Enabling customer centricity, upstream decisioning, and retention at the enterprise- level through Customer Experience Score (CES)


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

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


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


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


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


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


Let’s connect to work better, together.

Our email: [email protected]

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