You Know More Than You Think

Why the corner donut shop has better data than most Fortune 500 companies realize

4 minute read

I was picking up donuts this morning when the cashier mentioned they were out of glazed again. “Happens every day around 10 AM,” she said with a shrug.

The owner overheard and jumped in: “We never know how many to make. Some days we sell a few thousand, some days only a few hundred. It’s impossible to predict. We just make about the same every day–just like always.”

Forty-three years in business (I remember coming here when we first moved to the area in 1986). Same corner. Same customers. Plus now they’re on DoorDash, Grubhub, and have a loyalty program that tracks customer preferences digitally. Yet somehow, it’s “impossible to predict.”

The thing is, between their cash register, delivery platform data, and loyalty program analytics, they’re generating more customer insights than most Fortune 500 companies had access to a decade ago. The owner just doesn’t realize he’s sitting on a goldmine of interconnected data sources.

The Data Small Businesses Already Have

Every small business generates more useful data than they realize. That donut shop captures:

  • Daily sales by item and time across in-store, DoorDash, and Grubhub
  • Weather patterns (rainy days mean fewer walkers but more delivery orders)
  • Seasonal trends (holidays, school schedules)
  • Day-of-week variations across all channels
  • Customer return patterns through their loyalty program
  • Delivery heat maps showing neighborhood demand
 
Most small businesses have 2-5 years of transactional data sitting in their POS systems, plus delivery platform analytics and customer loyalty data, completely unused for decision-making.

According to Salesforce’s Small and Medium Business Trends Report, 92% of SMB marketing teams now use CRM technologies to track customer behavior, yet many still struggle with basic inventory and operational decisions. The report shows that 71% of SMB marketers have defined AI strategies, but 65% of customers still expect companies to adapt to their changing needs—suggesting the gap between data collection and actionable insights.

The gap isn’t access to data—it’s knowing what to do with it.

What Your Data Actually Tells You

Take that donut shop. Three months of combined data could reveal insights such as these examples:

What Three Months of Donut Shop Data Reveals

40%
Tuesday morning spike
50%
Delivery increase on rainy days
3:1
Glazed vs other flavors
60%
Higher loyalty member spending

None of this requires expensive analytics software. A simple spreadsheet with daily sales can show these patterns within weeks.

 
The cost of not knowing these patterns? That donut shop throws away roughly unsold donuts daily while running out of their bestseller before noon and missing hours of potential sales (and I go home with a muffin instead of a dozen glazed).

Simple Analytics for Real Businesses

You don’t need a data science degree. Here’s what you can do today without any advanced tooling or paid services:

Start with what you track anyway. Your POS system, appointment book, or even a notebook contains patterns. Look for:

  • Which days are consistently busy or slow
  • What times see the biggest rushes
  • Which products move fastest
  • When you typically run out of popular items

Use the weather as a variable. NOAA historical weather data is free and often explains mysterious sales variations. Rain affects restaurants differently than retail shops.

Track missed opportunities. That’s data too. When you run out of something popular, write it down. When someone asks for something you don’t carry, note it. These “negative sales” often matter more than what you did sell.

The Small Business Administration found that businesses tracking inventory turnover rates improved profitability by an average of 18% within the first year.

Making Decisions with Incomplete Information

Perfect data doesn’t exist, even for Fortune 500 companies. The goal isn’t prediction perfection—it’s making better decisions with what you know.

That donut shop could:

  • Make 20% more glazed donuts on Sunday mornings
  • Reduce overall production on rainy days while keeping popular items stocked
  • Experiment with afternoon specials during school holidays

Just like any good product, track which experiments work and which ones don’t. Iterate, track again, and improve!

This isn’t about complex algorithms. It’s about paying attention to patterns already in your business and testing small changes.

 

“Data is just a sophisticated way of paying attention to your customers.”

— Jim Barksdale, former Netscape CEO

The Competitive Advantage Hidden in Plain Sight

Large companies spend millions on market research and customer analytics. Small businesses get the same insights naturally—if they look.

You know your customers personally. You see seasonal patterns firsthand. You hear complaints directly. Your data doesn’t need to be big to be valuable.

The donut shop owner knows Mrs. Peterson comes in every Thursday for a dozen mixed. He knows the construction crew stops by at 6:30 AM sharp. He knows weekend mornings are slower until the church crowd arrives.

He just hasn’t connected those observations to his production decisions.

What patterns are hiding in your business data that you haven’t connected to decisions yet?