Granular customer segments ensure accurate, personalized communication, pricing and promotions, while driving hyper-local assortment of products and services.
Forecast channel volumes, trigger alerts, and prioritize KPI-based actions with automated predictive inventory-optimization and forecasting tools.
Determine the best price using granular item data, customer segments, traffic flow, seasonality, and insights on competitor and social media trends.
Transform your store portfolio by pinpointing specific geographical areas with high potential for profitability and campaign effectiveness.
Pinpoint the best locations to yield the most optimal customer experience and business results.
Analyze enormous volumes of various data to forecast demand, supply, quality issues, and market trends.
Identify the right potential customers, shape the right message and pinpoint the best delivery methods.
Analyze operational management — supply chain, logistics, field — and determine new factors that affect performance.
When machine-learning systems produce ideas, not just test them.
Here’s how a global retailer equipped their analytics team with AI to find answers hidden in large, complex and disconnected data sets, massively reducing time to value.
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.
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?