Transformational Analytics in Telecom

Discovery for Telecom

Telecom leaders are redefining how the world connects, using hyper-personalized marketing, targeted infrastructure investment, and enhanced customer support across their growing network.

Earn customer trust with personalization

Target high-risk customers by discovering thousands of unique drivers of churn per consumer attribute, and microsegment your entire customer base.

Optimize the omnichannel, omnipresent network

Find root causes of network latency across multiple sites, devices, operators, and locations. Avoid customer impact with accurate planning for capacity, routing and speed.

Superior service that exceeds expectations

Stay relevant with customers by providing a seamless, hyper-relevant, zero-friction experience: reduce call dropouts, instantly notify service interruptions, and identify changes in service quality.

Boost Lifetime Value with customized offerings

Mine internal data to generate thousands of novel insights that drive personalized incentives, product bundles, and smart ad placements, all via the user’s preferred channel.

New data models for a new world

Nearly all existing models have been invalidated by the coronavirus pandemic. They will presumably be invalidated again as the situation evolves as the world recovers. Even after recovery, it is unlikely that we will return to where we started before the crisis began. So how does that affect our data models?

Accelerating data-driven digital transformation

New world, new models….Data and AI have the opportunity to drive more impact than ever

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Applications for SparkBeyond Discovery in Telecom

Customer retention

Accurately identify customers at the highest risk of lapse for effective, personalized retention campaigns.

Cross-sell and upsell

Use granular customer insights to accurately predict the most effective product or service.

Campaign effectiveness

Identify the right potential customers, shape the right message and pinpoint the best delivery methods.

Demand Forecasting

Analyze enormous volumes of various data to forecast demand, supply, quality issues, and market trends.

Fraud

Analyze anomalies and subtle signals in large amounts of data to identify criminal activity.

Pricing

Anticipate changes in consumer preferences to tailor promotions, prices, and products for each customer.

Operational efficiency

Analyze operational management — supply chain, logistics, field — and determine new factors that affect performance.

Predictive maintenance

Accurately forecast equipment failures, and recommend action before faults and breakdowns cause costly delays.

Automated Hypothesis Generation

When machine-learning systems produce ideas, not just test them.

Read on

Generative AI for data analytics: the future of enterprise sense-making

In the case of enterprise data analytics, generative AI will radically change the way we interrogate our data to explore, react to and shape our business realities.

Generative AI for data analytics: the future of enterprise sense-making

New data models for a new world

Nearly all existing models have been invalidated by the coronavirus pandemic. They will presumably be invalidated again as the situation evolves as the world recovers. Even after recovery, it is unlikely that we will return to where we started before the crisis began. So how does that affect our data models?

The Game Changers: AI in Banking

AI analytics helps major banks stay connected to their customers – especially during the dramatic rise of online financial management use. As leading banks apply a range of strategies to navigate the new normal, AI analytics is opening greenfield opportunities to raise profits, combined with agility and ethical responsibility.