How Do You Test New DTC Products Cheaply Before Scaling?

You test new DTC products cheaply by running micro-tests on shared infrastructure, reading the signal within 30--60 days, and killing losers before they burn cash. The goal is not to find a winner on the first try -- it is to build a system where the cost of each test approaches zero so you can test many.

The Test-and-Learn Model

The standard startup approach is: pick one product, build everything around it, and hope it works. The Stealth Labz portfolio tells a different story. Over 28 months (October 2023 -- January 2026), Michael George Keating launched 38 distinct products through Konnektive CRM, generating $1,075,946 in gross revenue. Six products reached meaningful scale (over $25K net). Nine showed moderate traction ($1K--$25K). Twenty-three were micro-tests that generated under $1,000 each and were killed fast.

That 6-out-of-38 hit rate is not a failure -- it is the discipline working exactly as designed. According to CB Insights, 35% of startups fail because there is no market need. The operators who test broadly before committing deeply avoid that trap systematically.

Infrastructure Leverage Is the Key

All 38 products ran through the same infrastructure: same Konnektive CRM, same payment processing pipeline, same affiliate tracking, same fulfillment systems, same reporting and attribution. The marginal cost of testing a new product was near zero. That is what enables a 38-product test cycle -- when infrastructure is shared, the only cost of a failed test is the time to set it up.

The R&D process was systematized: source product via Alibaba, validate with Oberlo, run a Shopify dropship test, calculate CPA, and make a viability decision before committing full investment. This sequence meant the operator knew unit economics before any significant spend occurred.

Kill Speed Matters More Than Hit Rate

The micro-tests averaged $56 in total revenue each. That means the signal -- "this is not working" -- was read quickly and resources were redirected to winners. There was no $10K--$15K product that limped along for months consuming attention and budget. Products either showed traction within 30--60 days or were cut.

The pattern across launch waves: deploy 2--4 products in a window, measure results within 30--60 days, scale the ones that show traction, kill the rest, then repeat. Wave 1 (October 2023) proved the infrastructure with PRD-01 and PRD-02. Wave 2 (February 2024) ran multiple products simultaneously on the same infrastructure during peak revenue. Wave 6 (May--September 2025) produced PRD-03, which broke out to $101K net in five months -- a product that would not have been discovered without the testing discipline that preceded it.

The Power Law at Work

Six products generated 94.5% of all revenue. Nine products generated 5.0%. Twenty-three generated 0.5%. This is the power law operating in a DTC portfolio: a small number of winners produce almost all the return. The operator who accepts this distribution and builds a testing system around it -- rather than betting everything on predicting the winner in advance -- has a fundamentally different risk profile.

The 23 micro-tests are not failures. They are the cost of finding the 6 winners. And because every test ran on shared infrastructure, that cost was $4,320 total across 23 products. That is what cheap testing looks like at scale.

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Related: What Does a Full Product Lifecycle Look Like in DTC From Launch to Wind-Down?

References

1. CB Insights (2024). "Top Reasons Startups Fail." Analysis of startup failure modes including market need.
2. Keating, M.G. (2026). "Case Study: The Product Launch Engine." Stealth Labz. Read case study
3. Keating, M.G. (2026). "The Compounding Execution Method: Complete Technical Documentation." Stealth Labz. Browse papers