Your product sells in five channels. Your margin data lives in none of them.

CPG brands sell through more channels than almost any other industry. Amazon (Seller Central and Vendor Central). Shopify or another DTC storefront. Retail distributors. Subscription platforms. Wholesale accounts. Each channel has its own reporting, its own revenue recognition, and its own version of what sold.

The result is a brand that cannot answer basic questions: Which products make money after all costs? Which channels generate the highest margin? Is that promotion driving incremental revenue or just pulling forward demand? The data exists across systems. What does not exist is the layer that brings it together.

CorrDyn works with consumer brands across nutrition, apparel, medical devices, agriculture, and specialty coffee. The problems repeat: fragmented channel data, unknown product-level margins, marketing spend that cannot be traced to revenue, and data infrastructure that costs more than it should.

Multi-channel revenue reconciliation. For a subscription nutrition brand, we built ongoing Data Team as a Service covering Shopify, Amazon, Recharge, and Northbeam integrations. Executive dashboards replaced a patchwork of Looker Studio reports and manual data pulls. Revenue from every channel is reconciled into a single source of truth.

SKU-level profitability analysis. For a medical device e-commerce company, we connected marketing spend, conversion data, and revenue from ERP, CRM, Google Ads, Bing, Amazon, and Facebook to report profitability at the product SKU level. The product team used the analysis to cut their portfolio by a third, eliminating products that lost money after fully loaded costs.

Data infrastructure cost reduction. For a subscription apparel brand, high row-count connector utilization was driving variable and rising costs. We migrated to lower-cost connectors, reducing ELT costs by 80% with a parallel validation approach before cutover. For a specialty coffee chain, we migrated off a legacy warehouse to a right-sized platform. CPG brands with high transaction volumes are often the best candidates for cost optimization.

Subscription and replenishment analytics. For brands with subscription or auto-replenishment models, we build churn analysis, cohort retention, LTV calculation, and the billing platform integrations that subscription CPG businesses need. We have built this for both subscription nutrition and subscription apparel brands across multiple billing and storefront platforms.

From our podcasts: [Transforming CPG with AI at TICKR with Sam Kahn](/podcasts/eventual-consistency/ep-04-cpg-ai-tickr), and [The Back End Is the New Storefront](/podcasts/eventual-consistency/ep-18-agentic-commerce-retail-data).

We know the CPG data stack because we work in it every day. Storefront webhook schemas, marketplace API structures, subscription event models, retail distributor EDI formats. This is production experience across 5+ consumer brands, not theoretical knowledge from platform documentation. We also understand that CPG operates on tight margins, which is why we actively optimize data infrastructure costs alongside building the analytics layer.