LLMs offer better search relevance:

• Identifying a user’s intent in search queries

• Evaluating search results for relevance and quality

• Boosting natural language processing capabilities

• Sharpening entity recognition, including people, places, organizations, etc.

• Tailoring search results for a user’s behavior, preferences, and demographic

• Flagging low-quality, irrelevant, and duplicative results

• Training machine learning models on relevance of search results, keywords, relevant topics, etc.

Tactic 3: Work with AI data services experts. Working with AI training experts, like the ones at Lionbridge, is critical to providing customers and potential customers with the best search relevance. We’re already helping industry giants worldwide to audit and enhance their search relevance. We’re not just recent AI solutions experts. We’ve been working with AI’s predecessors for decades. We also developed our AI trust framework to ensure our services are consistently reliable, high quality, and transparent.

Get in touch

Are you ready to explore AI data services that help you offer exceptional search relevance to your customers? Want AI solutions with real ROI? We use innovative technology and our deep expertise to assist global industry giants who want to use AI to meet and exceed their content goals — faster. Let’s get in touch.