Building a real-time retail platform across 100K+ stores

Retailers and consumer-packaged-goods brands could create more relevant experiences by acting on shopping behavior while a transaction was still in progress. Connecting data collection, targeting, offer delivery, and measurement in real time opened new product and business models across the network.

The platform had to work across the United States, Europe, and Japan. We shaped a shared operating model that could accommodate regional retailer systems, identity models, compliance requirements, infrastructure, and commercial practices.

As Director of Platform Product, I led the progression from Catalina's data and analytics foundations into a campaign and advertising platform built from zero for real-time store integration. We began the cloud program on AWS, then migrated the platform to Azure with Red Hat technologies as the operating and delivery model evolved.

At Red Hat Summit, I joined Julia White, then Corporate Vice President of Microsoft Azure, onstage to explain the transformation and the leadership decisions behind it.

The platform release introduced Catalina's new core for omnichannel, real-time activation across its retailer footprint. The announcement walked through the capabilities that connected campaign intent, audiences, channels, and delivery through one platform.

This was the product expression of the broader transformation: shared platform capabilities could support coordinated activation across physical stores and digital experiences instead of treating every channel as a separate system.

My Favorite Deals translated the platform into a personalized offer experience for consumer-packaged-goods brands and retailers. Live store feeds, shopper context, product data, and campaign decisions could shape relevant offers while a transaction was still in progress.

The launch made the operating model visible from store activity through decisioning and delivery. Retailers could deepen customer engagement, CPG brands could activate personalized value, and both could participate through the same platform and measurement model.

International expansion across the United States, Europe, and Japan drove 10% to 20% year-over-year growth in platform-led campaign revenue. The ML personalization product contributed roughly 10% year-over-year revenue lift.

The transformation showed how a platform creates value across an ecosystem: participants need shared interfaces, data responsibilities, incentives, and outcome measures that work as one operating system.

Selling the platform into Japan required more than translating an interface. Campaign creation and delivery had to accommodate local retailers, brands, product conventions, language, creative formats, and market-specific operating practices.

This Japanese coupon-production view shows the platform composing localized offer creative for Starbucks and AEON. It is tangible evidence of the system's progression from a US product into an international advertising platform serving Europe and Japan.

Before leading the broader platform overhaul, I advanced Catalina's data platforms. The catalog and exploration system gave teams one place to manage retailer stores, product dictionaries, audience inputs, and the reference data used by targeting, advertising, and analytics.

Faceted search and geographic store selection turned large retail datasets into usable campaign inputs. Product and platform teams could inspect the same definitions, build precise lists, and carry those choices into activation and measurement.