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Sriram Rangarajan

Actionable Customer Intelligence using Cloud Technologies

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by Sriram Rangarajan

Customer intelligence system’s sources are Customer’s Demographics, Web and mobile browsing activities, Customer preferences, Sentiments, Customer support team interactions, Sales team interactions, Social media, and transactions.

Amazon Redshift/Redshift Spectrum/ Elastic Search

Amazon Redshift Spectrum can help you balance the need for adding capacity to the Redshift as well as operational optimization (reducing re-index overhead). Redshift spectrum allows you to directly query from S3 at scale and seamlessly integrate that with redshift (Spectrum Use case – Historical data and infrequently accessed data). Amazon Redshift Spectrum Extends Data Warehousing Out to Exabytes.

Redshift data processing system will help to streamline the Customer Acquisition, Repeat Purchase, Customer Retention, Up-sell/Cross-sell visibility, Product Adoption, and customer experience.

Redshift will provide visibility of Customer Lifetime Value, Churn Rate, Average Order Size, Profitability, and Cost-to-Serve for actionable insights.

Analytics insights could be integrated and automated into the actionable business process through AI.  Elastic search service allows customer intelligence analysis and AI automation to be near real-time.

AWS Customer Intelligence system architecture

Redshift Spectrum and emerging analytics architecture

 

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Azure SQL DW/Azure Databrick

Azure SQL DW data processing system will help to streamline the Customer Acquisition, Repeat Purchase, Customer Retention, Up-sell/Cross-sell visibility, Product Adoption, and customer experience.

Azure SQL DW will provide visibility of Customer Lifetime Value, Churn Rate, Average Order Size, Profitability, and Cost-to-Serve for actionable insights.

Analytics insights could be integrated and automated into the actionable business process through AI.

Azure Databricks allows customer intelligence analysis and AI automation to be near real-time. Databrick spark service also supports Data scientist to develop ML models (Python, Spark SQL, SPARK ML)

Azure Customer Intelligence system architecture

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Google BigQuery /GCP DataProc

Google Big Query data processing system will help to streamline the Customer Acquisition, Repeat Purchase, Customer Retention, Up-sell/Cross-sell visibility, Product Adoption, and customer experience.

Google Big Query will provide visibility of Customer Lifetime Value, Churn Rate, Average Order Size, Profitability, and Cost-to-Serve for actionable insights.

Analytics insights could be integrated and automated into the actionable business process through AI.

GCP Cloud Data proc allows customer intelligence analysis and AI automation to be near real-time. Cloud Data proc spark service also supports Data scientist to develop ML models (Python, Spark SQL, SPARK ML)

GCP Customer Intelligence system architecture