F100 Media Broadcaster – Hyper Personalized Customer Experience Score (CES)

Business problem

Annual customer-initiated churn rate greater than 10% with discount-driven customer retention initiatives leading to a $1B+ financial overhead annually. The client wanted to hyper-personalize metrics for every customer to avoid being pressed into rolling out deep offers and for upstream decisioning through churn detection 2-4 months in the future.

Fractal solution

Principles:

Working closely with multiple client teams, Fractal developed a solution to:

Define value-based relationships and move away from offer-based retention

Provide an in-depth understanding of drivers to design customer-centric strategies

Design an easy way to implement the relationship score metric (0 - 100) to provide a holistic view of customer’s relationship

Fractal harmonized the customer view (Customer 360) from 25+ raw data sources and created an ensemble AI framework with end-to-end automation on the cloud

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 the marketing platform (through the on-premise database) to ensure regular automated feeds of customer responses, lists, and drivers

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Innovation

  • First customer management solution on 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 change in customer behavior
  • Process automation, parallelized data processing and modeling, resulting 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

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

Business outcomes & impact delivered​

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40% of drivers discovered were new/unrelated to offers and provided incremental intelligence about - DTV and broadband product holdings and Billings/Pricing, marketing outreach

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Engineered 800+ granular features to identify early signals of 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

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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

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Revenue-loss prevention: Arresting average drop in subscription revenue per customer/quarter, leading to $40M+ in value savings in the first year

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Cost reduction: $20M+ savings due to the reduction in cancellation calls and churn prevention activities

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AI Platform Notebook

Fractal's value proposition

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Fractal developed self-learning models which enabled adjustment over time to account for change in customer behavior
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Process automation, parallelized data processing and modeling, resulting in efficiencies at scale and delivered a time reduction of 20X compared to traditional frameworks
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First AI-powered solutions on Cloud for the organization leading to improved scalability, efficiency, and accuracy in driving customer centricity at enterprise level
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