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Astra DB for Customer 360

Modern digital economies rely on providing seamless and personalized experiences for their customers across a variety of channels including web, mobile, social media or in-store. These requirements make it necessary for today’s businesses to prioritize a holistic understanding of their customers with real time data.

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Why Astra DB for Customer 360?

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Scale Without Compromise

With Astra DB, you can easily grow to petabytes of data, and hundreds of thousands of transactions per second while maintaining high responsiveness with low read/write latencies and high availability. This allows Customer 360 experiences to scale without compromising customer satisfaction.

Keep Customer Data Secure

Most (if not all) customer data (including Customer 360 data) is subject to privacy and security requirements.With Astra DB, ensure security for your Customer 360 data with authentication and authorization features such SSO/SAML integration, as well as data encryption & BYOK (bring-your-own-key) management.

Access Data with Multi-model Flexibility

A Customer 360 experience requires a variety of data collection through clickstream analysis, social media feeds, purchase history and account information. Astra DB provides multi-model flexibility with Document (JSON), REST, GraphQL and gRPC APIs to shorten time-to-value of all such Customer 360 apps.

Choose Any Cloud

The multi-model Astra DB can span across multiple data centers or cloud regions and can be deployed in AWS, GCP and Azure, while maintaining compatibility with open-source Cassandra. And all this can be done without the overhead of installation and maintenance. TCO savings with pay per consumption with universal Astra credits on any cloud.

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Architecture for Customer 360

Astra DB, along with CDC and Astra Streaming, connects data from many sources, allowing you to easily create data pipelines between mobile/web front-end services, and backend analytics, data warehouses, unifying all your data for improved Customer 360 views. Customer data at scale feeding ML models for real time personalization and recommendations will deliver improved business metrics, such as higher click through rates, improved conversions and resulting in higher average transaction value.

Architecture for Customer 360

Astra DB Example Customer 360 Data Model & Queries

An example of Customer 360 data is a personalized digital music library, in which the data model includes tables for performers, albums, tracks and users. Multiple types of queries and data access patterns exist for this data such as performers by name, users playlist and others. Here is a visual representation of the data model created within Astra DB for the relevant data and queries:

For more information on the digital music library, please see our data modeling library.

For more information on the digital music library, please see our data modeling library.

Figure 2 - Data Model & Queries - personalized digital music libraryFigure 2 - Data Model & Queries - personalized digital music library

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Industry & Customer Success

After implementing Astra DB, FINN.no has worked with DataStax on supporting the performance and availability levels of the personalization engine. This helps the Data Intelligence team deliver the speed of recommendations that the FINN.no team requires, as well as supporting more organizations within the Schibsted Group in making use of personalization and data science in their day-to-day activities.

Read how Finn.no uses Astra DB to power their classified ads

Siggy.ai is an artificial intelligence-driven recommendation app for Shopify webshops. While Siggy.ai was building out its artificial intelligence-driven Shopify app, it found key specialized operations he needed were only available on DataStax Astra DB.

Read how Astra DB helped Siggy.ai launch their app

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