VibeIQ Raised $22.5M to Fix Product Decisions. Here's What DTC Owners Can Steal for Free

$22.5M Says AI Is About to Rewrite How DTC Brands Decide What to Sell

VibeIQ, an AI-native product creation platform, closed a $22.5 million funding round led by Volition Capital with participation from Venture Guides, according to the funding announcement. The company's customer roster already includes New Balance, Vera Bradley, Converse, and Kizik. Founder and CEO Brian Lindauer built the platform to help product teams make merchandising and assortment decisions with data instead of gut feel and a shared spreadsheet nobody trusts.

If you run a DTC or ecommerce brand doing $500K to $5M in revenue, you do not have a merchandising team the size of New Balance's. You probably have a founder, a spreadsheet, and a supplier minimum order quantity that forces bets months before you know if a product will sell. That gap, between enterprise-grade product decision tools and what a small operator can actually access, is closing faster than most owners realize.

The Contrarian Angle: Your SKU Count Is Probably Your Biggest Hidden Cost

Every DTC founder wants more products. More colors, more sizes, more variants, more shelf space in the algorithm. It feels like growth. It is usually the opposite. When a business fully allocates its costs, meaning it stops pretending warehousing, markdown risk, and cash tied up in unsold inventory are free, a large share of SKUs turn out to be quietly unprofitable.

This is not a niche problem. Research across 50 ecommerce brands found that an average of 62% of SKUs were unprofitable once every cost was properly allocated, while the top 25% of SKUs generated roughly 85% of total contribution margin, according to a SKU-level profitability study from Fairview. A separate analysis of the same pattern found that a hero SKU, the one every founder points to as proof the catalog works, can sit in the bottom quartile of the catalog by contribution margin once ad spend, returns, and fulfillment are loaded in, per Eightx's breakdown of bestseller economics. Founders rarely notice because the accounting that would reveal it, fully allocated contribution margin per SKU, requires more discipline than most $500K-$5M operations have built into their monthly close.

The AI product-decision tools now emerging, VibeIQ among them, exist specifically to close this blind spot before the sample order gets placed, not after the inventory is sitting in a warehouse depreciating in value every month it does not sell.

What the Data Actually Shows

Research on SKU rationalization consistently finds that a small fraction of a product catalog drives the overwhelming majority of contribution margin, while a much larger share barely breaks even or loses money once true carrying costs are included. The average ecommerce brand carries 25-35% more inventory than it actually needs at any given time, according to Canopy's overstock management research, and every dollar of dead stock costs 20-30 cents a year just to hold, per Eightx's inventory turns analysis. Markdowns on those same SKUs are frequently the single largest unplanned expense line in a DTC brand's annual P&L, larger than most owners' initial estimate when they are only tracking it informally.

| Factor | Typical Finding | Why It Matters for a $500K-$5M Brand | |---|---|---| | Share of SKUs generating most contribution margin | Top 25% generates ~85% of contribution margin | Most of your revenue rides on a small set of proven winners | | Share of SKUs unprofitable at full cost allocation | Roughly 62% once properly measured | Many "revenue-positive" SKUs are margin-negative once storage and markdown risk are counted | | Excess inventory carried vs. actual need | 25-35% above what is needed | This is capital you cannot deploy toward better-performing products | | Annual carrying cost of dead stock | 20-30% of inventory value | Erodes margin on products that already looked marginal |

The uncomfortable implication: the instinct to keep adding SKUs to chase incremental revenue is often the exact decision destroying your margin. Seven of ten public DTC and consumer brands reviewed in recent SEC filings now disclose SKU rationalization as a deliberate margin-recovery lever, according to Eightx's analysis of ecommerce SKU counts by revenue band, which is the public-company version of the same discipline a $2M operator needs to run privately. An AI system that models expected sell-through, carrying cost, and markdown risk before you commit to a purchase order is not a nice-to-have. It is closer to a seatbelt.

The Mechanism: How AI Product-Decision Tools Actually Work

Platforms like VibeIQ pull together historical sell-through data, current trend signals, and supplier lead-time constraints into a single model that scores a proposed product or variant before it ever gets ordered. Instead of a founder guessing that a new colorway will sell because a competitor's version looked popular on social media, the system estimates expected demand against the actual cost structure of carrying that SKU: minimum order quantity, storage, and the realistic odds of a markdown if demand falls short.

This does not remove judgment from the process. It removes the specific failure mode where judgment operates on incomplete information. A founder who intuitively feels good about a new product line is not wrong to trust that instinct. The instinct becomes dangerous only when it is the sole input, with no counterweight from actual sell-through history or margin math. The AI layer is that counterweight. It does not replace the decision-maker. It gives the decision-maker a second, unemotional opinion before the purchase order gets signed.

I think of this as Data's DNA applied to merchandising: every product decision should trace back to a specific, checkable number, not a feeling about what looked good in a trend report. Contribution margin, not gross margin and not revenue, is the correct number to trace back to, because it is the only figure that nets out fulfillment, payment processing, and returns at the SKU level rather than blending them across the whole catalog, per Eightx's contribution margin framework. A brand that cannot answer "what is the expected contribution margin on this SKU at realistic sell-through" before committing capital to it is running on vibes, however sophisticated the spreadsheet looks.

