How early-stage startups can use data effectively – gpgmail


“If we have data, let’s look at data. If all we have are opinions, let’s go with mine.” – Jim Barksdale

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. More concretely, I’ll describe what to (not) focus on, and then, how to get real results.

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. Silver bullets, if you will.

The good way is comparable to first principles thinking. Below the surface of your day to day results, your startup can be described by a set of numbers. It takes some work to discover these numbers, but once you have them you can use them to make predictions and spot underlying trends. If everyone in your company knows these numbers by heart, they will inevitably make better decisions.

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.

Table of Contents


Common pitfalls

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.

My advice is to carefully plan what you want to measure upfront, then implement and conclude. 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. Without volume, the data itself is not statistically significant and is basically just noise. To detect a 3% difference with 95% confidence you would need a sample size of 12,000 visitors, signups, or sales. 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. During the early stages of Framer, we changed our homepage in the middle of a checkout A/B test, which skewed our results. But as a startup, it was the right decision to adjust the way we marketed our product. What you’ll find is that those two factors are often incompatible. In general, constant improvements should trump tests that block quick reactionary changes.

Understand your calculations


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Y Combinator-backed Narrator wants to become the operating system for data science – gpgmail


Cedric Dussud, Michael Nason, Ahmed Elsamadisi and Matthew Star (pictured above, in order) spent the summer sharing a house in San Francisco, cooking meals together and building Narrator, a startup with ambitions of becoming a universal data model fit for any company.

Narrator is one of more than 100 startups graduating next week from Y Combinator, the San Francisco accelerator program. Put simply, the company provides data-science-as-a-service to its customers: fellow startups.

“We provide the equivalent of a data team for the price of an analyst,” explains Narrator co-founder and director of engineering Star. “Within the first month, our clients get an infinitely scalable data system.”

Led by chief executive officer Elsamadisi, a former senior data engineer at WeWork, the Narrator founding team is made up entirely of alums of the co-working giant. The building blocks of Narrator’s subscription-based data modeling tool were developed during Elsamadisi’s WeWork tenure, where he was tasked with making sense of the company’s disorganized trove of data.

As an early addition to WeWork’s data team, Elsamadisi spent two years bringing WeWork’s data to one place, scaling the team to 40 people and ultimately creating a functional data model the soon-to-be-public company could use to streamline operations. Then in 2017, Elsamadisi had an a-ha moment. The system he created at WeWork could be applied to any data stream, he thought.

“All companies are fundamentally the same when it comes to the kinds of data they want to understand about their business,” Narrator’s Dussud tells gpgmail. “Every startup wants to know what’s my monthly recurring revenue, why are my customers churning or whatever the case may be. The only reason they have to go hire a data team and hire a business analyst is because the way that their data is structured is specific to that company.”

All Narrator clients use the same consistent format to absorb and manage their data, saving startups time and heaps of money.

Narrator follows a long line of Y Combinator graduates that built startups catering to other startups, as the accelerator becomes more of a SaaS incubator of sorts. PagerDuty and Docker proved that YC companies could build with a strong focus on other YC companies. Brex, a recent YC grad that issues credit cards to entrepreneurs, has leveraged the same startup-focused model for big-time success.

“Why not build a company to make something that other startups can have?” Asks Dussud. “It’s hugely valuable and only big companies have access to it. Let’s make it available to everybody.”

New York-based Narrator sees a massive opportunity ahead. Every company, after all, wants to increase revenue or decrease costs, a difficult task easier accomplished with a data-driven culture.

“If you start to imagine a world where, under the hood, the structure of the data at all companies is the same, you can now start reusing a lot of the things that in the past would actually be quite complicated,” said Star. “Right now, anytime you want to start from scratch with a new data system, you are literally starting from scratch and unfortunately reinventing the wheel. If you had a standardized system, you know, a standardized model, you could start reusing a lot of really wonderful things.”

Narrator is working with 14 clients today, each using an identical data model. Their goal is for Narrator’s structure to become the standard by which all startups do data science. In other words, Narrator hopes to become the operating system for data science.

“What’s kind of amazing is whether we’re working with a financial app … a clothing rental startup or a healthcare company, they’re all using the same data model,” said Star. “Any one of those teams, if they wanted to get the same level of analysis, they would have to hire a data analyst.”

Narrator raised $1.3 million in seed funding led by Flybridge Capital Partners prior to joining YC. Hot off the heels of the accelerator program, there’s no doubt the startup will close another round of financing soon.


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