The future of risk is powered by AI
Tickr AI helps companies identify and mitigate emerging risks. Our flagship platform, RiskWise, continuously scans vast datasets and applies advanced data science, predictive AI, and LLMs to detect, analyze, and contextualize risk signals before they escalate.
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Improving Forecast Accuracy During Volatile Market Conditions: A Hierarchical Reconciliation Approach
Forecasting across hierarchical levels — for example, national, retailer, and category — is often inconsistent and inaccurate when done independently. We show, via a CPG case study, how applying hierarchical reconciliation methods with Tickr’s Forecasting Platform improves forecasting accuracy by an average of 14.9% enabling better business decisions.
How AI Assistants can help you understand your business and drastically accelerate Scenario Planning
Traditional CPG forecasting is slow, manual, and often relies on intuition rather than data-driven insights. Our AI-powered solution automates scenario planning, cutting weeks of effort down to hours. Using machine learning, it identifies key factors—like competitor pricing and economic trends—while ChatCPG’s AI agents generate scenarios and provide clear, actionable insights. This streamlined approach enables faster, smarter decision-making, helping businesses confidently navigate market shifts.
Surpassing Frontier AI for CPG & Retail: Tickr AI beats OpenAI & Anthropic at Dynamic Hierarchical Product Categorization
Tickr’s Dynamic Hierarchical Product Categorization offers CPGs and retailers a scalable, AI-powered solution that automates the task of categorizing products across evolving categories, significantly reducing manual effort and operational costs. Utilizing Tickr-LLM-base and Tickr-LLM-fine-tuned, the system achieves up to 98.1% accuracy, outperforming leading models like GPT-4o and Claude Sonnet 3.5. This superior performance enables businesses to streamline processes, enhance efficiency, improve downstream data science applications and reducing time spent on maintenance.
Exploring Walk-Out Rates with Shopper Journey Data
Shopper Journey Data provides insights into why shoppers choose to purchase certain products at specific Retailers over others. Analyzing timestamps and basket-level purchases from the Ohio region, allows us to observe whether shoppers make subsequent trips for specific products on the same day. Our findings illustrate that walk-out rates vary by retailer, food category, and brand. Convenience and dollar stores have higher walk-out rates compared to grocery stores, likely due to their smaller size and limited assortments. Certain food categories have greater variation in walk-out rates across retailers, indicating that certain retailers are better at capturing the appropriate product assortment or provide more appealing pricing and promotions.