Startup Metrics 101 for Engineers

Hey there,

Welcome to where I write about my journey from a stable Big Tech Software Engineering job to the wild and volatile world of Venture Capital.

As a former engineer who traded code sprints for cap tables, I learned the hard way that shipping elegant code isn’t enough to build a successful startup. In my Big Tech days, success was measured in pull requests merged and 99.99% uptime. But once I jumped into the venture capital and startup world, I had to recalibrate my dashboard. It turns out that 100 loyal daily users can trump 1,000 signups with no retention. In other words, there are key numbers - growth, retention, runway, unit economics, etc. - that matter more to a startup’s fate than any single ingenious code commit.

This post is an engineer’s guide (in plain English and a bit of math) to the startup metrics that VCs and founders obsess over. We’ll cover the essentials: how fast you’re growing, whether users stick around, how long your cash will last, and if your business model makes any money. By the end, you’ll see why 100 delighted daily users beat 1000 drive-by signups, and why these metrics have become my new “performance specs” in evaluating startups. Let’s dive in.

In startup land, “growth” is king. It refers to how quickly your user base or revenue is expanding. Coming from engineering, you might be used to gradual, version-by-version improvements. Startups, however, live and die by the hockey stick graph - rapidly accelerating user or revenue curves. Investors often look for evidence that your startup can scale like crazy. In fact, VCs typically hope to see early-stage startups growing around 15-25% month-over-month . That kind of consistent double-digit MoM growth signals you might be on track to become the next big rocket ship.

Why is growth such a big deal? Rapid growth suggests strong product-market fit - that you’ve built something people want, badly. It’s like seeing your user count curve bend upward; that curve is more persuasive to a VC than the most polished demo. Conversely, flat or single-digit growth can be a red flag: it might mean the product isn’t resonating or that the market is limited. As an engineer, I used to celebrate a 2x speedup in an algorithm; now I get more excited by a 2x increase in monthly active users. High growth covers a multitude of sins - it can help you attract talent, raise funding, and gain press. Just remember, growth isn’t just about raw signup numbers (we’ll get to why quality matters in a moment). It’s about compounding. Even seemingly modest growth rates compound dramatically over time, as the chart above shows. The difference between 5% and 20% growth each month is the difference between stagnation and the stratosphere.

What to track for Growth: For early-stage startups, the key growth metrics might be user-oriented or revenue-oriented depending on your model. Common ones include: Monthly Active Users (MAU) growth, Monthly Recurring Revenue (MRR) growth if you’re a SaaS, or Gross Merchandise Value (GMV) growth for marketplaces. The exact metric can vary, but the theme is the same - up and to the right. Ensure you measure growth rate (percentage increase over time), not just absolute numbers. A startup that grows users 25% every month consistently will impress more than one that went from 1000 to 2000 users once and then flatlined. If you find yourself focusing only on code optimizations while your user count is flat, it’s time to shift priorities and figure out how to drive adoption.

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So you’ve got users signing up - congrats! But here’s the rub: if they don’t stick around, those signup numbers are meaningless. This is the difference between vanity metrics (numbers that look good on paper) and value metrics (numbers that actually indicate a healthy business) . Retention is all about how well you keep users coming back. A product with 100 daily active, loyal users can be far more valuable than one that had 1,000 people sign up and vanish after one try . In VC terms, strong retention is often seen as a proxy for product-market fit - it shows users are getting enough value to make your product a habit, not a one-time fling .

When I first saw founders boasting about “10,000 signups in our first week!”, the engineer in me was impressed. But as a VC, my next question is: How many of those 10,000 are still using it a month later? If the answer is only a few hundred, that big top-of-funnel number isn’t so impressive after all. Users abandoning the product after one or two touches is a major red flag - no matter how many new signups you can drive . High churn (low retention) means you’re essentially pouring water into a bucket with holes in it. You’re spending time or money acquiring users who slip away. On the other hand, if you see cohorts of users sticking around and even increasing their usage, you’ve got something truly sticky. Strong retention means your product delivers real value that keeps people coming back (and eventually, willing to pay or evangelize for you).

Let’s clarify a few retention-related metrics in plain terms:

Churn Rate: This is the percentage of users (or customers) who leave or stop using the product in a given period. It’s the evil twin of retention. For example, if 7 out of 100 users quit in a month, that’s 7% monthly churn. Lower is better - churn under 5% per month is typically considered healthy for early SaaS startups . High churn means you’re losing users almost as fast as you gain them, a sign something’s broken or the fit isn’t there.

