Headline

Data Everywhere, Answers Nowhere: A $50M Home Goods Manufacturer

Lede

• Industry: Home Goods Manufacturing
• Size: $50M, cash-constrained
• Engagement: AI Leverage Sprint

The Challenge

A $50M home goods manufacturer was profitable on paper but fragile underneath. Cash was tightening. Margins were compressing. Purchasing decisions were reactive — driven by supplier minimums and gut feel rather than data. Marketing spend kept rising with no attribution model to show what was actually driving profitable revenue. And stale inventory was quietly consuming cash the four-person executive team couldn’t see, because their data lived in disconnected spreadsheets across sales, operations, and finance. They had the data. They didn’t have the decisions.

What We Did

• Diagnosed the decision gap across sales, inventory, and marketing — the data existed but lived in disconnected spreadsheets across departments. We identified where leadership was making decisions blind and designed an integrated analytics architecture, built by our data intelligence partner, that gave the executive team real-time visibility into performance by channel, customer, SKU, and campaign.
• Identified the hidden cash drain — mapped obsolete and slow-moving inventory that was quietly consuming cash and margin, and designed automated alert thresholds so the team could catch aging inventory before it became a write-off.
• Redesigned purchasing from reactive to predictive — designed a coordinated buying process that consolidated orders to hit factory minimums, unlock better pricing, and reduce shipping costs, replacing gut-feel ordering with forward-looking demand intelligence.
• Eliminated chronic stockouts — diagnosed the root cause of out-of-stock issues that were costing both sales and channel visibility, and designed inventory triggers to prevent recurrence.
• Brought accountability to marketing and trade show spend — designed attribution models tying campaigns directly to subsequent sales and margins, enabling ROI tracking by show, campaign, and product line, so the team could kill what wasn’t working and double down on what was.
• We did the strategic and diagnostic work; our data intelligence partner built the AI-powered tools. The result was a decision system the leadership team actually used — not another dashboard collecting dust.

The Result

• 17% sales increase — driven by better inventory availability, channel optimization, and more targeted selling.
• 8-point gross margin improvement (60% to 68%) — from smarter purchasing, better vendor pricing, lower shipping costs, and SKU rationalization.
• Net income doubled (4% to roughly 8% of sales) — the margin improvement dropped directly to the bottom line.
• Cash crisis resolved — went from severe cash constraints to several million dollars on the balance sheet by year-end.
• Negotiating from strength, not fear — with precise product-level profitability data, the company could push back on price-cut demands and selectively pull products where margins fell below threshold.

We had data everywhere and answers nowhere. The team didn’t just give us better reports — they gave us a decision system. For the first time, we could see exactly where we were bleeding cash, which products were worth fighting for, and which marketing dollars were actually working. We stopped guessing and started managing — CEO, $50M Home Goods Manufacturer

If this maps to what you’re seeing in your business, the diagnostic surfaces the exact breaks in 90 minutes.