3 Must-Have Semantic Search Use Cases

“Understanding Semantic Search” is an ongoing series. This is the third post in this series. In case you missed them, go back and read our first two articles “Understanding Semantic Search” and “The Evolution of Search Engines”.

Now that you understand the foundation of semantic search, it’s time to look at how to use it to maximize results. Enhancing information retrieval is critical to companies.

A study by Obrizum reported that about one fifth of workers are unable to locate job essential information at least once a day, with 62% spending over two hours searching and 28% spending three to four hours searching. This is unsurprising when only one third of companies centrally index their information so it’s easier to find. They concluded that enterprises waste £13,291.18 per UK employee or $31,754.20 per US employee annually due to time wasted looking for relevant information, reworking outdated resources, or bothering colleagues for help.

To avoid such staggering costs, we’ve outlined the three crucial ways you should integrate semantic search into your business. Then, we clarify the expected benefits of executing these implementations.

Glossary of Relevant Terms

• Generative AI (GenAI): This is a category of AI that produces content — text, images, audio, code — to try and copy human creativity. It uses datasets to study patterns and produce content based on this data in response to prompts (instructions).
• Retrieval Augmented Generation (RAG): RAG is the process of enhancing the outputs of a Large Language Model (LLM). This is done by dynamically adding data at query time to the LLM. As a result, the LLM has access to specific, accurate, and up-to-date information without needing retraining.

Semantic Search Use Cases for Enterprises

As we saw in the first article, similarity and keyword search each have their own pros and cons. One type of search is not universally better than the other. However, combining them together in a hybrid model opens a world of new possibilities. There are specific areas and workflows where companies can benefit from implementing such semantic search. We recommend starting with these three business uses.

Sales Enablement

Technical documentation is a key asset to SEO and brand awareness: thousands of pages of top-notch content with the right keywords ready to be indexed by Google and Bing. But technical documentation can play an even bigger role in enabling sales performance. Selling sophisticated products and services often rhymes with replying to requests for proposal (RFPs) and answering endless questionnaires about your company’s product capabilities, security, and miscellaneous compliance. It’s all the same, yet slightly different each time. Salespersons need to mobilize solution experts who get bored with this repetitive work.

However, the replies already exist in your content, your process description, and past answers to similar RFPs. With semantic search combined with the power of LLMs, you can automate the replies to these questionnaires: for each question, semantic search identifies the proper passages about the subject, and with a prompt as simple as “You are a product expert. Please reply to this question … using that knowledge…” you generate the proper answer. And voilà. You see how much faster and better you can automate replies to the same endless questions, empower your salespeople, remove tedious work, and ramp up newcomers.

Customer Support Ticket Resolution

High-performing semantic search engines are deeply beneficial for customer support operations. When a support agent has a complex case that requires research, traditionally they must manually search through support documents, knowledge bases, and past tickets to find pertinent information.

However, with a semantic search engine, the entire customer ticket becomes the query. A keyword search engine would be overwhelmed by the number of keywords in a lengthy problem description. However, similarity search divides the text into fragments which are then turned into embeddings. The vector coordinates of the embedding are compared with the existing vector database to pinpoint the most relevant troubleshooting guides, user manuals, FAQs, previously resolved tickets, or other data sources.

Companies can go further by combining the search engine with a RAG-enabled LLM. The LLM will provide results with trusted information from your enterprise knowledge base and deliver the answer as a conversational reply. Agents can then modify or validate the responses to ensure accuracy. Finally, once the answer is approved, the agents simply copy, paste, and send the information to the user. This expedites issue resolution, taking agents two minutes to do what would normally take ten minutes.

User Self-Service

Companies engage in customer self-service when they provide users with easy access to the tools and resources needed to autonomously solve their issues. The goal of self-service is to eliminate the systematic need to interact directly with the support team. By embedding semantic search into self-service options like customer portals, knowledge bases, FAQ pages, community forums, and GenAI chatbots, users can easily search for the information they need. Beyond a typical keyword search, semantic search options like chatbots that provide a conversational search experience are beneficial for users who don’t know what they are looking for. Sometimes users encounter a problem and can describe what’s happening, but they don’t know the source of the issue or what documentation is needed to resolve the problem. It may take several back-and-forth clarifications for the engine to determine the results they need, requiring semantic search to get the job done.

Benefits of Semantic Search

Employing a semantic search engine provides many benefits for internal teams and external users alike.

Improve Conversational and Natural Language Search

Imagine a support agent searches for answers using the text body of a help desk email. Or maybe a user isn’t sure what kind of documentation they need, so they type a long explanation into a self-service search bar. Either way, keyword search would not be ideal. Long, complex explanations with many words are too confusing for keyword search engines. They need search queries that contain minimal words. Conversely, similarity search improves search accuracy for the case studies presented because it correctly interprets the contextual meaning of the words a person is using for search.

The similarity search engine’s process of finding relevant documentation by comparing query embeddings to those in the vector database greatly enhances the search result accuracy for these use cases.

Enhance Customer Satisfaction

Customers like finding their own solutions and 81% of users want more tools to be able to do this. In order for these tools to effectively deflect help desk tickets and solve user challenges, they need to move towards a more conversational self-service. Semantic search allows users to launch complex search questions where they can explain the situation and give details like they would to a support agent. In return, they receive accurate, relevant information, wherever and whenever they need it. This results in faster, smoother problem resolution without needing to speak to a real support agent. When customers autonomously find quick and easy answers, they are more satisfied, reducing support burdens.

Increase Enterprise Productivity

When it’s difficult to find information, your teams waste precious time. Without a strong search engine, studies show that in 80% of cases in the UK and US, employees take up to eight attempts to find the information they need. Providing a semantic search engine majorly boosts team productivity. Its search capabilities allow employees to find documents quickly without knowing if they exist or how other teams label documentation in the company’s internal knowledge hub. As a result, employees spend less time searching for documentation and more time autonomously advancing on their daily tasks.

Conclusion

Semantic search makes finding information easy for customers and employees who were previously lost and unable to find the answers they needed. By implementing a semantic search engine into these three core use cases, your company will increase productivity, enhance the user experience, and boost accuracy for complex searches. With these recommendations in mind, all that’s left is to choose a solution! Fluid Topics’ advanced search engine provides relevant, personalized results across user touchpoints. Stay tuned for the series finale to learn more about our innovative search solution in “Fluid Topics: Your Semantic Search Solution for Relevant Results”.