Today, we live in the digital era, where a great deal of information is available at our disposal. Unfortunately, access to such a wealth of data is of no importance if you can’t find the information you are searching for at the right time. That is where intelligent search, a solution to inefficient search, comes into play. This post will explore what intelligent search is, how it works, and its importance for organisations.
The term intelligent search has lots of definitions. You have probably come across some of its definitions with highly technical terms that can be tricky to grasp. However, you don’t need to be a tech guru to understand what intelligent search means.
In simple terms, intelligent search refers to an AI-powered search that aims to figure out what the user wants and formulates the results based on the assumption. At first glance, that sounds like the regular ‘query and answer’ searches you make on your browser daily. However, intelligent search returns more accurate results by combining machine learning technology and elements of semantic vector search to create a better picture of the users’ goal when conducting a search.
For instance, when someone searches for ‘acrylic paints’ on a browser, there are many questions that arise: are they looking for a step-by-step guide or interested in online shops for acrylic paints. The traditional search, which relies on keywords, will give answers that are not necessarily clear or personalised towards the user’s goal. With intelligent search, the results are more precise since it leverages machine learning and understands human language.
Now that you understand what intelligent search is and how it is different from traditional searches, let’s look at how it works.
Intelligent search employs three primary technologies to provide accurate results to user’s queries. These include:
- Machine learning
- Semantic vector search
- Natural Language Processing (NLP)
Combining these technologies allows intelligent search solutions to better understand users’ goals and provide the best results. Although they work together toward one goal, the functions of NLP and machine learning slightly differ from that of semantic vector search. Let’s take a brief look at each technology and how it contributes to intelligent searches:
The primary purpose of machine learning in intelligent search is to make predictions and provide results tailored for the individual user. As we already know, machine learning continuously improves the quality of results over time. So, by leveraging this technology, intelligent search can predict the information more suited for a particular user based on their query.
The purpose of Natural Language Processing in intelligent learning is slightly similar to that of machine learning. Typically, NLP is used to analyse text, making it easier for machines to understand the natural human language. By leveraging NLP, intelligent search can better understand users’ queries, which is essential for generating relevant results.
As mentioned earlier, the function of semantic vector search is slightly different from the two other technologies. Its primary purpose is to encode queries and products in the same space by learning from buyers’ previous behaviours. Basically, semantic vector search allows intelligent search to display results according to what you mean rather than the keywords you type in the search box.
In a nutshell, intelligent search involves analysing queries, classifying them, and delivering more accurate and relevant results to the particular user.
Here are some of the benefits of intelligent search for organisations.
One of the benefits of using intelligent search is easy search and access to information. Most traditional search systems rely primarily on consumers’ understanding of how to search effectively to display the best results for the queries. That means users have to construct a more specific search to increase their chances of getting the information they are searching for.
With intelligent search, you do not have to worry about formulating structured searches since the technology employs natural language processing, which allows the machine to interpret the user’s natural expressions. Additionally, using machine learning enables the technology to continually learn from previous searches, which further improves results.
Another critical benefit of intelligent search is improved employee productivity. Traditional search solutions have been going out of the market over the past few years since they waste lots of time. As we all know, time is money, and every time spent trying to figure out the best query to access information costs the business lots of money.
Using intelligent search makes employees more productive since it significantly reduces the time it takes to access data sets. Additionally, it makes searches more user-friendly, encouraging faster completion of tasks. It no longer matters whether you are working with technical or non-technical personnel. With less time spent searching for information, employees can focus their energy on other important tasks, such as customer service.
Apart from these two, there are many other benefits of intelligent search.
Intelligent search is a new beginning designed to outperform traditional searches. The technology uses three primary technologies, machine learning, natural language processing and semantic vector search, to sift through enormous amounts of data and generate personalised results for each user. It offers a wide range of benefits, including increased productivity and easy access to information. If you haven’t replaced your traditional search systems, it isn’t too late to switch to intelligent search solutions today.