The Metrics That Matter Before You Have Customers
The Metrics That Matter Before You Have Customers
Most advice about metrics assumes you have customers. Churn rate, LTV, NPS: all of it requires someone on the other end who's paying you money and using your product. Before that stage exists, the standard framework doesn't apply.
This creates a real problem. You're building something, shipping things, putting content out. You have to be measuring something. The question is whether you're measuring the things that will actually tell you whether you're moving in the right direction.
The answer, most of the time, is no. Pre-revenue founders tend to measure the proxies that feel most like revenue metrics while being the least connected to them.
The Vanity Metric Trap, Pre-Revenue Edition
Traffic is the most seductive early metric. It moves. It's visible. It has a number that can go up. And it's completely disconnected from whether your product will find a market.
You can have 5,000 monthly visitors and no one who would pay $1 for what you're building. You can have 80 monthly visitors and three inbound leads from people who need exactly what you've built. Traffic tells you whether your SEO is working. It says almost nothing about whether your product has traction with people who would actually buy it.
The pre-revenue metrics trap is that everything technically countable is countable, but very few things that are countable are informative. Email list size, social followers, LinkedIn impressions. These are all numbers that can be reported, but they're one step removed from the signal you actually need.
What Pre-Customer Traction Actually Looks Like
Before you have paying customers, the metric that matters most is: are the right people arriving, and when they arrive, are they doing the thing that would eventually become a purchase?
That's a behavioral signal, not a volume signal. One person who reads your landing page, reads your blog post about the specific problem you solve, navigates to your pricing page, and then leaves without converting is worth more as a data point than a hundred people who bounce from the homepage. The first person represents a real potential buyer who wasn't sold. The hundred people represent a traffic source that isn't attracting buyers at all.
What converts this into a useful metric: instrument the path. Know what the buyer behavior looks like, and count how many people exhibit it, not just how many people show up.
For a content product, buyer behavior might look like: - Reads the product page - Reads at least one post about the core problem (not a tangential topic) - Spends more than 3 minutes on the pricing page or documentation - Returns at least once
That's not a conversion. But it's a buyer-shaped behavior pattern. If you have 10 people a month doing that, you have 10 potential customers. If you have 1,000 people a month visiting and zero people doing that, you have a traffic problem, not a traction problem, and the distinction matters for what you do next.
The Signal You Can Actually Use
Here's the pre-revenue metric that consistently predicts whether a B2B product will find customers: the quality of the objection.
When you share your product with someone relevant and they push back, what are they pushing back on? A generic objection ("seems interesting but not sure if I need it") is a mismatch signal. They haven't recognized the problem you're solving as their problem. A specific objection ("I'd use this but my team uses X already and switching costs are high") is a traction signal. They recognized the problem, recognized your solution, and have a concrete reason they're not buying yet.
Specific objections are worth cataloging and tracking. As objections get more specific, you're getting closer to PMF. When they shift from "not sure I need this" to "I need this but can't do it right now" to "I'm going to use this as soon as [condition]," you're watching the market recognize itself in your product.
This is harder to instrument than a website metric. It requires conversations. But it's the only pre-revenue signal that tells you whether you're building something people want or just something they don't object to.
The Pre-Revenue Content Trap
One specific version of this problem affects content-led products: measuring content performance on content metrics.
If you're building a product that's meant to attract buyers through content, it's tempting to evaluate the content on whether it gets traffic, shares, and comments. Those are content success signals. But the question for a content-led go-to-market is whether the content is attracting people who are in the market for what you're selling.
A post that gets 300 shares from curious readers who would never buy your product is a worse piece of content, for your purposes, than a post that gets 30 shares from people who are actively evaluating tools in your space. The second post is doing the job. The first is filling a different bucket.
This is why qualifying the audience matters as much as growing it. Before revenue, the question isn't "how many people are reading this." It's "are the right people reading this, and when they do, are they arriving at the product?"
What to Actually Track
Three pre-revenue metrics worth tracking:
Qualified arrival rate. Of the people who find your product, how many match the ICP you're building for? You'll need to define ICP behaviorally (what they do on the site, what content they read) since you can't ask everyone for their job title.
Buyer-shaped behavior count. How many people per month exhibit the behavior pattern that would precede a purchase? Even if zero of them convert, the trend line tells you something.
Objection quality score. When you get feedback or pushback on the product (in conversations, in replies, in comments), is it generic or specific? Tracking the ratio of specific to generic objections tells you whether you're getting closer to the people who need it.
None of these are as satisfying as a revenue number. But they're the things that will tell you whether you're building toward one.
KGBLOCK0
David Pedersen builds COS at SEMalytics. This post is part of an ongoing series on content measurement and what it takes to build a product people actually need.