Develop individual scores for each product category by incorporating and aggregating individual input factors for price sensitivity and promotion affinity.
Achieve increases in revenue and margin with a granular view of the cumulative KPI—or “total customer effect”.
Test smart price recommendations and identify key success factors that can be replicated across products and accounts.
Actively monitor customer-level profitability, including volume discounts, rebates, and cash discounts, to ensure they’re earned.
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.
Anticipate changes in consumer preferences to tailor promotions, prices, and products for each customer.
Here's how a global retailer massively reduced time to value by equipping their analytics team with AI to find answers in complex, disconnected data sets.
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.