Improved the Retention Rate by 70% by delivering Personalized user recommendations for an American mass media house conglomerate

Viewership details of each user are captured from various partners such as Hulu, Youtube, AppleTV, etc. and stored in Redshift

Highlights

70%

Improved Customer Retention Rate

3–5x

Improvement in BI Reporting load times

40%

Project delivery time saved

20%

Reduced TCO up to

Client Profile

The client is an American mass media and entertainment conglomerate. It is one of the world’s leading media and entertainment companies in the development, production, and marketing of entertainment, news, and information to a global audience.

Business challenges

Lack of Personalized user experience due to the absence of Personalized recommendation Engine
Need to Fetch information from different brands / partners (Youtube, Hulu, etc.) for a single user and provide recommendations which maps to the right brand
Revenue impact due to high churn rate – the absence of personalized content
Lack of business insights for deeper perspective and understanding

Agilisium Solution

Viewership details of each user are captured from various partners such as Hulu, Youtube, AppleTV, etc. and stored in Redshift
The Recommendation Engine arrives at a ranked order of likely shows to be viewed by the user
The top 10 recommended shows by the user is stored in DynamoDB
When the users log in, the app uses the recommendations to create personalized offers

Tech-stacks Used

AWS – S3

Redshift

Business outcome

Recommendation Engine

Viewership details are stored in Redshift
The top 10 recommended shows are stored in DynamoDB, and the same is retrieved to create personalized offers while user login
S3 Storage is used while ML algorithms are running and get stored in S3

Deployment Automation

Applications are deployed to lambda serverless environments and hosted on containers in AWS ECS
AWS Auto Scaling is enabled to scale in and scale out ECS clusters and services
AWS Cloudformation and Terraform are being used for Infrastructure Automation
Deployment is done using CircleCI with automated pipelines

Monitoring & Logging

AWS XRay is used for all serverless lambda applications, and it helps in monitoring most of the applications
For applications running on containers, AWS CloudWatch metrics are being leveraged
Can Integrate with third-party monitoring tools like SignalFx, NewRelic, and Splunk
Application-level health checks are also enabled and are being monitored using Grafana

Security

IAM best practices and principles are followed
AWS Certificate Manager is being used for the load balancer and CDN certificates
All Data stores are in private subnet
Amazon KMS is used for encryption of data at rest

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