Optimize the self-service customer experience across channels and devices with detailed usage info, customized energy-saving tips, and personalized prompt alerts.
Ensure optimal value from your assets by using root-cause detection to predict leaks and outages, prioritize engineering team deployment and mitigate environmental risks.
Use auto-monitoring of infrastructure and equipment to minimize asset failure, and plan for new and fluctuating consumption patterns by identifying customer microsegments.
Proactively identify, prioritize, and block potential grid threats by detecting physical and informational breaches and correlating unusual customer patterns.
Analyze anomalies and subtle signals in large amounts of data to identify criminal activity.
Accurately identify customers at the highest risk of lapse for effective, personalized retention campaigns.
Pinpoint the best locations to yield the most optimal customer experience and business results.
Use granular customer insights to accurately predict the most effective product or service.
A global mining company wanted to optimize operational costs by reducing fuel consumption for each vehicle across its fleet.
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?
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.