We unified Dunn-Edwards' eCommerce, inventory, and CRM into a single operating system — connecting the entire digital stack for a 130-location manufacturer and retailer competing against Sherwin-Williams and Benjamin Moore.
Dunn-Edwards had built a strong brand and loyal customer base across 130+ locations, but their digital infrastructure was constraining growth. eCommerce, store inventory, and customer relationship data lived in separate systems that didn't talk to each other. A customer could browse online, see inventory in one system, but that data wouldn't sync to stores in real-time. Conversion rates suffered because the company couldn't surface real-time inventory at point-of-transaction. The friction cost them revenue every single day.
Worse, the marketing team couldn't leverage customer purchase history to drive targeted campaigns. Store managers couldn't see online demand signals to inform local stocking decisions. The entire organization was operating on stale data, shipping slower, and losing deals to competitors with integrated systems.
The constraint wasn't talent or brand. It was architecture. A fragmented operating model was leaving $18M+ on the table annually, and the company didn't have a system to capture it.
Deployed a real-time inventory sync across all 130 stores and eCommerce. When a customer orders online, live stock updates instantly. Store managers see online demand signals. Conversion rates jumped because browsers no longer hit "out of stock" pages that should have shown available inventory ten miles away. The system now routes orders to the nearest location with stock, reducing fulfillment costs and shipping times.
Built a unified customer profile that consolidated purchase history, online behavior, and location-based signals. Marketing could finally segment by intent and purchase history. Store teams could identify high-value customers walking through the door. The system powered personalized recommendations that increased basket size. Customer lifetime value tracking became visible for the first time, unlocking ROI clarity on marketing spend.
Deployed an agentic system that ingests online orders, checks real-time inventory across the network, selects optimal fulfillment location (minimizing cost and shipping time), and executes pick-pack-ship workflows autonomously. Store associates were freed from manual order triage; the system routed 92% of orders to fulfillment without human intervention. Throughput increased. Cost per order fell. Ship-to-delivery time dropped by 2 days on average.
Implemented a predictive layer that forecasts demand by product, location, and season. The system automatically recommends inventory allocation to stores, cutting stockouts by 34% while reducing excess inventory carrying costs. Store managers no longer rely on gut feel; they work from a system-generated allocation that reflects both historical patterns and current demand signals across the entire network.
Online conversion lifted from 2.1% to 3.0% in the first 90 days post-launch, then to 3.4% by month six. Real-time inventory visibility eliminated "out of stock" friction and enabled personalized product recommendations powered by the unified customer data layer. For a company processing $40M+ in annual eCommerce revenue, 43% conversion improvement translates to $18M+ in incremental annual revenue.
Average ship-to-delivery time reduced from 4.2 days to 2.1 days. 92% of orders fulfilled autonomously without store associate intervention, freeing 8-12 FTE per location to focus on customer service and sales.
Stockout rate fell 34%; excess inventory costs reduced by $2.1M annually through better demand forecasting and allocation optimization across the 130-store network.
Store managers and operations teams transitioned from manual data management to system-driven decision making. Real-time visibility eliminated daily friction calls between eCommerce and retail operations.
We didn't start by building. We spent two weeks mapping the cost of fragmentation—every failed conversion, every missed fulfillment optimization, every day of stale inventory data. The cost was $42M annually. With that clarity, the investment case was obvious. The organization aligned quickly because we diagnosed the real constraint, not a vanity metric.
We didn't hand off to a contractor or systems integrator. Our team embedded with store operations, customer service, and marketing for the full six-month build. We designed the system around how work actually moves through the organization, not how it should, theoretically. That rigor shortened the deployment timeline and eliminated post-launch friction.
Most retail platforms fail because they add features on top of bad data. We built the unified data layer first, clean, normalized, real-time. Only then did we deploy the inventory sync, customer profiles, and predictive systems on top. This substrate approach prevented the system from becoming brittle or slow as scale increased.
How to deploy autonomous workflows that handle order-to-fulfillment, demand forecasting, and supply chain orchestration without fragile integrations.
The operational transformation framework we use to identify trapped value, design the system to release it, and prove ROI, applied across portfolio companies.
How to build infrastructure that compresses decision cycles, enables faster shipping, and creates competitive advantage through systems, not software alone.
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