Case Study: The Founder Who Learned Contribution Margin the Hard Way

I once advised a DTC apparel brand doing roughly $2.4M in revenue that had grown its catalog to over 300 active SKUs chasing every trend that looked promising on social media. Revenue was climbing. Cash was tightening, and nobody could explain why, because the P&L at the top line looked healthy. When we ran a full SKU-level contribution margin analysis, allocating actual warehousing cost, markdown history, and payment processing fees down to each product, we found that roughly 60% of the catalog was either break-even or actively losing money once fully costed. A tight core of about 40 SKUs was generating almost all of the real profit.

We did not need an AI platform to find this. We needed six weeks of unglamorous spreadsheet work the owner had never made time for. What an AI product-decision tool changes is not whether this math exists. It changes when you see it: before you place the order, not eleven months later during a fire-sale markdown clearing shelf space for next season.

I learned a version of this lesson somewhere far removed from retail. Recovering from open-heart surgery years ago, I had a lot of forced, quiet time to think about what actually matters versus what only looks like it matters. In the hospital, the vital signs on the monitor did not care how confident I felt. They were the only honest measure of what was actually happening inside my body, independent of how I felt about my own recovery. A SKU-level contribution margin report works the same way for a business. It does not care how good the product photos look or how confident the founder feels about the new line. It is the honest number, and ignoring it in favor of how a decision feels is how healthy-looking revenue turns into a cash crisis nobody saw coming.

The Honest Risk

This could blow up in a specific way worth naming directly. An AI product-decision tool is trained on historical sell-through data. If your brand is genuinely trying to launch something categorically new, a product with no comparable historical pattern, the model has nothing reliable to learn from and may score a legitimately good idea as risky simply because it has never seen anything like it before. Treat AI merchandising scores as a strong prior, not a verdict. A founder who blindly follows an algorithm's risk score on a genuinely novel product idea can talk themselves out of the one bet that would have actually differentiated the brand.

There is a second risk on the vendor side. A $22.5M raise means VibeIQ and platforms like it need revenue growth to justify that valuation, and enterprise-focused platforms have a track record of pricing small operators out once the product matures and the target customer shifts upmarket toward brands the size of New Balance. Do not build your entire merchandising process around a tool priced for a $2M brand today if the same company's roadmap is clearly aimed at nine-figure enterprise accounts tomorrow.

Your Next Step This Week

Pull your last 12 months of sales data by SKU. For each product, estimate the fully allocated cost, unit cost, storage cost for the average number of months it sits before selling, and any markdown taken. Rank every SKU by true contribution margin, not revenue. You will very likely find a small core of products doing most of the real work and a long tail quietly bleeding cash. That exercise costs you an afternoon with a spreadsheet. It is the same math the $22.5M platforms are now automating, and you do not need the funding round to start applying it.

Doctrine Connection: Verification Beats Optimism

Verification beats optimism, and nowhere is that more expensive to ignore than in a product catalog built on hope instead of contribution margin. A new SKU that "feels right" is optimism. A new SKU that clears a documented sell-through and margin threshold before the purchase order goes out is verification. AI product-decision tools like VibeIQ are, at their core, a way of forcing verification earlier in the process, before capital is committed rather than after the markdown bin is full. You can build that same discipline manually before you can afford the platform. The discipline is the point. The software is just faster.

FAQ

Q: What does VibeIQ actually do? VibeIQ is an AI-native product creation and merchandising platform that helps brands score proposed products and variants against historical sell-through, cost structure, and markdown risk before committing to a purchase order.

Q: Do I need an enterprise budget to apply this thinking to my business? No. The underlying discipline, fully allocated contribution margin analysis per SKU, can be done manually in a spreadsheet for any catalog size. The AI tools speed this up and add trend signals, but the core math is accessible to any $500K-$5M brand willing to do the work.

Q: How do I know if my SKU count is hurting my margin? Calculate contribution margin per SKU including storage cost and markdown history, not just revenue minus unit cost. If a meaningful share of your catalog is break-even or negative once fully costed, you likely have SKUs that should be cut or reworked.

Q: Should I cut every unprofitable SKU immediately? Not automatically. Some low-margin SKUs serve a strategic purpose, drawing in new customers or supporting a bundle, so weigh that role before cutting. But any SKU that is both unprofitable and strategically pointless is a clear candidate for removal.

Q: What is the biggest risk of relying on AI merchandising tools? They are trained on historical data, so a genuinely novel product with no comparable sales history may be scored as riskier than it deserves. Use the AI score as one input alongside founder judgment, not a final verdict.

Jeff Barnes is the founder of Digital Evolution Marketing Group (demg.ai). This article is for informational purposes only and does not constitute business or investment advice. The frameworks, tools, and strategies discussed reflect the author's operational experience and may not apply to every business context.