The applications of Semantic Search in different industries

9 min readJun 14, 2024

The Semantic Search tool offers a whole new degree of convenience and delight to your customer base as well as your own enterprise management. Eliminate the need to scroll through pointless search results or waste time on ineffective searches. Semantic Search guarantees that consumers will find products that properly fit their intent with ease, and you as a business owner can keep track of the organizational processes of your erprise.

A wide range of industries can benefit using the semantic search engine. Convenience for the users, more personalizing of search results and tracking of the quarries helps this technology applicable to a wide range of industries.

E Commerce

Any E-commerce business has to have a search engine in order for its customers to find and order their desired products easily. In the initial days, this was done by using keyword search, but it is not very helpful for easy and smooth user experience. For the keyword search to work, a customer needs to know the exact name of the product otherwise the search engine can not identify the order and show the product to the customer. In reality people look for their desired product by searching it with similar languages and synonyms and this natural language quarry must be understood by your search engine and it should provide the users’ prompt with convenience.

Semantic Search improves the following aspects of E commerce businesses:

Increasing customer satisfaction: Semantic searches provide customers with more precise results matching the prompt. Which keeps the customer engaged with your site and improves satisfaction.
Enhancing user experiences: By keeping track of customer’s searches through its indexing algorithm, semantic search engines can accurately suggest similar products to customer’s needs. A personalized approach is taken by semantic search, as the algorithm follows trends and search pattern, it can easily decide based on the data and recommend word searches, autocomplete search prompts, and provide suggestions based on the previous purchases.
Automating the product listings: Updating your own product line, and optimizing keyword searches are time consuming and costly, by integrating semantic searches you can easily update the catalog of your products, as the AI can sort out the similarities between product listings and segment them for you.
Increasing sales: A smooth and easy search helps the customer stay engaged with the website, as the customer finds the desired product quickly he or she is more likely to browse around for other products. Semantic search performs frictionless search journeys where users can find what they’re looking for with ease and become more purchase oriented.
Building a solid customer base: Semantic search improves on learning as it uses machine learning. It has key performance indicators such as conversion rates, and bounce rates. When users find what they need quickly and easily, they’re less likely to bounce off your site and go elsewhere. Semantic search helps users get to the information they desire faster, reducing bounce rates and improving overall user experience.

Real Estate

Your real estate company might be bogged with large amounts of customers listings, eligibility form, and credit lines for assessment. Which can be time consuming and take valuable time out of your other important tasks. Realtors often spend a significant amount of time sifting through client inquiries and weeding out unqualified leads. There are also significant issues when clients find difficulties sniffing through your listings, as in most cases they search involved broad concepts, and not specific names of apartment criterias. That is why it is vital for your search engine to interpret the prompts by understanding the customer’s needs as broadly as possible.

Semantic search can keep your customers happy and help you smoothen your internal processes in the following ways:

Getting more precise search results: Traditional keyword search is highly ineffective for your customers. A client might search “affordable apartment” quarry and may be shown apartments with high rent just because “luxury apartments” share the same word. That is why semantic search is important which can categorize the apartment prices in accordance to the price range your company offers. That makes the search more precise and engaging to the customer.
Saving time on finding the right client for the right condo: The customer’s credit listings, and the options you provide are hard to match manually. It requires hours of careful assessment and consideration. Using semantic search you can easily automate your match making processes, and build a more efficient process for you and your customers, to show the right apartment to the right client based on credit and income.
Sifting through data: Maintaining and evaluating data regarding the clients, apartments, the locations, certain regulations, new potential listings etc. can be challenging to a retailer. All of these different aspects can be linked through semantic search, making the retailer more knowledgeable about different sectors and the relations they share, this is time saving and helps to make correct decisions.
Ranking based on demand: Display and upsell your most demandable locations and update the pricing in relation to bids. This up to date method can ensure your company gets the best prize and also boosts your client’s participation as they can see which condos or apartments are more trendy and desirable.

Healthcare

Any healthcare provider or insurance company might have vast data on its customer base, but without the ability to extract meaningful links between different data, this knowledge base is wasted. Whether deciding on an insurance premium, or managing customer requirements, semantic search can be crucial for your company in sorting through patients and determining the pricing for your customers.

Semantic search can fast-track your decision making through,

Matching premiums based on customers’ records: Semantic search makes the task of medical records and the customer’s income more easier. The large amounts of insurance history of the customer and the pricing options can be weighed and matched based on your set requirements.
Extracting relevant data: Semantic search can explore through large amounts of data and show you only the relevant information. Because semantic searches can understand the context in relation to keywords, it can sort out irrelevant information which saves time for the insurance provider.
Providing better customer experience: When searching for insurance plans, customers can cross examine different plans your company provides, by giving their own affordable range of insurance plans. You as a provider can also benefit by directly observing the desired price range searched by the customer.
Improving satisfaction by giving relevant information: A customer can have questions regarding some medical terminology, but might not know the accurate prompt to search with. Semantic search can easily give the proper search result to the customer and improve satisfaction by processing natural language quarries.

Fin-Tech and Banking

Through understanding users’ data, creating a context based recommendation option, and by analyzing the customer’s trend, any Financial company can make better decisions using semantic search.

Semantic search solves a whole array of problems by helping your organization analyze and decide on your next steps.

