Tickr AI Research & Engineering
Research
Tickr’s AI and Data Science teams are pushing the boundaries of artificial intelligence and machine learning, with a focus on large language models, time series analysis, natural language processing, retrieval augmented generation, causal inference, pricing, and distributed systems. Join us and check in regularly for the latest discoveries.
Authors
Sam Kahn
Ken Koski
Andrew Lyon
Michael Gou
Will Preble
Tim Williams
Destiny Ziebol
Seeing Risk Clearly: How RiskWise AI Moves Past Emotion to Measure Risk Exposure
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Advancing Foresight: How RiskWise AI Quantifies Emerging Risk Drivers
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RiskWise for the Era of Hyper-Automation: Proactive Risk Intelligence at Scale
The rise of AI agents and hyper-automation is fundamentally reshaping how organizations surface, analyze, and act on risk. With nearly half of AI-assisted tasks now fully automated and enterprise adoption accelerating, risk intelligence can no longer rely on manual workflows or siloed data. RiskWise is built for this shift. By combining high-recall retrieval pipelines, proprietary LLM-driven agents, and dynamic risk indices, RiskWise transforms vast, heterogeneous datasets into structured, actionable risk insights. From uncovering hidden risk drivers to modeling cross-industry causal chains, RiskWise enables organizations to move beyond reactive monitoring toward proactive, agent-driven risk discovery. This post explores how RiskWise delivers scalable, future-ready intelligence for decision-makers navigating the era of hyper-automation.
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.
Identifying Substitutable Goods using Large-scale Shopping Cart Basket Data
This post addresses the challenge of identifying substitutable goods in consumer behavior analysis by leveraging large-scale shopping cart data. We introduce a sequential probabilistic model called "SHOPPER" developed by Ruiz, Athey, and Blei, to analyze hundreds of thousands of shopping trips encompassing millions of transactions. Our approach captures the complexity of consumer purchase behavior, considering various factors such as shopping purpose, seasonal variations, in-store promotions, and personal preferences.
Identifying Substitutable Goods using Large-scale Shopping Cart Basket Data across Retailers & Geography
In a previous post, substitutable goods were identified by leveraging large-scale shopping cart data to estimate a sequential probabilistic model called “SHOPPER,” developed by Ruiz, Athey, and Blei. In this post, the analysis is expanded by analyzing shopping cart data from several retailers across various geographical regions. Similar products are queried to investigate how the ranked lists of substitutable products vary across different retailers and geographies. These results provide insights into what products are substituted at various retailers and for the same retailers in different geographies.
Digital Twins – Generative AI Extensions of Qualitative Survey Data
By creating a retrieval augmented LLM pipeline, (RAG with OpenAI's GPT-4o), we explored the capability of LLMs to 'twin' real respondents in a publicly available survey. By providing the model context of previously answered questions from a particular respondent, we were able to achieve far better than random accuracy at masked questions. This opens the door to utilizing LLMs to augment known sentiment, extending surveys beyond the original question set and providing a far more flexible framework for working with qualitative data from real respondents and their 'digital twins'.