The Great Retail Data Divide

I’ve been tracking retail data for over 33 years, and for the last two years we have seen a major disconnect between government survey methodology and actual transaction reality. The June 2025 US retail data release offers a perfect case study in why the government needs to rethink how it measures market health. I’m thankful this crosses administrations, as it hopefully removes conversation of political bias one way or another.

Here’s what caught my attention: While both the U.S. Census Bureau and the CNBC/NRF Retail Monitor reported identical +0.6% month-over-month growth for June, dig one layer deeper and you’ll find dramatically different stories about what’s actually happening in American retail. If you are looking at the government data to determine what is really happening with customers and making investment decisions upon that day, you should think twice.

Two Methodologies, Two Realities

The fundamental difference isn’t just academic—it’s operational. The Census Bureau surveys roughly 4,800 retail firms and extrapolates that data across 3+ million businesses. The CNBC/NRF Retail Monitor tracks actual transactions from 140+ million credit and debit cards, capturing nearly 9 billion transactions annually.

Census Approach:

• Survey-based sampling with statistical extrapolation
• Subject to non-response bias and revision cycles
• Released 11-17 working days into each month
• Benchmarked against annual survey data

NRF/CNBC Monitor:

• Real transaction data from actual consumer spending on 140m cards
• No revisions—transaction data is final
• Near real-time visibility into market behavior
• Captures spending patterns as they happen

Key Observations on Retail Data Reliability:

• Survey extrapolation creates systematic bias: 4,800 firms representing millions creates margin for error
• Transaction data captures market reality: Real spending behavior vs. estimated reporting
• Category-level variance reveals measurement gaps: Where methodologies diverge most
• Revision cycles obscure real-time decision making: Census data changes; transaction data doesn’t

Where the Stories Diverge

The June 2025 category-level data reveals striking differences that should concern anyone making retail investment decisions:

Month-over-Month Growth Comparison:

These aren’t rounding errors—they’re fundamentally different assessments of sector performance that are quoted widely that move markets. The Census data suggests broad-based strength across categories, while transaction data shows widespread weakness.

Year-over-Year Reveals Even Larger Gaps:

The annual comparisons are even more telling:

• Building & Garden Supplies: Census shows -1.1% vs. transaction data at -5.33%
• Health & Personal Care: Census reports +8.3% vs. actual spending growth of +3.47%
• Furniture & Home Furnishings: Census claims +4.5% vs. transaction reality of -1.14%

What This Means for Strategic Decision Making

The implications extend beyond data curiosity or basic analysis. I’ve watched companies miss critical market shifts because they relied on survey data instead of transaction reality.

For Everyone:

• Planning based on broader sector or economic data will miss actual demand patterns
• Traditional economic indicators may be lagging market reality
• Economic stimulus effectiveness from the Fed or others requires real-time measurement
• Consumer spending patterns are shifting faster than surveys can capture
• Monetary policy decisions need transaction-level market feedback

The Path Forward

The retail industry is experiencing its fastest transformation in decades. Digital payments, omnichannel commerce, and changing consumer behavior are outpacing traditional measurement methodologies. Much like the CPI as compared to Truflation, it looks like the government is using outdated methodology. Yes, the government data takes into consideration cash transactions which explains a small portion, but the sample size is the main culprit.  Where the CNBC/NRF Retail Monitor index fed by Affinity has a sample of roughly 11% of all transactions, the US Retail Census now is far less than 1% of retailers. 

This isn’t about abandoning government statistics—it’s about recognizing their limitations and creating a combined public/private partnership for better data. 

The Bottom Line

I believe we should have a fundamental shift in how economic data is collected and analyzed. The emergence of high-frequency transaction datasets represents the most significant advancement in retail measurement since modern survey systems were established. New AI tools as well are bringing even more insight and clarity. And perhaps the best part, the real-time transaction data removes most arguments of political bias in the data and timing.

The smart money is already adapting. The good news is retailers have their own ground truth of what is happening in their operations. But the vendors calling on those retailers and others?  Not so much.

The question isn’t whether transaction-based data is better than survey-based data—it’s whether we can afford to make critical business decisions using methodologies that increasingly miss market reality and are outdated.