Retail & eCommerce

Picture this: your Monday morning starts with three different revenue numbers. Shopify shows one total, your POS system shows another, and the finance team's Excel rollup splits the difference. By the time someone figures out which is right, you've already made Tuesday's replenishment decision on Wednesday's stale data.

This is what fragmented retail analytics actually looks like. Not a theoretical "data silo" problem. A real operational drag where every inventory call, every ad budget shift, every promotion decision carries a margin of doubt that compounds across hundreds of SKUs and locations.

The replenishment trap. Automated systems like Walmart's RetailLink are supposed to solve this. They often make it worse. We discovered a negative feedback loop at P&G where products sitting in backrooms — visible to the warehouse but invisible to shelves — caused the automation to interpret low sales as low demand. The system ordered less. Sales dropped further. Forecasts cratered to single-digit weekly units for products that should have been top sellers. Our custom On-Shelf Availability algorithm proved the pattern across hundreds of SKU-store combinations and led to $3M in incremental revenue within four months by doing something counterintuitive: strategic overstocking.

The reporting bottleneck. Retail teams spend an absurd amount of time assembling reports that should already exist. At P&G Canada, replenishment analysts burned three days every week manually reviewing fill rates by store and SKU. By the time they identified stockout patterns, the window to act had closed. We automated the entire pipeline — Retail Link extracts, phantom inventory detection, Monday-ready dashboards per analyst — and recovered 120+ hours of analyst time per month. In-stock rates went up 1% portfolio-wide. That sounds small until you run the math across Walmart Canada's volume.

The Nielsen problem. If you depend on syndicated POS data, you know the pain. Nielsen's standard UI pipeline ran 20 days from request to delivery. We bypassed it entirely — reverse-engineered the backend via ODBC, built automated extraction through KNIME and VM infrastructure, and cut the SLA to 3 days. P&G's sales teams were selling with fresher data than their competitors had access to. That speed advantage contributed to securing Walmart Category Captaincy in Oral, Fem Care, and Baby Care, with roughly $5M in annual POS uplift attributed to faster decision cycles.

The eCommerce stack challenge. For digital-native brands, the problem looks different but rhymes. Shopify order data, Google and Meta ad spend, ERP inventory from Odoo — each lives in its own API with its own definitions of "revenue" and "cost." We've built unified Snowflake + dbt foundations that pipe all three into a single model, so your marketing team and your finance team are finally looking at the same numbers when they argue about ROAS.

What changes after engagement. You stop debating which dashboard is right because there's one source of truth. Replenishment decisions run on today's data, not last week's export. Marketing attribution connects ad spend to actual margin, not just top-line clicks. And your analysts spend their time finding opportunities instead of assembling spreadsheets.

How do you know if you need this? Ask yourself: How many hours does your team spend each week reconciling numbers between systems? When was the last time an inventory decision was made on data less than 24 hours old? If your marketing team and finance team pulled a report on the same campaign right now, would the numbers match?