Customer retention

Keeping existing customers is more cost-effective than replacing them once they’ve gone.

Understanding why customers churn is often buried within customer activity data. AI-powered analytics reveals thousands of clues that identify who is likely to churn, and why.

Make each customer a segment of one

Most customers who churn do so quietly, without leaving feedback.  SparkBeyond Discovery uses powerful analytics to determine thousands of drivers of churn and recommend highly accurate, CLV-driven retention actions.

Using selected criteria, the platform analyzes complex customer datasets – transactions, contact history, redemptions, offers and app/website browsing history – and combines them with external indicators, such as social media trends, footfall/mobility, geospatial and economic, to complete fragmented profiles and microsegment your entire customer base. 

By evaluating millions of hypotheses per minute, the platform uncovers multi-layered customer insights within hours, and predictive models continue to update automatically, adapting to new data as needed.  The resulting insights enable you to anticipate individual consumer preferences and demand shifts, prioritize personalized offers at the right price points, profile social media influencers, offer hyper-targeted advertising and one-click transactions, identify win-back opportunities – and reward loyalty.

Today, power is shifting towards the consumer in a noisy, fragmented marketplace and global lockdowns have only accelerated this trend. For example, a media company wanted to put a stop to stagnation in its digital subscription base, where 10-15% monthly growth was losing the fight against 14.8% churn.  SparkBeyond Discovery integrated diverse internal datasets (subscription, billing, status) and external datasets (Wikipedia, census), resulting in:

  • Identification of 200 drivers of churn
  • 30% churn reduction in the first 3 months
  • 86% accuracy in identifying customers likely to churn (and their behavior drivers)

As customers increasingly look for continuous engagement, AI-powered intelligence is essential to detect drivers of demand, hidden deep within data, and prioritize retention.

Features

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Business Insights

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Predictive Models

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Micro-Segments

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Features For
External Models

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Business Automation
Rules

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Root-Cause
Analysis

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