I’ve always been fascinated by the parallels between running a clinic and investing in startups. Both require a sharp eye for detail, a willingness to make calculated risks, and the ability to navigate the unpredictable currents of human behavior. Recently, my thoughts have been centered on a particular aspect of startup economics that’s often overlooked: testing economics on a small scale.
In the world of startups, there’s a comforting myth that a product or service will naturally scale up once it works on a small level. It’s an appealing thought — who wouldn’t want to believe that a few successful trials are a surefire sign of future triumphs? But in reality, economics isn’t quite so straightforward.
Take, for instance, the case of Asha, a founder I recently mentored. She had a brilliant idea for a subscription service and initially priced it at Rs 2,000 per month. Her cost assumptions included Rs 150 for tools and messaging, Rs 600 for delivery labor, Rs 250 for support, and Rs 100 for expected rework and other variable delivery costs. This left her with Rs 900 per customer per month before considering acquisition costs and fixed overheads.
Now, Asha’s initial numbers seemed promising, but they were just that — initial. The truth is, these calculations are inherently fluid. They are based on assumptions, and assumptions can often lead us astray. For instance, Asha’s Rs 600 labor allowance included her own work at a replacement cost. If she didn’t account for this correctly, she might have been misled into thinking her product was more profitable than it actually was.
It’s vital to distinguish between temporary research effort and ongoing delivery. Watching a first-time user might be a one-time learning expenditure, but correcting their list every morning is operational work. This differentiation is crucial for understanding where your resources are truly going.
Let’s not forget the cost of acquiring a customer. For Asha, it was Rs 1,800. A simple contribution-based payback estimate would suggest it takes two months to break even, assuming the Rs 900 contribution per customer continues, the customer remains, and the revenue is collected. But this isn’t a guarantee. It excludes any additional setup costs unless they’re included in acquisition spending.
One glaring mistake many make is manufacturing a lifetime value by assuming customers will stay for years when they’ve only observed them for weeks. Start with what you’ve actually seen. If your customer engagement is measured in weeks, resist the temptation to project years. Instead, show longer-term scenarios separately as assumptions, including what happens if renewal is weaker or support costs rise.
Running a sensitivity check is not just a good idea; it’s essential. What happens if delivery takes twice as long and labor costs double? Suddenly, the contribution drops from Rs 900 to Rs 300, and the payback stretches to six months. This changes everything: how much growth is feasible, which process improvements are urgent, and whether the current pricing model is sustainable.
In the startup world, cash flow timing is as crucial as the numbers themselves. A customer may be profitable on paper but pay after workers and suppliers are due. The delay can be crippling. It’s critical to track invoices, receipts, refunds, and commitments separately. A small business can quickly find itself in trouble while waiting for money it has technically earned.
These calculations are not just academic exercises; they are operational learning. They should be refined with appropriate accounting help as the business develops. But more fundamentally, the founder’s responsibility is to notice whether the model has a credible path to supporting its work and to investigate uncomfortable gaps before scaling makes those gaps more expensive.
In my experience, both in the clinic and in investing, it’s the small, often overlooked details that can make or break an endeavor. The overlooked joy in small wins is something I’ve written about before, and it applies here too. Recognizing these small milestones can provide valuable insights and guide the larger strategy.
As an IVF specialist, I’ve learned to pay attention to the minutiae — the subtle changes in a patient’s response, the delicate balance of hormones, the precise timing of treatments. These details often mean the difference between a successful cycle and a failed one. Similarly, in startups, it’s the refined understanding of one’s economic model, the nuanced adjustments in strategy, and the careful tracking of cash flow that can determine success.
For founders, the takeaway is clear: Test your economics on a small scale, and be honest with yourself about the results. Don’t let the allure of potential scale blind you to the realities of your current model. Just as my IVF patients’ bravery taught me about startup failures, understanding and accepting the limitations of your current model can pave the way for more sustainable growth.
In the end, the art of scaling a startup is akin to the art of medicine. It requires patience, precision, and a willingness to learn from every misstep. By focusing on the small scale, you build a foundation capable of supporting the weight of future success. And that, I’ve found, is where true growth begins.
Recognize the limitations of what you can observe, and don’t be afraid to re-evaluate your assumptions regularly. In entrepreneurship, as in life, it’s those who are willing to adapt and learn who ultimately succeed.
Part of the Building Frugal Startups: Lessons from the Trenches series — read the full guide.