How early-stage startups can use data effectively – TechCrunch

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Headings…
How early-stage startups can use data effectively
“If we have data, let’s look at data. If all we have are opinions, let’s go
Table of Contents
Common pitfalls
Don’t measure too much
A/B tests are anti-startup
Understand your calculations

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How early-stage startups can use data effectively “If we have data, let’s look at data.
It is a commonly held belief that startups can measure their way to success.
And while there are always exceptions, early-stage companies often can’t leverage data easily, at least not in the way that later stage companies can.
It’s imperative that startups recognize this early on — it makes all the difference.
In this piece, I draw on my experiences using data to take Framer from seed round to Series B.
There are good and bad ways for startups to use data.
In my opinion, the bad way unfortunately is often preached on saas blogs, a/b test tool marketing pages, and especially growth hacker conferences: that by simply measuring and looking at data you’ll find simple things to do that will drive explosive growth.
Below the surface of your day to day results, your startup can be described by a set of numbers.
But most importantly, using data the right way will help answer the single most important – but complex – question at any moment for a startup: how are we really doing?
Let’s start with looking at what not to do as a startup.
Don’t measure too much
Technically, it’s easy to measure everything, so most startups start out that way.
But when you measure everything, you learn nothing.
Just the sheer noise makes it hard to discover anything useful and it can be demotivating to look at piles of numbers in general.
You should only expand your set of measurements once you’ve made the most important ones actionable.
Later in this article, I provide a clear set of ways to plan what you measure.
A/B tests are anti-startup
To make decisions based on data you need volume.
That sample size is generally too high for most early-stage startups and forces your product development into long cycles.
While on the subject of shipping fast and iterating later, let’s talk about A/B testing.
To get reliable measurements, you should only be changing one variable at a time

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