F100 Media Broadcaster – ML Engine based next best recommendation​

Business problem

Client wanted to drive better customer experience and up-sell/cross-sell conversion over the huge volume of interactions between customers and contact center  agents. Client call volume: 25M+ incoming calls a year from 15M+ customers​.

Fractal solution

  • Fractal architected a customer-first approach in the form of top 3 product recommendations from 30 product lines for the entire customer base on the cloud platform.
  • Built AI-driven recommendation systems, post customer 360 development with the help of 25+ data sources. Cloud architecture to incubate harmonization solution of input data pipelines as well as the modeling assets.
  • AI model outputs are served via API that services call agent screens to surface top-3 product recommendations.
  • The contextual layer comprising of customer insights to provide ‘talking pointers’ to call agents vis-à-vis rationale of respective product relevance

Business outcomes & impact delivered​


Big data sources like viewing and clickstream provided a significant boost in the understanding of customers’ product affinity. Global top drivers from models, when contextualized to the customer journey, provided call agents with a relevant conversation context.


First sophisticated cloud tech stack to support enormous computation of weekly acceptance likelihood of 30 products for 15M+ base​


Five thousand call agents and retail shop agents use this tool regularly. Swift adoption and excellent agent feedback; high % of agents showed an uplift in sales in 1st week itself.


Improved accuracy from benchmark models by an average of 18%. The annual incremental benefit of $10M+ is based on higher upgrade sales volumes, better sales mix, better save rate, etc.

Looking for a similar project?

Google Cloud services​

Google Cloud Consumption Estimate $30,000 annually

Google Cloud services Big Query

Big Query

Google Cloud services compute engine

Compute Engine

Google Cloud services Cloud Storage

Cloud Storage

Google Cloud services Cloud Composer

Cloud Composer

Google Cloud services Cloud SQL

Cloud SQL


AI Platform Notebook

Value addition / accelerators​

Scalable TensorFlow based AI architecture to host 3 sets of models – Linear, DNN & Boosted tree classifiers across 30+ products
Average models build to productionization time for new products significantly reduced from 4-6 weeks to 1-2 days using automated, scalable AIML architecture
Highly modular cloud-based architecture to orchestrate streamlined data processing and AI-powered modeling pipelines (including model training, integration, and serving) at scale

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