RECOMMENDATION ENGINE

• Increased business revenues
• Personalized marketing campaigns
• Enhanced customer service
• Accurate analytics reports
• Personalized customer experience
• Improved customer retention

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.

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.

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.

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.

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 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.