Retention Rate (Cohort Retention): The flip side of churn - what percentage of users remain active over time. You might measure, say, 30-day retention: of all users who signed up in January, what % are still active in February? Tracking retention by cohort (group of users who joined around the same time) is super insightful: if newer cohorts retain better than older ones, your product or onboarding is improving. If it’s worse, sound the alarm - you may have shipped changes that hurt user experience.

DAU/MAU (Daily Active Users / Monthly Active Users ratio): This mouthful of a metric basically measures how “sticky” your product is - how often users engage. A higher ratio means people are using your product frequently (daily vs monthly). As a rule of thumb, a DAU/MAU above ~20% is solid for many consumer apps, while 50%+ indicates your product is part of users’ daily routine . For example, if you have 50,000 monthly active users and 10,000 of them use the product every day, that’s a 20% DAU/MAU. It suggests they’re coming back regularly, not just signing up and disappearing.

The key lesson on retention: Don’t chase raw signup numbers without ensuring users love the product enough to stay. It’s better to have a smaller, active user base that’s growing organically through word-of-mouth than a huge number of downloads that never convert to active use. One of my investor friends puts it bluntly: a spike in app installs may look great on a pitch deck, but if 80% of those users churn in a week, you’ve got a leaky bucket, not a business . Plug the leaks (improve your product, onboarding, engagement loops) before you pour in more users at the top.

“Runway” is startup-speak for “How long can you keep the lights on before you run out of cash?” It’s measured in time - usually months. Even the most brilliant product idea won’t matter if your startup runs out of money and shuts down. As an engineer, I rarely thought about my employer’s bank balance. But as a founder or investor, I’m practically looking at a countdown clock in the corner of every board meeting. Runway focuses the mind: it tells you how much time you have to reach the next milestone or funding round before the money dries up .

Two concepts go hand-in-hand here: burn rate and runway. Your burn rate is basically how much cash you’re net spending per month. For example, if your startup spends $50k a month on salaries, servers, etc., and earns negligible revenue initially, your net burn is ~$50k/month. Now, if you have $600k in the bank, a quick division ($600k / $50k) gives ~12 months of runway. VCs watch this closely. Burn rate tells us how efficiently you’re using capital, while runway shows how much time you have to achieve key milestones with that money . It’s like fuel in a jet: how fast are you burning it and how far will it get you?

Why does runway matter so much? Because time is life in startups. The longer your runway, the more iterations and pivots you can attempt to find product-market fit or scale up. A short runway (say <6 months) is extremely risky - it means you’re either actively raising money right now or you’ll have to drastically cut costs to avoid crashing. Most founders and investors like to see 12-18 months of runway after a fundraise . This typically gives ~1 year to execute and 6 months to fundraise before money runs out. In a booming market, 18 months might be fine; in a downturn, startups try to push for 24+ months to be safe .

As a VC now, I love to see founders who are shrewd about managing their burn. It shows they can do more with less. In 2023, when venture funding got tighter, many startups cut burn rate by 50%+ to extend runway and survive . That discipline can be the difference between survival and shutdown. Remember, runway = survival in the startup game . Keep an eye on your cash like your life depends on it - because it does. If you’re an engineer founder, this might mean tempering your urge to over-engineer or scale prematurely. You may want to build a fancy microservices architecture, but if it requires hiring five more engineers you can’t afford, you might opt for a scrappier approach to conserve cash.

A quick VC math tip: I often ask founders about “gross burn” vs “net burn.” Gross burn is total cash outflow (expenses), net burn is outflow minus any cash coming in (revenue). In early stages, you might have near-zero revenue, so gross and net burn are the same. But as you start earning, net burn is what really counts for runway. Runway = Cash in Bank / Net Burn per Month . Improve that either by burning less or earning more (or ideally both). There are only so many levers: cut costs, increase revenue, or raise new funds to refill the tank .

Bottom line: make sure you always know how many months of runway you have left. It’s the one metric that can keep you up at night even more than a Sev-1 bug in production. You can debug a prod issue overnight, but you can’t instantly debug a cash crunch - prevention (via smart cash management) is the only cure.

Let’s talk money - not how much you have in the bank (runway), but whether your business model makes sense per customer. Unit economics is all about the profit (or loss) you make per user or unit of your service. As an engineer, I used to focus on system efficiency; unit economics is about business efficiency. You might have a million users, but if you lose money on each one, that’s a problem (remember the joke: “we lose money on every customer, but we make it up in volume!” - yeah, don’t be that startup). Here are the key concepts translated for our kind:

CAC (Customer Acquisition Cost): How much do you spend to acquire a customer or user? This includes marketing, sales, promotions - any resources spent to sign up one user. If you’re an early-stage B2C startup, CAC might come from ad spend divided by new signups. For a B2B SaaS, it might include sales team salaries proportioned per deal. Lower CAC is generally better, but it can be high if the lifetime value justifies it.