Adjusting prices: Semantic search can maintain an up to date information regarding stock prices, exchanges rates etc. and show you a whole picture that may have otherwise taken you a huge time to process.
Following market trends: The Ai based platform can keep track of demand shifts as well as market shifts, whether the market shifts upwards or downwards, semantic search engines can keep you informed through its capabilities of understanding vast amounts of interrelated data.
Managing customer base: Semantic search can be used to offer optimized prices for different customers. Not all customers’ patterns are the same, and by analyzing the behavior patterns, semantic search can deliver you a personalized option for each of your customers.
Keeping track of your own finances: Semantic Ai can be effectively used for bookkeeping. Semantic AI eliminates human error in record keeping and can give decisions by understanding large amounts of data to help you make the right decision.
Ensuring security: Semantic search can be used to analyze vast amounts of financial data and identify patterns that might indicate fraudulent activity. For example, it can analyze transactions and communication patterns to detect anomalies that might be indicative of money laundering or identity theft.

Edu-Tech

Customers often struggle keeping up with static video courses and assignments. As there is little chance for interactiveness, many learners simply give up on their learning path. There is also a problem in designing generalized learning paths for students with different levels of abilities. A learned quarry may be multidimensional and a traditional keyword search may struggle to provide the learner with the proper material he desired.

Semantic searches can improve the learners’ experience in navigating your site and attract potential customers by creating a personalized touch for each of the learners using the following processes:

Providing relevant study materials: The indexing algorithm can identify different subjects and the links they share, and provide the relevant searches inquired by the customer. Semantic search can also interpret the natural language queries thus giving students the option to search for their material using different relevant prompts.
Ranking demandable courses: Semantic search can display and promote courses that are popular based on students’ admissions. This can ensure more engagement of the customer base and better marketing opportunities.
Creating personalized learning paths: Semantic search can overcome the traditional fixed learning path suggestions shown to learners. Any student can address their personalized lackings in various subjects, and using that data semantic search engines can create a personalized learning path that can cast a wide net for each student.
Giving Assessment and Feedback: Semantic search can analyze a student’s responses to quizzes and assignments to understand their thought process and identify areas for improvement. This allows educators of your platform to provide more targeted feedback and tailor their teaching methods to address individual student needs.
Tracking and adjustable pricing: Semantic search can be used to track the pricing of each course, based on enrollment, seasonality, and admission patterns. Your platform may have a certain influx of students at particular times of the year, and you can easily use semantic AI to determine the best price range where you want to sell your courses.

Internal Management of Enterprise

Semantic search not only focuses on the customer end, but can be used to streamline your own business operations. By making information more retrievable and making knowledge sharing more efficient among different departments, semantic search is a truly effective operational tool.

Using the following functionalities, you can efficiently run your businesses:

Surfacing relevant internal data: Semantic search can be used by employees to look up all relevant data concerning a particular product, such as delivery time, order date, purchase price, expired date, inventory location etc. In traditional organizations these data belonged to different departments, but using semantic search all important information regarding a product can be sourced and found using a basic search, saving effort and time.
Overcoming unstructured data: Semantic search can find data regardless of formatting and placement. As data is not optimized and belongs to different departments, it is hard to find relevant data using traditional keyword searches. This is solved by the vector embeddings used in semantic search which can easily incorporate different data available in different formats.
Prioritizing tasks: The indexing algorithm can be used to prioritize the tasks to be completed. The vector embeddings of the data can be used to understand the relevance among tasks and employees can easily assess which tasks to perform first. Saving time in meetings to make those decisions.
Analyzing HR operations: Semantic search can be used in assessing employee profiles, salaries, and track records surfing through large amounts of data in a more efficient manner. Which improves decision-making and managing your company staff.

To sum up, semantic search can be used in any sector where a large sum of data is to be interpreted and shared using a more intelligent and human friendly manner. Making the process more efficient for your users and for your own company.

Solution Overview

The improvements mentioned in the above industries are possible by semantic search because of the following abilities:

Intelligent search outputs: Users can submit search queries using words, similar phrases, or sample documents using semantic search, and the results are sorted according to semantic similarity. Finding bits of information, deciphering user intent, and delivering pertinent search results are all made easier by it.
Knowledge extraction capabilities: Question answers are “extracted” from the text body in an extractive question answering method. Answers to the questions are “generated” using pre-existing instances of questions and appropriate responses in generative question answering. Searching for answers in a FAQ approach by using a database of previously asked and answered queries. Lookup in text-based internal systems, such as systems for finding court cases or financial reports (Document Search) is possible.
Indexing Algorithm: Semantic search facilitates the extraction of metadata from a variety of sources, including papers and images. It makes it possible to classify data according to its goal and contextual significance, which makes managing and organizing unstructured data more effective.
Metadata Filtering: Metadata filters are used in searches to precisely determine how many nearest-neighbor results fit the criteria. Search latency will typically be even lower than that of unfiltered searches, providing a more focused search experience.
Advance data sorting: In addition to searching data semantically, a semantic search engine with a page ranking algorithm may successfully re rank search results and will make every effort to present users with the most relevant web results. The outcome of the semantic web combined with user attention time is the basis for the suggested algorithm for page ranking.
Prompt error managing capabilities: Spelling mistakes and query variations are handled by semantic search. Simple errors in phrasing are fixed using inclusion, omission, character exchange, and permutation automatically. improving search accuracy and user experience.