Why I'm Building a CPG Automation Platform (And What 20 Years Taught Me)

Opening Hook

In my mid-twenties, I transitioned from a sales and account management role—working on one of the UK's biggest customer accounts—to an internal 'retail marketing' position. That's when I noticed something odd.

My new role required me to create and implement a '5P' strategy for one of our major healthcare categories. One of those P's was promotions. Simple enough, right? Except I needed to answer what seemed like straightforward questions: How many promotions did we run in the last two years? How many were we planning for the year ahead? What was the price, feature space, quality, and mechanic of each promotion? Which promotions were off-strategy?

The problem? Nobody could answer these questions without weeks of manual work. The data existed—scattered across retailer portals, spreadsheets, emails, and someone's memory. Getting to a single source of truth meant an analyst copying and pasting between systems for days on end.

I remember thinking: "This can't be normal." But it was. And twenty years later, across multiple companies and countries, I'm still seeing the same problem.

The Pattern Recognition

Over the next two decades, I worked across multiple CPG companies in different countries. The names changed. The products changed. The markets changed. But the problems? They were eerily similar.

Data silos everywhere. Sales data lived in one spreadsheet. Inventory data in another. Digital performance in a third. None of them talked to each other. Every weekly business review meant someone spent hours copying and pasting to create a single view of what was happening.

Master data mapping nightmares. You couldn't join internal and external data because product codes didn't match. Customer hierarchies were inconsistent. What one retailer called "SKU-1234" was something completely different in your ERP system. These mapping issues didn't just slow down analysis—they made critical commercial process meetings throughout the year nearly impossible to run effectively.

The dashboard delusion. Companies kept thinking that if they just built another dashboard, their analytics problems would disappear. But dashboards without proper data foundations are just expensive ways to visualize bad data. The real problem wasn't visualization—it was the mess underneath.

Point solution addiction. Everyone wanted the quick fix. Build a tool for this specific problem. Create a workaround for that issue. But without data foundations, these point solutions just created more silos. More systems that didn't talk to each other. More technical debt.

Key person risk. The analyst who built that complex spreadsheet everyone relies on for quarterly planning? They just left the company. Now nobody knows how it works or how to update it. I've seen multi-million dollar decisions delayed because the one person who understood the model was gone.

Why These Problems Persist

If these problems are so common, why hasn't someone solved them? The answer is simple: the solutions that exist weren't built for mid-market CPG brands.

Enterprise solutions are out of reach. The big platforms—SAP, Oracle, IBM—cost millions to implement and require armies of consultants to maintain. They're built for Unilever and P&G, not for brands doing $50-500M in revenue.

Custom builds require teams you don't have. You could build it yourself, but that means hiring data engineers, maintaining infrastructure, and constantly updating integrations as retailers change their APIs. Most mid-market companies can't afford a full internal tech team just to solve data problems.

Point solutions don't talk to each other. So you buy a tool for trade promotion analysis. Another for retailer data ingestion. Another for demand planning. Now you have five systems that don't integrate, and you're back to spreadsheets to tie it all together.

The default becomes Excel. It's the universal glue. It's familiar. It's flexible. And it's killing your team's productivity. But what's the alternative when everything else is too expensive or too complicated?

The mid-market has been stuck between enterprise complexity and spreadsheet chaos. That gap is what I'm building to fill.

The Realization

The turning point came when I moved to our Australian subsidiary.

Within weeks, the sales director pulled me into a meeting and described the problems they were facing. Data scattered across systems. Manual reconciliation taking days. Critical commercial decisions delayed because nobody could get a clear view of what was actually happening.

Then he asked: "Can you build us the same solution you rolled out in the UK?"

That question stopped me cold. Same problems. Different country. Different team. Different retailers. But underneath it all, identical challenges.

That's when it hit me: I'd been solving the same problems over and over, just in slightly different packaging. Trade promotion reconciliation in the UK. Retailer data integration in Australia. Inventory planning in another market. The specifics varied, but 80% of what I was building was the same underlying infrastructure.

I was treating symptoms, not the disease. Each engagement meant building custom solutions from scratch. But these problems didn't need custom solutions—they needed a platform. A foundation that could be deployed once and configured for each brand's specific needs.

Here's what I realized: only things that add significant strategic value should be built as custom solutions. Getting retailer data into a usable format? That's not strategic—it's infrastructure. Master data mapping? Infrastructure. Automated trade promotion reconciliation? Infrastructure.

