Image Recognition (IR) has rightly earned its place in the CPG industries toolkit. It’s fast, scalable, and delivers shelf data in near real-time. But here’s the uncomfortable truth: no matter how powerful the technology, poor input data can quietly sabotage your results (just like any advanced technology).
This is the classic “garbage in, garbage out” principle, and it’s alive and well, even in the age of AI.
The hidden cost of bad data
According to Gartner, poor data quality costs organizations an average of $12.9 million per year. In the context of CPG brands, that cost shows up in more subtle, but equally damaging, ways. If your IR system is fed with flawed insights like outdated catalogues, mislabeled SKUs, or incomplete data regarding KPI, etc., it simply cannot deliver reliable in-store, shelf and SKU-level intelligence.
Your teams might be looking at real-time data, but that doesn’t make it accurate.
Misclassified products distort share of shelf and on-shelf availability metrics. Inaccurate pricing reference files trigger false alerts. Worst of all? Your field reps begin to doubt the data. That erosion of trust is hard to claw back.
AI doesn’t clean your data for you
There’s a popular misconception that AI magically fixes bad data. It doesn’t (we’ve debunked that and more in this blog). IR systems, like Trax, use machine learning to recognize products and shelf conditions, but they rely heavily on the accuracy of the source data, product images, hierarchies, SKU-level details and retailer-specific configurations.
As Thomas Redman, widely known as the “Data Doc,” puts it:
“If your data is bad, your machine learning tools are useless.”
That might sound blunt, but it’s spot on. IR reflects the shelf, but the intelligence it provides depends on how well your data infrastructure supports it. Think of it like a high-performance car. The engine might be world-class, but if you’re fueling it with contaminated fuel, you won’t get far.
Clean data makes everything sharper
The most successful CPG brands don’t treat data governance as a background IT task. They invest in building and maintaining accurate, centralized product catalogues. They align naming conventions across markets. They create structured hierarchies that make sense to both the tech and the humans using it.
McKinsey research found that organizations that prioritize data quality are 1.5 times more likely to report data-driven decisions improving operational efficiency. That’s not a marginal gain; it’s a competitive edge.
When your reference data is clean and your systems are well-integrated, IR becomes a multiplier. Recognition accuracy improves. Time-to-insight drops. Your field teams get reliable, targeted actions they can actually trust and act on. And so on.
Trust is the currency of execution
Sales reps are a smart, practical bunch. If your IR solution repeatedly flags missing products that are clearly on the shelf, or tells them to fix displays that don’t exist, they’ll stop engaging. They’ll default to legacy behaviors or ignore the alerts altogether.
That’s not a tech problem; that’s a trust problem.
Maintaining high data quality isn’t glamorous, but it’s mission critical. The best IR programs are built on consistent, disciplined foundations, not just flashy features and clever algorithms.
Final thought: what’s under the hood?
Before you expand or optimize your IR program, ask this: Are we giving it the right fuel?
A high-impact image recognition program requires:
You don’t need perfection. But you do need consistency, accuracy and a feedback loop that can catch and correct errors fast. When you have the foundations right, IR delivers what it promises: speed, scale, and shelf intelligence you can trust.
Let’s talk
Are you confident in the quality of your IR inputs? Or is your team constantly second-guessing the insights? Reach out to me directly or speak with the team at Trax. We’ve helped CPG leaders clean up their data and unlock the full potential of IR, and we’d be happy to do the same for you.
Give us some info so the right person can get back to you.
