Guides
Evergreen, chaptered, and updated quarterly — each guide is the definitive reference on its topic.
Guide · 9 chapters
Syndicated Data: SPINS Vs Circana Vs Nielsen
What syndicated data is, how SPINS, Circana and Nielsen differ, and how to read ACV, TDP and velocity correctly without breaking the math.
1What is syndicated data?2How is syndicated data collected and projected?3SPINS vs Circana vs Nielsen: how do the providers differ?4What do ACV, TDP, and velocity actually mean?5How do you read base, incremental, and promotion measures?6Markets and periods: MULO, xAOC and the provider calendar7The aggregation trap: syndicated math that breaks silently8What syndicated data does not tell you9Which syndicated data provider do you need?
Updated Aug 2026
Guide · 8 chaptersThe Brand Context Layer
A brand context layer is the data, semantics, memory, and rules that let AI reason about a specific brand's business. The operational definition, explained.
1What is a brand context layer?2Brand context layer vs brand voice3The four components: data, semantics, memory, and rules4Is a brand context layer the same as a semantic layer?5How a brand context layer compounds over time6Who owns your brand context layer?7What a brand context layer looks like for a CPG brand8From brand context to the autonomous brand
Updated Jul 2026
Guide · 9 chaptersAgents in CPG: the Complete Guide
What AI agents and agentic analytics really are, why dashboards fail brand teams, and how agents run SPINS, Circana, and Nielsen data correctly.
1What is an AI agent in CPG analytics?2Why do dashboards fail brand teams?3What is agentic analytics?4How does an agent work with SPINS, Circana & Nielsen data?5How accurate are AI agents on syndicated data?6Why does an AI agent need to know your brand?7How do agents keep syndicated reports current?8What should CPG teams look for in an analytics agent?9What comes after agents? The autonomous brand
Updated Aug 2026