Consumer Goods (CPG)

A replenishment analyst at a major CPG company opens Retail Link on Monday morning. She needs to review fill rates across 400+ SKU-store combinations before the weekly order cycle closes. The data she's looking at is six days old. By Thursday, she'll have her analysis ready. By then, the stockouts she's flagging have already cost a week of lost sales.

This is the rhythm of CPG analytics at most companies. Not broken in a dramatic way — broken in the slow, compounding way that erodes millions in revenue annually while everyone agrees "our data could be better."

The syndicated data bottleneck. Nielsen and IRI are the lifeblood of CPG planning. They're also notoriously slow. At P&G Canada, standard Nielsen extractions took up to 20 days through the UI — with frequent timeouts and failures that forced restarts. We bypassed the entire front end, connecting directly through ODBC, automating extraction through KNIME, and cutting delivery from 20 days to 3. The impact went beyond convenience. P&G's sales teams sold with data their competitors hadn't seen yet. That information advantage contributed to winning Walmart Category Captaincy in three major categories and an estimated $5M annual POS uplift.

The hidden cost of replenishment automation. Automated ordering systems are a staple of CPG retail partnerships, but they carry a risk that brand teams rarely see until the damage compounds. We identified a pattern at P&G where the automation was systematically destroying its own forecasts — the full case, including the On-Shelf Availability algorithm and $3M recovery, is in our Supply Chain practice. For CPG brand teams, the industry-specific danger is this: you lose visibility before you lose the shelf. Category managers rely on scan data to justify planogram positions and negotiate shelf space with retail buyers. When availability issues suppress scans, the brand's internal data tells the same false story the retailer's system tells — that the product isn't selling. By the time someone questions the number, the buyer has already reallocated facings to a competitor. Recovering that shelf space takes a full category review cycle, often six months or more.

Analyst capacity as a competitive advantage. Every hour a replenishment analyst spends pulling Retail Link extracts and formatting pivot tables is an hour not spent spotting the stockout pattern, the promotional lift anomaly, or the emerging regional trend. We automated P&G Canada's weekly reporting pipeline end-to-end — details on the 120+ hours recovered monthly are in our Reporting practice. What matters for CPG specifically is what the recovered time enables: proactive category management instead of reactive fire-fighting. The analysts who used to spend Monday through Wednesday assembling data started spending that time interpreting it — finding the patterns that justify promotional investments and defend shelf allocations in retailer QBRs.

Route-to-market visibility. For regional CPG brands, the challenge isn't just shelf performance — it's knowing which routes, distributors, and retail partners actually drive growth. We've worked with regional producers to digitize sales route data, analyze basket composition by territory, and surface the distribution patterns that separate their expanding markets from their stalling ones.

What the engagement delivers. A governed data layer where Nielsen, POS, and internal supply chain numbers reconcile automatically. Analyst workflows that start with insights, not extraction. Replenishment logic that distinguishes between demand problems and availability problems. And a clear connection between what happens at the shelf and what your planning team sees on screen.

Diagnostic questions. How many days old is the syndicated data your team makes decisions on? Could your analysts tell you today which low-performing stores have a demand problem versus a stocking problem? When your replenishment system reduces an order, does anyone verify whether the sales decline that triggered it was real?