Most small business owners know their data has answers in it. They just don't have time to dig through 2,800 rows of a spreadsheet to find them. This is the story of how a Detroit coffee shop owner discovered she was leaving thousands on the table — and how it took one afternoon of analysis, not months.
The Client
Detroit Coffee Co. is a two-year-old independent coffee shop on Livernois Ave. The owner — let's call her Keisha — had been running the business from a mix of handwritten receipts, a Square export, and a Google Sheets document she'd been maintaining since opening day.
She came to Keyuna Data Studio looking for a simple insight report. "I just want to know if I'm doing better this year than last year." What we found was significantly more interesting.
Step 1: Data Quality Scan
Before we ran any analysis, we audited the data. 2,841 rows of transaction data — but the initial scan found three problems that would have quietly corrupted any revenue calculation:
Without cleaning these records first, any revenue figure reported to Keisha would have been inflated by an estimated $840 — a small number, but enough to send her a wrong signal. Cleaning came first.
Step 2: Revenue Patterns the Owner Didn't Know
After cleaning, we ran a day-of-week revenue analysis. Keisha's assumption: Friday afternoon was her biggest block. Her anecdotal memory told her that's when the post-work crowd peaked.
The data told a different story:
Saturday was her actual peak. But more interesting was the gap between Saturday and the rest of the week. With only one staff member on shift during Saturday morning rushes, customers were waiting 12–15 minutes. Keisha had no idea. No one had told her. The data did.
Step 3: The Hidden Revenue Opportunities
Three findings stood out once we dug into the full dataset:
The Dashboard We Delivered
Keisha received a shareable dashboard with three views: Overview, Day Patterns, and Customer Trends. Here's a preview of what that looked like:
What Changed After
Keisha implemented two changes in the first month based on the report: she started staffing an extra person on Saturday morning, and she moved the pastry display from behind the counter to eye-level at the register. By end of month one, Saturday wait times dropped from 14 minutes to 5, and food attachment rate in the afternoon window rose 22%.
The full revenue impact won't be measurable until the quarter closes, but our conservative estimate based on the data is $3,000 in the first quarter alone — from two changes that cost nothing to implement.
The report cost $499. The insight paid for itself in the first three weeks.
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