When customers type a query into any online retail store, they have high expectations that the search engine should provide accurate, relevant, and immediate results. Therefore, using Artificial Intelligence (AI), companies improve the search engine to understand each customer request and provide personalized recommendations even if the words entered are misspelled, poor grammar, or irrelevant words.  

Search Relevance Optimization

The optimization rate is dependent on the level of accuracy between the asked query and the search results. Higher accuracy means higher search relevance optimization. This level of accuracy plays a vital role for many big firms with robust websites. Customers can approach any business website by directly clicking on the search bar to look at company products. The search results create an impact on the customer's mind. If it provides the requested information and takes them to the desired outcome, it leads to sales. But if search engines fail to, it will lead to customer disappointment and business loss. A search engine with optimized search relevance produces high customer satisfaction, increased revenue, and sales, while an inefficient search engine can lead to customer frustration and negative feedback. 

Every industry proactively works to apply ML and AI to optimize search relevance to enhance online customer experience, improve customer satisfaction, generate more sales, retain and convert a prospect into a customer. Also, with Machine learning, a business can now quickly gain insight into customer behavior and search relevance as a parameter to improve search engines.

ML-Based Methods for Search Relevance Optimization

Natural Language Processing(NLP) is used for reading, interpreting, and analyzing queries. Therefore, it is vital to train the model on a vast amount of Natural Language Processing (NLP) data to improve search relevance. These must contain millions of use cases and edge cases from ambiguous to accurate.

Named Entity Recognition(NER), text analysis, semantic annotation, and data labeling are NLP techniques that help search engines analyze queries optimally. These techniques can use their rich in-built tools to parse texts and uncover their meaning. Then, search engines use these derived meanings to optimize the query results and mark them higher in the performance table.

Click tracking is also a commonly used technique by search relevance models. This technique works based on past queries. Therefore, it results in the most viable product based on stats. Mostly the input queries are in text format. But with the growing world image and voice searches are also in trend. A large amount of pictures and audio data is necessary for these searches, which increases the complexities in search engines. 

Tough Challenges in Improving Search Relevance

Even though the AI Search Relevance produces excellent search results, it also has its challenges. For instance, one customer might type in the search bar for "bed" and the other "mattress" product the same with different words, the result will be identical. Because each customer is uniquely different in searching and describing a product, it becomes challenging for the algorithm to learn and iteration all possibilities.

Natural Language Processing is the domain in which machines can learn, understand and interpret human language. A powerful and accurate search engine model must recognize "mattress" meaning and produce relevant results. The search engine should assume that when a customer types" mattress," it means the same as "bed". The engine must have the ability to interpret poor spelling and improper grammar and consider their prevalence. 

Hence, building a search engine requires a large amount of data that is not always easy to acquire, especially for medium to small firms. And not to mention the cost, human labor, time, and preparation can seem very overwhelming. Which can lead to discouragement and seem impossible when the necessary resources to complete the task might not be readily available. And that's why AI companies hire data providers like Indika to train datasets for better customer search experience.

How can Indika help?

Indika is a global data service company specializing in labeling all types of data across all industries. We have been working with some of the most advanced AI companies worldwide to provide data annotation/labeling services for all structured and unstructured data (such as text, images, videos, speech, music, etc.) 

We have been providing Data Tagging, Search Evaluation, and other such Search Relevance services to improve the search results for many companies. In addition, we can assist in further enhancing the user experience with better search results and ML-based AI Models.