Most SaaS analytics tools are expensive and focus only on past data. ScaleSense is a real-time profitability simulator built for early-stage founders. It instantly calculates LTV, CAC, and recovery months without needing payment API access. The standout feature is the "What-If" slider—visually projecting how a 1% churn reduction boosts long-term revenue. It also includes an actionable Strategy Engine that advises exactly when to pause ads or scale growth based on your unit economics.
Hi Product Hunt! 👋
I built ScaleSense because I kept seeing early-stage SaaS founders, indie hackers, and subscription business owners struggle to find the right balance between their Customer Acquisition Cost (CAC) and Lifetime Value (LTV).
While tools like Baremetrics are great, they are often too expensive for micro-SaaS creators and act as historical dashboards rather than predictive tools. Founders needed a simple way to model their metrics and visually understand how a mere 1% drop in churn could completely change their business trajectory.
I wanted to create a lightweight "revenue command center." Instead of building a complex backend, I engineered ScaleSense to run entirely in your browser. You don't need to connect payment gateways. Just plug in your ARPU, Churn, CAC, and Margins. The "What-If" slider dynamically projects your revenue, and the built-in Strategy Engine gives you hardcoded advice on whether to pause paid ads or hit the gas on scaling.
"I kept the tech stack incredibly lean—it's built purely with Vanilla JS and hosted entirely for free on Google Sites. Proof that you don't need a massive backend to solve real revenue problems!"
I'd love to hear your thoughts on the scenario slider and the strategy recommendations! Let me know if you have any questions.
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LTV to CAC calculation assumes the tomorrow customer is the same as yesterday. You hand these "precise" unit economics to an early team. Does it help them fix the churn. It gives mathematical permision to stop talking to the users who left)
LTV to CAC calculation assumes the tomorrow customer is the same as yesterday. You hand these "precise" unit economics to an early team. Does it help them fix the churn. It gives mathematical permision to stop talking to the users who left)