Wrong answer to the right problem

The Real Job is Picking the Right Problem
The most dangerous thing you can do in a startup is execute brilliantly on the wrong thing. It's when you lack a forcing function to step back and ask:

• Is this actually the right motion?

• If this works…does it move the metric that matters?

• Am I optimizing this because it's familiar or because it's high-leverage?

Welcome to a new Marketing in a Box weekly segment called Wrong answer to the right problem, where we dissect one sharp, well-executed tactic that looked like progress and explain why it didn't matter.

Not because it failed but because it solved the wrong layer of the problem.

Was this email forwarded to you?

Segment #01: The product feed that didn't fix growth

🎯 The Setup

As CEO of TeeFury, a once-iconic daily t-shirt site that had just posted 5 straight years of declining revenue, I decided to prioritize Google Shopping and update the product feed. TeeFury's feed lived in a Google Sheet which wasn't ideal. There was an instance when we added new SKUs to the sheet but dragged the price column. The result was t-shirts increasing in price by $1 per row, leading to some t-shirts that were priced at over $600. We didn't catch that for a few days.

We migrated the product feed to ProductsUp, a feed management system that I had used successfully at my former company (a direct competitor). This was an obvious upgrade (and something I was well familiar with):

• Cleaner bulk editing

• Dynamic sale windows

• Image testing and attribution control

• SKU labeling across 3M+ products

Felt like a grown-up move. And tactically, it was.

⚠️ The Wrong Answer

We treated infrastructure like strategy. Founders love platform upgrades, especially the ones that feel clean, scalable, and technical. And on paper, this migration had it all:

• Automated syncing instead of manual uploads

• Structured product data instead of messy rows

• SKU pruning logic and better error handling

Here's the trap: it solved for execution efficiency not revenue performance.

We weren't a logistics company. We were a long-tail ecommerce marketplace. And the real constraint on growth wasn't slow feed uploads or a messy Google Sheet.

⚡It was irrelevant, low-intent, low-margin SKUs flooding paid campaigns.⚡

We didn't need to ship faster. We needed to decide more clearly what was worth shipping and what would win in a commoditized environment where we couldn't outspend our competition. But by making the migration the central focus, we created the illusion of progress:

• The feed looked better

• The tech stack sounded more mature

• Everyone felt busy and productive

But it didn't address the root cause: Google Shopping can't print money if your inputs are trash. We built a smarter machine and filled it with garbage.

🧩 The Real Problem

We were solving for scale before solving for signal. Google Shopping is brutally simple:

• It thrives when you feed it products people are actively searching for

• It fails when you throw the whole catalog at it and hope something sticks

TeeFury had millions of SKUs. But most had no demand, no margin, and no match to high-intent queries. We couldn't outspend our competition. And most of the SKUs we advertised were also in our competitors' feeds…with better pricing and more aggressive discounts.

Our constraint wasn't infrastructure, it was product selection. I mistook TeePublic's (my former company) need to migrate the product feed as my current company's need. But TeePublic was operating in a much different context and trying to solve different problems when we migrated to ProductsUp.

TeePublic needed a better feed system.
TeeFury needed a smarter SKU strategy.

One that answered:

• Which products get surfaced?

• Which ones generate profitable clicks?

• Which align with the right queries, at the right time, for the right margin?

The right problem was: How do we make Google Shopping profitable in a long-tail marketplace? But the answer we gave was: Let's clean up our feed tech. Wrong lever, wrong layer, wrong result.

✅ The Right Lever

Build a SKU ranking system that capitalizes on capturing demand. Prune 90% of the feed. Rebuild Shopping around contribution margin rather than catalog size.

We didn't need a "complete" feed. We didn't need a billion SKUs like another competitor, Redbubble. We needed a curated, high-intent, margin-aligned subset of the feed: updated dynamically, ranked by performance, and prioritized with unique content that shoppers couldn't get anywhere else.

This means stepping back from what’s technically possible (sync every SKU, test every variation) and asking: "What are the fewest products we can promote to drive the most profitable growth?"

What That Looks Like in Practice:

⚡ Rank SKUs by signals that matter by using first-party data to create tiers and tells us what actually resonates with real buyers.

• Impressions + CTR (Do they get seen and clicked?)

• Add-to-cart and purchase rates (Do they convert?)

• Margin after returns and discounts (Do they profit?)

• Page engagement (Are users browsing variants?)

• Time on site or bounce (Do people stay or nope out?)

⚡ Organize SKUs into performance-based buckets with differing bids and budgets.

• Best: top 1-3% of SKUs with high ROAS, strong contribution margin %

• Average: middling performers, potential upside with better assets

• Poor: low intent, low click, low margin

• New: SKUs published in the last 7 days (eligible for "experimental budget")

Use this structure to:

• Build campaigns by SKU tier

• Auto-pause bottom-tier performers

• Surface "best" in Shopping, Discovery, and Meta

• Keep "new" in lightweight test loops

⚡ Test only where it matters.

Want to test product titles, images, landing page variants, or dynamic pricing? Only do it on top SKUs. Most feed testing is wasteful because it assumes every SKU deserves attention. But in a long-tail business, 95% of SKUs are noise. Don't optimize dead weight. Redirect all testing, creative variation, and budget rotation toward the SKUs that can actually move revenue.

⚡ Align campaign structure to contribution margin. Most teams build Shopping campaigns by product category. But that's cosmetic. You should structure campaigns around:

• Contribution margin tiers

• Price bands tied to ROAS goals

• Intent signals (e.g., branded vs. non-branded queries)

This lets you bid differently based on what a sale is worth and prune aggressively where it isn't.

⚡ Cut dead weight relentlessly. Founders often ask: "shouldn't we show everything, just in case?" No. Not in paid acquisition. Every low-performing SKU in the feed:

• Burns crawl budget

• Dilutes machine learning signals

• Wastes CPCs on zero-margin products

• Slows refreshes + kills data hygiene

The best feed isn't the biggest. It's the tightest: filtered down to high-signal, high-leverage products only.

🔥 TL;DR for Founders

You don't need a better pipe. You need to control what flows through it.

The right lever was not "cleaner feed tech." It was ruthless SKU prioritization, margin filtering, and signal-driven campaign architecture.

Feed tech was a means.
The real job was choosing what deserves to be scaled.

This newsletter is for you. What marketing challenges are you facing in your startup journey? Reply directly to this email with your questions or topics you'd like to see covered in future issues.

Until next week,

Adam

P.S. Found this helpful? Forward it to another founder who might benefit. We're all in this together.

P.P.S. Don't forget to download the Growth Marketing OS by clicking the button below.