How a Detroit Boutique Cut $8K in Dead Stock with One Spreadsheet Audit

Marcus runs a boutique on Caniff Avenue — women's apparel, accessories, a few home goods. He was organized. He had a spreadsheet. He had a Square POS. And he had absolutely no idea he was sitting on $8,400 in inventory that would never sell.

The Situation

Marcus came to Keyuna Data Studio with a specific question: "I feel like I'm reorder too much, but I don't know what's actually moving." He'd been running the shop for three years, built his own inventory spreadsheet from scratch, and was tracking everything by hand — quantities, costs, retail prices for some items, not others.

What he didn't have: a system. What he thought he had: visibility.

The spreadsheet had 847 active SKUs. He'd been adding to it since day one without a consistent format. Some rows had cost prices. Most didn't. Categories were written in three different formats. Three items had zero quantity on hand but were still listed as active. He was reordering blind — guided by memory, not data.

Step 1: The Data Quality Scan

Before we touched a single analysis, we audited the dataset. 847 rows. Three people could have looked at this spreadsheet and drawn three different conclusions about what was actually in stock. Here's what the scan found:

57.6% completeness — meaning nearly half the inventory was missing the basic information needed to make a buying decision. Marcus had no idea. He'd been reordering against a spreadsheet that was half-blind.

Step 2: The Pareto Discovery

Once the data was cleaned and deduplicated (814 active SKUs after removing duplicates and zero-qty stale entries), we ran a category-level turnover analysis. The results were immediate:

The top 20% of SKUs by revenue (163 items) drove 65% of total annual revenue. The bottom 40% — 326 SKUs — generated just 8% of revenue. Marcus was maintaining carrying costs, shelf space, and mental overhead for hundreds of items that were effectively dead weight.

Step 3: Dead Stock Identification

We defined dead stock as any item with zero sales in the past 90 days AND cost basis below a threshold that made clearance economically irrational. 91 items met that definition:

Step 4: The Inventory Dashboard

We built Marcus a three-view dashboard: Inventory Overview, Turnover by Category, and Dead Stock Alerts. Here's what that looked like:

What Changed After

Marcus acted on the report in two phases. First, he cleared the 91 dead stock items via a weekend sale — priced to move at 40% off cost recovery minimum. He cleared $5,100 of the $8,400 in three weeks. The remaining $3,300 was either donated (tax write-off) or archived pending a potential pop-up event.

Second, he updated his reorder points using the velocity data. Within six weeks, his monthly purchasing had dropped from $3,800 to $3,200 — without any sales disruption. The math was simple: fewer slow-moving accessories meant more cash available for the tops that actually sold.

The full report cost $499. The spreadsheet audit took one afternoon.