Don’t settle for blind spots

Why Did Market Share Decline? The Decomposition Analysis Most Teams Get Wrong

Most CPG teams can see that market share moved, but not why—because they rely on slow, manual “share analysis” that stops at charts instead of diagnosis. The blog explains how proper market share decomposition should break a change into concrete drivers (distribution, velocity, price/mix, and competitive moves) at retailer, region, and segment level, instead of confusing simple correlations or national averages with true attribution. It shows how AI automates the hard parts—data integration, baseline modeling, and competitive tracking—so brand managers can ask “Why did share drop at Kroger last month?” and instantly see quantified drivers and recommended actions. With this always-on decomposition, teams move from week-long investigations and stale insights to fast, retailer-specific decisions that actually recover and grow share.

How Agentic Analytics Automates Executive Reporting

AI-powered narrative generation turns variance analysis into board-ready CFO commentary in seconds instead of days. The blog shows how agentic analytics monitors performance continuously, decomposes variances, and auto-writes tailored summaries for executives, business partners, and the board—while keeping every sentence traceable to source data. Finance leaders still own the judgment and messaging, but they reclaim close-week time by automating the repetitive “what happened and why” part of reporting.

AI-Powered Variance Analysis for FP&A: Root Cause in Seconds, Not Days

AI-powered variance analysis automates the painful “why did we miss budget?” work that FP&A teams currently do in Excel over 3–5 days every close. Instead of testing price, volume, mix, region, and channel hypotheses one by one, an AI platform connects to ERP, planning tools, and warehouses, decomposes budget-vs-actual gaps across all drivers in parallel, and ranks the true root causes in seconds—often at dimensional intersections humans don’t check. A governed semantic layer keeps definitions for revenue, EBITDA, and variance consistent, while agentic analytics continuously monitors KPIs, triggers investigations when thresholds are breached, and generates executive-ready narratives and P/V/M breakdowns automatically. The blog explains what AI does not replace (finance judgment, strategic decisions, stakeholder storytelling), addresses common objections around messy data and trust, outlines an 8–12 week implementation path, and offers a checklist for evaluating variance analysis platforms—highlighting when this kind of AI is a strong fit for finance teams.