Sitting services website
Our client is a leading online marketplace for sitting services, such as childcare, senior care, special needs care, pet care and housekeeping helping more than 30 million families in 20 different countries find local high-quality caregiversbest suited to their specific needs. Client operates under subscription model offering free basic membership tier providing limited capabilities and premium paid membership tier enabling access to all site features.
Search capability lies at the very heart of our client’s business enabling millions of care seekers to match with appropriate caregivers. Our client presented us with the challenge of significantly improving the existing search function with the aim of decreasing customer churn rate and increasing conversion rate from free to premium tier membership.
Based on in-depth examination of the platform and extensive domain knowledge our team decided to tackle enhancement of the search function from two angles:
- Search Speed: Based on detailed site analysis it was determined that both dated presentation layer at the front-end of the application as well as legacy search engine implemented on the backend of the platform need to be redesigned. Our team has significantly redesigned presentation layer of the search functionality as well as reengineered search engine by migrating the database service layer from on-prem to cloud-based solution, rewriting the service architecture from monolithic solution to microservice based one and providing migration path from the old solution to the new one.
- Search Relevance: One of the most important lessons learned from well-established search engines is that key to business success in the domain is improving relevance of search results while preserving simplicity of user specified search criteria. Our team devised an approach for improving search relevance by personalizing search results based on search history, job fulfillment history, previous interactions with other users (in the form of shortlisting, liking and skipping certain profiles) and finally the user set preferences set in the search itself. To transform implicit user data into explicit search criteria to be served to search engine the team utilized sophisticated machine learning algorithms tailored specifically towards the specific needs of the project that was specifically trained based on non-discriminatory limited profiles of users to provide results that were both relevant while still being fair to all participants of the marketplace.
Design, Development, Testing, DevOps and Maintenance.
6 months project delivered by cross-functional team of 5 software engineers, technical architect, product owner and scrum master, assisted by a team of QA specialists.
Solution provided measurable and reliable reduction of search result loading time (from up to 5 seconds waiting for results to an observable maximum of 200ms), that resulted in a significant increase in customer satisfaction scores based on the customer feedback.
Personalization of search results has further driven the job fulfillment rate up, with the increase averaging about 15% depending on the market segment. As a result of higher quality candidate results job poster premium conversion rate has seen a significant increase as well as premium membership retention rate.
The results have been tested in production and were observed to be reliable, scalable and highly cost efficient compared to the previous solution deployed by the client.
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