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Building recommendation engines that boosts revenue & customer experience
According to the statistics provided by McKinsey and Tech Emergence, implementing recommendation systems in their business operations brought Amazon 35 percent of its revenue and 23.7 percent growth to BestBuy. 75 percent of video consumption on Netflix comes from its recommendation system. Similarly, 60 percent of the views on YouTube come from their recommendation feature.
We build custom recommendation engines to enable our clients with WHAT, WHEN, WHERE, and WHOM to recommend their products, services, and applications. Our AI-driven engines use machine learning and deep learning algorithms to serve the purpose and provide relevant information. We help in determining which recommendation system is best suited for our clients.
We make sure that our recommending systems help our clients with
- Increased business revenues
- Personalized marketing campaigns
- Enhanced customer service
- Accurate analytics reports
- Personalized customer experience
- Improved customer retention
Categories of Recommendation Engines
Our expert team develops hyper-personalized engines based on different categories:
Collaborative filtering
These algorithms generate recommendations based on crowd-sourced inputs, where customers’ affinity define the similarities. This filtering increases further engagements from the users as they build strong connections with every piece of content; users share.
Content-based filtering
These algorithms generate recommendations based on crowd-sourced inputs, where the customers’ affinity defines the similarities. Various models are built to process various types of attribute data. The approach requires the implementation of market research data, and hence, no user ratings are required.
Demographic-based recommendation
We categorize the users based on attributes and facilitate recommendations based on a set of demographic classes. We build demographic recommendation systems that are not complex and are easy to implement. The approach requires market research data to be fully implemented and hence, no user ratings are required.
Hybrid recommendation systems
We integrate any of the above two models that are best suited for a particular industry and build hybrid models that provide more effective recommendations to the users. These systems help improve recommendations from inadequate, scant, and infrequent datasets.
Our Featured Recommendation Engine Use Cases
Product recommendation engines
Product recommendation engines recommend new products to customers based on their previous search or purchasing behavior. These engines filter, predict and show items that a user would like to purchase and allow for natural, logical upsell and cross-sell opportunities.
Health and wellness recommendation systems
Health and wellness recommendation systems recommend specialized doctors based on the symptoms provided, recommend yoga and other wellness-related services, etc. Leveraging big data and predictive analytics, the engines help people make informed decisions about their health.
OUR CLIENTS
Why Choose Us For Your Recommendation Engine Project?
- A dedicated team of expert data scientists with deep authority over AI, Machine learning and NLP, etc.
- Experienced in building Recommendation Engines.
- End-to-end services from strategy consulting to software development.
- Complete adherence to data regulations like HIPAA, Data protection 2018, etc.
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