The strategic work—the analysis, the insights, the decisions—that's where commercial teams should spend their time. Not wrestling with data foundations.

So I made a decision: build the platform. Make it reusable. Make it deployable behind a client's firewall for those concerned about data privacy. And most importantly, build it against real client problems, not theoretical ones.

What I'm Building

A CPG automation platform that handles the 80% of work that's the same across every company, so your team can focus on the 20% that's actually strategic.

Here's what that looks like in practice:

Automated data ingestion. Retailer data flows in automatically—no more logging into 18 different portals, downloading CSVs, and hoping the file format hasn't changed. The platform handles the extraction, transformation, and loading.

Master data mapping that actually works. Product codes, customer hierarchies, location taxonomies—all mapped and maintained in one place. When a retailer calls your product one thing and your ERP calls it another, the system reconciles it automatically.

Trade promotion management end-to-end. From planning through reconciliation, the platform tracks promotions, monitors performance, flags conflicts, and handles the post-event analysis. What used to take weeks happens in minutes.

Data foundations, not dashboards. A proper data warehouse where internal and external data can be joined cleanly. Where your commercial process meetings have reliable, consistent data. Where building a dashboard is trivial because the hard part—the data quality—is already solved.

95% automation doesn't mean zero human oversight. It means your commercial team spends their day making decisions, not preparing data. It means your Monday morning meeting starts with insights, not "let me check the numbers and get back to you." It means your best analysts are analyzing, not copy-pasting.

And because it can be deployed behind your firewall, you maintain complete control over your data. No concerns about leakage or privacy.

How I'm Building It

This isn't vaporware or a theoretical framework. I'm building this platform through real client engagements, one proven module at a time.

Every project I take on now serves two purposes: it solves an immediate problem for the client, and it adds a validated, reusable component to the platform. When I build a retailer data integration for one client, that module becomes part of the core platform. When I solve master data mapping for another, that capability is captured and made reusable.

My current clients are essentially design partners. They're working with real data, real business problems, and real constraints. If a solution doesn't work in production, I know immediately. If the automation breaks under real-world conditions, we fix it together.

This approach has a major advantage: every component is battle-tested before it becomes part of the platform. I'm not building features based on what I think CPG brands need. I'm building based on what they're actually using and relying on every day.

It also means the platform grows with clear priorities. The modules I build first are the ones solving the most common, most painful problems. The edge cases and nice-to-haves come later, if at all.

This is the opposite of how most software is built. I'm not raising millions, hiring a team, and hoping the market wants what we create. I'm proving the need first, building the solution second, and letting real client value drive every decision.

Who This Is For

This platform is built for mid-market CPG brands and retailers—companies doing $50-500M in revenue who are stuck in no-man's-land.

You're too big to run everything in spreadsheets effectively. But you're too small to justify the $5M implementation of an enterprise system that requires a full-time IT team to maintain.

You're competing against giants who have entire departments dedicated to data infrastructure. They have the resources to build custom solutions. They can afford the enterprise licenses. They have teams of data engineers.

You don't. And you shouldn't have to.

The giants can build this themselves. My mission is leveling the playing field for everyone else. Your brand shouldn't be at a structural disadvantage just because you don't have Unilever's IT budget.

If you're a commercial leader who's tired of your team spending 80% of their time on data wrangling and 20% on actual analysis, this is for you. If you're frustrated watching talented analysts do work that should be automated, this is for you. If you know your data problems are solvable but can't afford the enterprise solution, this is for you.

Join the Journey

I'm building this platform in public because I believe the CPG industry needs it—and I want to build it with the people who will use it.

If you're ready to solve these problems now, working with me today means you're getting early access to the platform as it's being built. You'll influence what gets prioritized. You'll shape how features work. And you'll benefit from automation that's custom-fitted to your actual problems.

If you want to follow along, connect with me on LinkedIn. I'm documenting this journey—the wins, the challenges, the lessons learned. You'll see what it actually takes to build a CPG automation platform from the ground up.

If you're skeptical, good. You should be. The industry is full of people promising transformation and delivering PowerPoints. That's why I'm building with real clients solving real problems. Results speak louder than pitches.

The mid-market CPG space doesn't need another dashboard vendor or another consultant selling reports. It needs infrastructure that works. It needs automation that actually saves time. It needs solutions that level the playing field.

That's what I'm building. Come along for the ride.