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 shelf availability. They often make it worse. We uncovered this exact pattern at P&G's Walmart business, where a feedback loop in automated replenishment was silently destroying demand — the full story is in our Supply Chain practice. For retailers, the lesson is specific: your POS data doesn't just measure demand — it shapes future supply. When shelf-level execution breaks down (backroom inventory, missed restocks, bulky items that don't fit displays), the automated system reads the symptom as the cause. The retailer sees declining velocity on a top SKU and deprioritizes shelf space. The brand loses its planogram position. Recovery takes months and costs multiples of the original lost sales.

On-Shelf Availability as a revenue lever

Our custom On-Shelf Availability algorithm at P&G proved this pattern across hundreds of SKU-store combinations in the Fem Care category. By cross-referencing RetailLink POS data, inventory positions, and store stocking patterns using R and KNIME workflows, we separated genuine low demand from availability-driven sales suppression. The fix was counterintuitive: strategic overstocking. Pilot results in 20 stores were visible within two weeks, leading to all-Canada expansion and then cross-category application to Shaving — P&G's highest-margin category. Total impact: $3M incremental revenue in four months, 10% stockout reduction, 5% improvement in on-time deliveries. That work ultimately contributed to securing Walmart Category Captaincy in Oral, Fem Care, and Baby Care.

The reporting bottleneck

Retail teams spend an absurd amount of time assembling reports that should already exist. We automated P&G Canada's entire replenishment reporting pipeline — the full automation story is in our Reporting practice. For retail operations specifically, the cost of slow reporting compounds faster than in other industries because the decision windows are so short. A weekly order cycle closes whether your analysis is ready or not. A stockout that takes five days to detect costs five days of lost sales — there's no backfilling demand that walked to a competitor's shelf. The retailers who win at execution aren't necessarily smarter. They just see the data sooner.

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.

Omnichannel attribution

The hardest analytics problem in retail today isn't any single channel — it's the seams between them. A customer discovers a product on Instagram, researches it on your website, and buys it in-store. Your ad platform claims the conversion. Your POS system records it as organic foot traffic. Neither is wrong; both are incomplete. We build attribution models that stitch the customer journey across touchpoints, giving marketing and merchandising a shared view of what's actually driving revenue — not just what each platform wants to take credit for.

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?