My goal is to generate enough signal with MAKE to attract creators to list their products on the platform and/or use MAKE's white-label printing services. In order to do that, I need to launch internal products that win.

Every product I launch costs time and dollars. I need to ideate the product, iterate a design for aesthetics and function, tune for printability, create marketing copy and images, list on the site, and run ads. Each concept I launch is a discrete bet. Get it wrong and those hours and dollars are down the drain. Get it right and MAKE scales.

I needed a way to make informed bets about what products to launch before spending any time or dollars.

Approach

3D model platforms are abundant, with each boasting millions of model downloads. Across them, there's a wealth of engagement metrics: downloads, prints, remixes, comments from hobbyists that own 3D printers and create. That's demand signal at scale. Millions of people voting with their time on what's worth printing.

But in raw form, it's useless. What engagement metrics matter? How do I spot the difference between a viral fluke and a category pattern? How do I evaluate the data while keeping in mind the bias differences between a consumer and a hobbyist printer?

I built tools to extract patterns from the noise.

Scraping

I chose four platforms and scraped data from their most downloaded models. Across them, I captured 880 models representing around 20 million total downloads.

Classification

I built a 13-facet classification system designed specifically for commercialization decisions.

Product facets capture what the thing actually is: functional purpose across 27 categories from tech accessory to kitchen dining, aesthetic style across 28 options from minimalist to fantasy medieval, mechanism type, placement, and size class.

Consumer psychology facets capture who buys it and why: age group, emotional trigger (practical need, novelty delight, collecting, fandom, gift giving), expected price tier, and seasonality.

IP and metadata determine commercial viability: whether it's original or fan art, franchise associations, and license type.

Manually categorizing 880 models across 13 dimensions would take weeks and produce inconsistent results. So I ran every model through Claude's vision API. The model receives the title, description, license info, and thumbnail image, then returns structured classifications across all facets. Multi-modal classification let me process at scale and spot patterns I'd miss through fatigue.

With the data structured, I could finally ask useful questions.

What the Data Said

With data classified across four platforms, a few clear patterns emerged:

Articulated Toys and Impossible Geometry Drive Novelty

The collecting trigger (32,817 avg downloads) and novelty_delight trigger (25,847 avg downloads) were the two highest-performing emotional facets. Within these, 43% of "collecting" models and 39% of "novelty_delight" models feature articulated joints: the Articulated Crystal Dragon (170K downloads), flexi turtle (108K downloads), Fidget Cube (105K downloads), and Cute Mini Octopus (97K downloads).

The value proposition here is novelty through impossible geometry: toys that move, flex, and fidget in ways that are difficult or impossible to manufacture through injection molding or traditional assembly. They're buying a dragon whose joints ripple, a turtle that curls, an octopus with tentacles that wiggle.

This is where 3D printing's commercial advantage is clearest: creating objects that couldn't otherwise exist at accessible price points. The top performers skew toward organic, animal forms, creatures whose flexible movement feels almost alive. Mechanical articulation (robots, vehicles) underperforms by comparison.

Fidget and Sensory is a Distinct Category

The fidget cube, flexi turtle, and similar tactile toys are currently bucketed under "novelty_delight" and "collecting," but they represent a distinct buyer psychology: sensory satisfaction and stimming. This audience (stress relief seekers, ADHD, desk toy buyers) has different motivations than collectors and may respond to different marketing angles.

Many of the top-performing articulated toys function as fidget objects rather than traditional collectibles. Separating this segment could sharpen both product development and positioning.

Functional Goods Dominate, But With Caveats

Categories like tech_accessory (39,104 avg downloads), container_box (33,339 avg downloads), tool_utility (28,504 avg downloads), and mount_bracket (23,309 avg downloads) show strong download performance. Top examples include the Crocodile Bag Clip (51K downloads), Loud Whistle (66K downloads), and Fast Funnel (37K downloads).

However, these numbers may require careful interpretation. Hobbyist printers gravitate toward functional prints to justify their hardware investment, a dynamic that may not transfer to consumers who can buy a chip clip for cents. The commercial opportunity in functional goods likely exists only where 3D printing offers genuine advantage: customization, niche sizing, integrated mechanisms, or improved designs unavailable at retail.

Modular Systems Show the Power of Ecosystems

Modular ecosystems command significant aggregate attention:

The appeal is clear: these systems let users create custom configurations (drawer organizers, tool holders, storage solutions) tailored to exact needs. This is personalization at no marginal cost, impossible with mass-produced alternatives. Parametric designs like the customizable name plate (80K downloads) reinforce this trend.

For commercial application, the question is whether consumers will buy into a modular system without owning a printer, or whether this pattern is intrinsically tied to the hobbyist's ability to print additional pieces on demand.

Home Goods Show Broad, Consistent Demand

Home goods perform well across multiple subcategories. Lighting leads with 21,200 average downloads across 9 models. Planters have carved out a reliable niche: the Self-Watering Planter (36K downloads), Japandi Vases (35K downloads), and the modular Plantygon stacking planter (22K downloads) all show steady interest. Decorative items like the Bouquet of Roses (35K downloads) and seasonal pieces like Christmas decorations (46K downloads) indicate gifting and holiday appeal.

The throughline is that consumers want home items that look intentional, not printed. The best-performing aesthetics (minimalist, geometric, Scandinavian) succeed because they read as design objects rather than hobbyist projects. This is a key filter for commercial viability: home goods must clear a higher aesthetic bar than toys or tools because they live in visible spaces and compete with mass-market décor. The opportunity exists, but execution quality matters more here than in other categories.

My Plan: Seven Products in Two Weeks

I'm launching a product every other day for the next two weeks. Seven products, each targeting a different high-potential combination from the data.

The strategy splits across paid and organic.

Paid is for fast feedback. Five hundred dollars across seven products, roughly seventy each. At realistic CPCs for a new brand ($2-2.50), that's 200-250 total visitors. Not enough to draw conclusions from conversions alone. But enough to see which products earn clicks, which creatives stop the scroll, and where intent shows up. Early reads on what's resonating.

Organic is for actual traction. For each product, I'm using AI pipelines to generate content at a pace I couldn't sustain manually: product videos, lifestyle images, short-form clips. The goal is to seed every relevant channel and let compounding do the work. SEO builds slowly, but it builds free. Social proof accumulates. A product that gets one organic sale this month might get ten next month without another dollar spent.

The two work together. Paid tells me where to aim. Organic builds the foundation that makes the flywheel possible.

And the flywheel is the point. Every product that wins becomes a permanent traffic source. As the catalog grows, organic traffic grows with it. Each new product doesn't just generate its own sales. It brings visitors who discover other products.

When creators join the platform, they bring their own audiences. A creator with 50,000 Instagram followers listing on MAKE doesn't need me to pay for their traffic. They are the traffic. And their visitors discover products from other creators. Network effects kick in. Customer acquisition cost drops toward zero.

At scale, the math inverts. Instead of spending to acquire customers, the platform acquires customers for free and takes a margin on transactions. That's when MAKE becomes a real business.

These first two weeks are about proving the demand patterns hold. If they do, I'll have validated the product selection process and earned the right to build the flywheel.

I'll follow up with results.