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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ThoughtSpot hauls in $248M Series D on $1.95B valuation – gpgmail


ThoughtSpot was started by a bunch of ed-Googlers looking to bring the power of search to data. Seven years later the company is growing fast, sporting a fat valuation of almost $2 billion and looking ahead to a possible IPO. Today it announced a hefty $248 million Series D round as it continues on its journey.

Investors include Silver Lake Waterman, Silver Lake’s late-stage growth capital fund along with existing investors Lightspeed Venture Partners, Sapphire Ventures and Geodesic Capital. Today’s funding brings the total raised to $554 million, according to the company.

The company wants to help customers bring speed to data analysis by answering natural language questions about the data without having to understand how to formulate a SQL query. As a person enters questions, ThoughSpot translates that question into SQL, then displays a chart with data related to the question, all almost instantly (at least in the demo).

It doesn’t stop there though. It also uses artificial intelligence to understand intent to help come up the exact correct answer. ThoughtSpot CEO Sudheesh Nair says that this artificial intelligence underpinning is key to the product. As he explained, if you are looking for the answer to a specific question like ‘What is the profit margin of red shoes in Portland?” there won’t be multiple answers. There is only one answer, and that’s where artificial intelligence really comes into play.

“The bar on delivering that kind of answer is very high and because of that, understanding intent is critical. We use AI for that. You could ask, ‘How did we do with red shoes in Portland?’ I could ask, ‘What is the profit margin of red shoes in Portland?’ The system needs to know that we both are asking the same question. So there’s a lot of AI that goes behind it to understand the intent,” Nair explained.

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Image: ThoughtSpot

ThoughtSpot gets answers to queries by connecting to a variety of internal systems like HR, CRM and ERP and uses all of this data to answer the question as best it can. So far, it appears to be working. The company has almost 250 large company customers, and is on a run rate of close to $100 million.

Nair said that the company didn’t necessarily need the money with $100 million still in the bank, but he saw an opportunity, and he seized it. He says the money gives him a great deal of flexibility moving forward including the possibility of acquiring companies to fill in missing pieces or to expand the platform’s capabilities. It also will allow him to accelerate growth. Plus, he sees the capital markets possibly tightening next year and he wanted to strike while the opportunity was in front of him.

Nair definitely sees the company going public at some point. “With these kind of resources behind us, it actually opens up an opportunity for us to do any sort of IPO that we want. I do think that a company like this will benefit from going public because Global 2000 kind of customers, where we have our most of our business, appreciate the transparency and the stability represented by public companies,” he said.

He added, “And with $350 million in the bank, it’s totally [possible to] IPO, which means that a year and a half from now if we are ready to take the company public, we can actually have all options open including a direct listing, potentially. I’m not saying we will do that, but I’m saying that this kind of funding behind us, we have all those options open.”


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