Noventiq Helps Lal Pathlabs Enhance Search Experience of Users
Dr Lal’s PathLabs
www.lalpathlabs.com
01. The Client
The company has a national network comprising National Reference Laboratory in New Delhi, 170 clinical laboratories, 1,700+ patient service centres and over 5,000 patient service pickup points. It is present in metropolitan areas such as New Delhi, Mumbai, Bengaluru, Chennai, Hyderabad and Kolkata. Dr Lal has more than 3000 employees with over 55 percent of the staff engaged in laboratory functions.
02. The Challenge
The search functionality of the website was basic, allowing customers to search only along limited indexes: test name, test package and location. This was adversely affecting its business as customers could not find all services on offer. The system also required an automated process whereby updates and new services reflected in the search index.
03. The Solution
We deployed Elastic Search, a scalable and full text search service from AWS by programmatically linking queries with index. The team created categories of keywords to make the engine faster. Next, we worked on the search engine to map queries with indexes and analysers that recognizes a range of keywords, and provides the desired output.
Further, our team introduced automation by programmatically integrating AWS Lambda with Elastic Search. We implemented automatic data ingestion from database—Lambda is triggered by Cloud Watch periodically and automatic data ingestion occurs whenever there are modifications in the master data and updates ElasticSearch index so future search queries start rendering these changes/additions. This made outputs to customer queries much more comprehensive, dynamic and current.
04. The Result
The search engine provides much faster by moving search function from database to Elastic Search. And thereby reducing the load on database.
Search capabilities are significantly higher covering a comprehensive range of keywords across indices. Web pages have higher hit rate as customers are able to find services easily.
In the next phase, the engagement will focus on making the engine more intelligent by enabling it to recognize keywords and make recommendations, even if spelled incorrectly. Search will also have capabilities to provide output based on “relevance” which considers weightage and popularity.
Says Sandeep Singh of Dr Lal PathLabs, “The website is much faster, and our services are more searchable, thanks to the meticulous work by Noventiq in understanding our business requirement and mapping queries skilfully to make the engine very intelligent.”
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