LTV (Lifetime Value): How much revenue (or profit) will a user bring in over their entire time using your product? For a paying product, it’s average revenue per user times estimated customer lifetime (minus costs to serve them). If you have a subscription of $10/month and users stay ~2 years on average, LTV might be ~$240. This metric can be tricky to predict for new startups, but you can use proxies or industry benchmarks.

The LTV/CAC Ratio: This is the magic number every investor wants to know. It’s literally LTV divided by CAC. If it costs you $50 to acquire a customer who, on average, brings $150 of value over time, your LTV/CAC = 3. That’s good! If it costs $50 to get a user who only ever pays you $20, LTV/CAC = 0.4 - you’re in trouble (losing money per user). A general rule of thumb in venture is LTV should be at least 3× CAC for a sustainable model. In fact, many VCs consider a 3:1 LTV/CAC ratio the minimum for a viable startup, with 5:1 or higher indicating a really efficient growth engine . Anything below 1:1 means you’re essentially burning cash to buy users that never pay back - a sure path to the deadpool.

Why do unit economics matter so much? Because they determine if you can make money in the long run. You might not be profitable today (many startups aren’t, as they reinvest for growth), but you need a line of sight that eventually the lifetime profits from customers will exceed the costs to acquire and serve them. Think of unit economics as the fundamental viability of your business model. If each user you add increases your losses, growing faster just digs a deeper hole. Investors will press you on this once you get past the earliest stages: What does it cost to get a customer, and how much do you earn from them over time? If those numbers don’t make sense, either the product or the model needs fixing.

A couple more terms you’ll hear: CAC Payback Period - how long (in months) does it take to recoup the cost of acquiring a customer? If your CAC is $50 and that customer pays $10 per month, the payback period is 5 months (you break even on that user after 5 months, and after that it’s pure upside). Shorter payback is better; a common target is under 12 months payback , meaning within a year the customer has “paid for themselves.” Another one: Gross Margin - essentially the profit margin on your service after direct costs. For software, gross margins are usually high (70-90% is typical for software companies ) because once you’ve built the product, serving one extra user is cheap. If your margins are low (say you’re in e-commerce or hardware), you have thinner room for error on unit economics, because each sale doesn’t generate much profit to cover overhead.

For an engineer, these concepts might feel foreign. I remember my eyes glazing over the first time I saw an LTV/CAC graph. But it’s just like optimizing code, except the “code” is your business. You want to optimize the funnel so that acquiring users is as cost-efficient as possible and that users stick around and spend enough to more than pay back that cost. If your user acquisition is too expensive, you either need to find cheaper channels, improve conversion rates, or charge those users more (or all of the above). If your users aren’t sticking around long enough or spending enough to ever cover what you paid to get them, you’re essentially subsidizing them - and that’s not a sustainable strategy (unless you enjoy literally burning cash).

Unit economics discipline is what separates startups that can blitzscale responsibly from those that growth-hack themselves off a cliff. As a VC, when I see a deck that says “we project 1 million users by Year 3,” my immediate thought is “Ok, but what will it cost to get them and will each user be profitable eventually?” You don’t want to be the startup that “grows” to a large user base and then implodes because it was losing money on each user (we’ve seen this with some ill-fated gig economy and delivery startups). Aim to be the startup where each user acquired is like an investment that returns a profit over its lifetime.

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Shifting from an engineering mindset to a startup business mindset isn’t easy, but it’s incredibly eye-opening. I learned to love metrics that I scarcely thought about before. Now I geek out on graphs of cohort retention almost as much as I used to geek out on elegant code refactoring. The big takeaway for us ex-engineers is this: Great code and great products are essential, but great metrics make the difference in survival and scale.

When you’re building a startup (or evaluating one for investment), keep one foot in the technical world and one in the business world. Sure, ensure your codebase is solid, but also instrument your product to measure these key metrics from day one. Treat your metrics like you treat your system monitoring: if a server goes down at 2 AM, you’d wake up and fix it. Similarly, if your user retention drops or burn rate spikes, that deserves immediate attention.

In summary, focus on growth that is real (not vanity), foster retention through user delight and engagement, keep an eye on the runway clock while calibrating burn, and design a business model where the unit economics eventually make sense. Do this, and you’ll be speaking the language of both engineers and investors - translating technical genius into startup success.

Remember, 100 users who love your product are worth more than 1000 who couldn’t care less . And in the end, the numbers will tell the story of how far your startup can go. Happy building, and don’t forget to keep an eye on those metrics!

Until next time!

Signing off and signing zero checks,

SWEdonym