A large CPG company's sales, brand management, and I&A teams were unable to proactively detect sales and market share risks, with fragmented monitoring and no unified signal view across data sources. Micro-trends and risks were being identified too late to take meaningful corrective action. POS, panel, macro, and social signals were scattered across disconnected systems, and significant analyst time was spent on data aggregation rather than decision support.
Cloverpop built a 4-step AI workflow: Integrate data feeds → Identify anomaly flags → Interpret risk severity → Act with personalized alerts. The system used logic trees, deep learning, anomaly detection agents, and NLP narrative generation, scanning at channel, retailer, brand, and PPG levels. Automated feeds from Syndicated (Nielsen) POS, Panel, Macroeconomic, Social Sentiment, and E-com data provided continuous real-time refresh, processing ~80,000 signals.
Results
~80,000 signals processed in real time
5+ integrated data source types (POS, Panel, Macro, Social, E-com)
Teams shifted from reactive to proactive risk monitoring, with curated alerts reducing noise and persona-based insights delivered to Sales, Brand, and I&A teams. Real-time integration across POS, Macro, Social, and Panel data gave teams a unified signal view. Early warning signals and AI-driven insights enabled proactive interventions, reducing analyst burden and accelerating decision velocity across brand and sales teams.