Enabling customer centricity, upstream decisioning, and retention at the enterprise- level through Customer Experience Score (CES)
Situation
One of the UK & Europe’s largest Entertainment & Media companies with a 15M+ customer base experienced an annual customer-initiated churn rate of > 10%
A large proportion of customer retention initiatives were discount-driven, leading to a $1B+ financial overhead annually
The client’s Customer Retention team wanted to develop a hyper-personalized metric for every customer to enable:
Avoid being pressed into rolling out deep offers for churn retention at renewal through customer-centricity and nurturing of value-based relationships
Upstream decisioning through churn detection 2-4 months in future
The solution needed to be scalable and enable a near real-time refresh of scores, reporting and treatments for the entire customer base
Our approach
Principles
Working closely with multiple client teams, Fractal developed a solution to:
Define a value-based relationship and move away from price discounts, and offer-based retention
Provide an in-depth understanding of drivers of customer experience to help design customer-centric strategies
Design an easy-to-implement relationship score metric (0-100) to provide holistic view of customers’ relationship with the business
Development
AI: Ensembled AI framework with superior performance and ability to leverage:
Static information like demographics, pricing, billing
Dynamic journeys, such as a sequence of past transactions/interactions
Engineering: Harmonized customer view (Customer 360) at weekly level, from 25+ raw data sources
Design: ‘Simple to measure’ metric, model explainability, and business actions
Testing & Operationalization
Results validated on OOS & OOT samples and against existing baseline models
End-to-end automation on the cloud and incremental execution on a weekly basis
Integration with marketing platform (through the on-premise database) to ensure regular automated feeds of customer responses, lists ,and drivers
Insights
40% of drivers discovered by the model were completely new/unrelated to offers and provided incremental intelligence about:
DTV and broadband product holdings
Billings/Pricing, marketing outreach
Engineered 800+ granular features to identify early signals of poor experience and churn-risk behavior across 150+ customer segments
Established the hypothesis that experience is defined not only by the current state but by the overall journey the customer has been through with the organization
Impact
Estimated annual impact
Call reduction: 7-10% reduction in the total annual volume of cancellation calls
Churn reduction: 5-6% reduction in the total annual volume of churn
Innovation
First customer management solution on the cloud for the organization resulting in accelerated migration from on-premise data and technology platforms
Simultaneously captured dynamic and static behavioral patterns
Self-learning models enabled adjustment over time to account for the change in customer behavior
Process automation, parallelized data processing, and modeling resulted in efficiencies at scale and delivered a time reduction of 20X compared to traditional frameworks
CES is a dynamic indicator of the experience customer is having with the business throughout the relationship
Customer 360 is a single row per customer capturing a holistic view of the relationship
More than 40% of drivers of Customer Experience were unrelated to End of Offer/Contract and helped discover insights not known earlier to client
CES successfully differentiated the high and low risk of churn
AI/ML framework on the cloud was based on three key components to drive customer-centricity
Ensembled deep learning model was built to capture both historical journeys and current behavior to identify at-risk customers
Ensembled deep learning model was built to capture both historical journeys and current behavior to identify at-risk customers
