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What Is Customer 360?

Customer 360 is a collection of analytics use-cases developed for our customers to understand their customers. They come in handy across the lifecycle of a customer in the evolution from the lead stage to the maturity stage.
B2C businesses across domains have different names for customers. The customer can be a viewer for a media company, subscriber for a telecom company, users for an app company, traveller for a travel company.

How Customer 360 Works?

Run all the campaigns in parallel by calling the on-demand prediction models. Engagement campaigns need not wait for Acquisition campaigns to complete. A retention program need not be sequenced strictly after a cross-sell program. Customer 360 allows you to execute programs at the right time, and not wait for one campaign to complete to kickstart the other.

The customer moves across multiple phases of the customer journey and gives feedback all along. Capture each of the customer’s action and non-action data points in big data systems to be utilized in enriching the customer profile.

Data from disparate sources like CRM, POS systems, web and app analytics tools, campaign managers, marketing automation tools can be integrated into a data lake and transformed to provide insights.

Customer insights stand-alone is just like a recipe on paper, they are advantageous only if the right ingredients are provided at the required stage in the required shape. We help you integrate the insights (scores, lists, characteristics, metrics) to your systems,

  • Say a prospect’s risk score in the application panel
  • A buyers’ favorite categories at the POS terminal
  • The list of customers with the personalised message in the messaging systems
  • The customers segments and customer deep dives in the campaign managers
  • Customer dashboards anytime in your browse





Deep Learning

Benefits Of Leveraging Our Data Warehouse Services Include

  • Customer experience optimization

    Predictive care, Safety analytics, queue minimization and more
  • People process optimization

    Predictive care, Safety analytics, queue minimization and more
  • Product performance optimization

    Traffic management, Predictive maintenance, Smart grid analytics and more
  • Operational efficiency

    Energy utilization analytics, Infrastructure optimization, fuel consumption analytics and more

Algorithms – Neural Networks



Linear Regression



Collaborative Filtering

Tools & Technologies

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