Anticipate individual consumer preferences and demand shifts, while personalizing promotions and communications using a data-driven 360-degree view.
Empower the sales force with geospatial intelligence: location optimization, range selection, and trend analytics using IoT, transaction, and external, real-world data.
Pinpoint marketing efforts with a centralized view of budget-spend, with ongoing omnichannel analysis across all channels and devices.
Analyze drivers of SKU-sellout data to optimize the supply chain, and augment macroeconomic variables with consumer sentiment.
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.
Instead of leveraging physical scale to capture margins and share, leaders within the consumer packaged goods (CPG) industry are now using AI analytics, and its store-specific insights to be super agile, close to their customers, and faster than their competitors.
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.