Users today are submerged in vast amounts of information, which makes finding the information they need complex. Search relevance measures the accuracy of information an individual requires vis-a-vis their search query and results. It’s not important to provide results but to provide results according to the user’s search intent. Hence, search relevance helps with making it easier and seamless for a user to get the required information. Search relevance is crucial for owners and search engine enablers to help their users to showcase the desired results.
Understanding Search Relevance
Search relevance may seem like a simple metric that measures the search results with the search queries. But there is an interplay of several variable factors that help determine the quality and degree of relevance.
Getting the relevance right is hard, but getting it right for a seamless and memorable search user experience. Moreover, even the users expect search engines or websites to completely understand their search intent in as few words as possible.
According to an Adsearch report, 68% of shoppers won’t come back to a website if they have a poor search experience.
The factors affecting search relevance include:
Search Intent
The ability to understand what the searcher wants to know. It’s important to analyze whether the search query is transactional, commercial, or informational.
Geolocation
This looks for the searcher’s location and whether the results provided are relevant to their search query.
Keyword Proximity
The closeness of a keyword to the search query is also an important factor. It helps determine the search’s context, which gets more important than individual words in the keyword.
While these are three crucial factors, others are the user’s historical data, the frequency of search, and the device from which the search query has emerged.
The Evolution of Search Relevance
Search relevance was not given importance in the days of primitive search engines. This was when a keyword match was based on the individual words, at a time when search relevance was at its nascent stage. However, when Google came into the picture, it changed the game by first introducing Page Rank.
Google changed the page ranking mechanism, which was earlier limited to content. It brought in factors like number of pages on the website, quality of content, links to the page, etc. As the modern search relevance was taking shape, Google’s algorithms were updated from time to time to refine their algorithms.
Fast forward to the present day, where Google and many other search engines are using artificial intelligence, natural language processing (NLP), and neural matching algorithms for a more detailed understanding of the user’s search terms.
Currently, Google is using BERT to understand the user’s search terms better and provide relevant results.
Technological Advances in Search Relevance
The modern-day search relevance system is driven by a host of amazing technologies and techniques.
Term Frequency-Inverse Document Frequency (TF-IDF)
TF-IDF is an old technique, but still relevant. It checks the importance of the terms in the document by identifying their frequency across multiple documents. As a result, the commonly used terms are differentiated from unique terms.
Machine Learning
Search engines have started to integrate machine learning models for providing relevant results to queries. These systems continue to update their models by analyzing patterns to refine the predictions and provide better results.
Semantic Search
With semantic search capabilities, search engines can decipher the meaning behind the search queries. Semantic search accounts for the context, synonyms, and the intent to provide the results.
Natural Language Processing
With NLP, search engines can better understand human language in its natural form. Search queries in the form of voice and conversations can help a search engine understand the complexities of an individual’s speech.
With the help of these technologies and systems, searching the web has become more interactive and easier.
Search Relevance in E-commerce
Search optimization is essential in eCommerce, especially with the diversity of products and services present today. With the number of online shoppers increasing by the day, search relevance is required for a buyer’s convenience and satisfactory user experience.
Product Range
Every category we have has multiple products, and search relevance helps ensure that the search query for polo shirts does not generate results for t-shirts, collar shirts, flannels, etc.
Purchase Behavior
eCommerce platforms have begun to understand the user’s intent, whether they are viewing products for buying or exploring their options.
Dynamic Pricing
Airlines give the best example of dynamic pricing, where the ticket price changes according to the seats available, routes, time of the year, etc. eCommerce platforms adopt the same pattern when the stock changes and prices along the supply chain fluctuate.
Personalization
eCommerce platforms are providing personalized product recommendations to their customers based on their previous purchases and buying behavior.
Search relevance has become a key distinguishing factor today as eCommerce platforms are evolving according to the user’s needs. With further development, we can expect search relevance to help eCommerce platforms generate even better results.
The Future of Search Relevance
Alexa, Siri, and Google Voice Assistant are among the next generation of developments in search relevance and how users interact with the web. These systems are the masters of comprehending natural speech and interacting like a person. But that’s not all; these voice-enabled systems have learned to understand the context of a customer’s query by comprehending the context, intent, and emotion. Going forward, we can expect a stronger merger of NLP and machine learning as the interaction with smart devices will become more seamless and intuitive.
Conclusion
Where search relevance is of paramount importance, data analysis is equally important for any type of website, search engine, or organization. With accurate data, you can fuel efficiency and accuracy in search relevance. Discover how Shaip can help organizations improve their capabilities with search with high-quality data analysis, sorting, and annotation services.
