The challenge
The cost of every “Hello”
The insurer's call centers were overwhelmed, leading to increased operational costs and potential declines in customer satisfaction. Traditional methods lacked the predictive capabilities needed to anticipate call volumes and understand the underlying reasons for customer inquiries. The insurer sought to:
Key challenges
Utilize customer data to optimize self-service channels
Develop predictive models to forecast call frequency and intent
Implement chat support options to reduce reliance on call centers
The solution
From guesswork to great service
Getting smart with the data
Unified customer data through ETL pipelines and the Cogentiq Personalization platform
Processed digital activity using Spark and Python for real-time insights
Built a 360° customer view to uncover patterns behind call behavior
Predicting the next call
Developed ML and deep learning models to forecast call likelihood and reasons
Linked digital behavior to likely service needs before calls happened
Enabled proactive interventions to reduce the need for customer calls
Implementation approach
1
Data prep
Integrated digital and call data
Built scalable ETL pipelines
Cleaned and structured inputs
2
Model build
Designed predictive models
Trained on behavioral data
Validated for accuracy
3
Smart activation
Deployed real-time triggers
Enabled proactive nudges
Informed agent interactions
The impact
The success snapshot
Rapid impact
Enabled real-time interventions
Reduced inbound call volume
Lowered operational strain
Smarter insights
Identified call intent drivers
Mapped digital-to-call behavior
Surfaced key customer patterns
Faster response
Streamlined support workflows
Boosted customer satisfaction
Shortened resolution times
Looking ahead
Sustained efficiency
Ongoing cost savings through reduced call dependency
Customer empowerment
Improved self-service experiences and digital engagement
Data-driven culture
Equipped teams with scalable analytics capabilities
