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
Big Query
Compute Engine
Cloud Storage
Cloud Composer
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
