 started writing the first few lines of code for what would eventually become 

 joined a few months later to help build out the product. By experimenting with different delivery models and cultivating a small yet mighty fanbase of early customers, they were able to bootstrap until they raised their seed round in the summer of 2012, which was co-led by First Round’s 

. A few key early hires joined in the months that followed, and Looker 

 came on as president and, after going shoulder-to-shoulder with Tabb on the fundraising frontlines in their Series A round, took over as CEO to firm up the sales motion and scale the business.

Fast forward ahead of these early markers to June 2019, 

when Google Cloud announced its intent to acquire the data analytics startup

, inking a $2.6 billion deal that was one for the history books. In a little more than seven years, Looker went from a single customer to more than 1700, from a small, scrappy team in Santa Cruz to a 700-person company spanning eight offices around the globe.

But in between those bookends of starting out and attaining success sit many volumes that go unread. Dust off the covers, and you’ll find countless stories of all the thorny issues the founding team grappled with, the lessons they figured out the hard way and the calls they got right. Some of those tales make their way into startup lore — but most are lost to history.

That’s because it’s hard to tell the story of a startup as it’s being built. In the thick fog of the early days, you can’t tell which bets will pay off and which moves will turn out to be inflection points — and you definitely don’t have time to stop and write it all down.

But when it comes to weaving together this particular startup story, we’ve got more yarn to draw on than usual. Here at First Round, we’ve had front-row seats for Looker’s rocketship ride as their first investors — and fortunately, 

. (The Looker team also carved out some time for reflection right here on the Review, from sharing tactics for 

Over the past month, we sat down with Tabb, Porterfield, Bien and Trenchard to tease out their takeaways and reflect on Looker’s trajectory. 

In this exclusive interview (and epic history)

, this group comes together to fill in the details and mine the treasure trove of insights buried within Looker’s founding story. 

, respectively, they unpack Looker’s recipe for success from every angle, sharing an inside look at why it worked, how the business was built, and what other startups can learn from its early days.

This crew covers all the essential cogs in the startup machine: finding the idea, standing out in the market, experimenting with different models, scaling sales, nailing customer success, and of course, assembling a winning team. From perspectives on curating company culture and intentionally screening early-hires to strategies for messaging a product's value and beating every bookings plan, early-stage teams will find plenty of wisdom here in this detailed accounting of how Looker got built. 

It makes for a long read — but we hope it's a tale that's worth settling in for. Let's begin.

IDEA: START WITH A CRISP THESIS AND DON’T RAISE TOO EARLY

When it comes to extracting the essence of Looker's success, Tabb, Porterfield, Bien and Trenchard were all quick to point to the same takeaway: strong ideas and exceptional execution were key.

According to Tabb, the strength of the initial thesis was a by-product of the fact that he had been simmering the idea for his startup for quite some time. “I waited a really long time to start Looker. Looking back, I think we had a lot of success because I waited until I had a crisp thesis on what the problem was — and then we went directly at it,” says Tabb.

“I see so many founders starting companies saying ‘There’s an opportunity in this space somewhere, I’ll find it.’ Or, I've known kids just out of school who announce that they’re ‘off to do a startup.’ That might work out eventually, but it’s a hard thing to do because there’s no clear path. 

With Looker, I may not have been sure how I was going to achieve it, but I was clear on what the mission was from the jump: to build a product that lets everyone in the organization see everything that’s happening, through data

Future founders, start with a really strong thesis on the problem you’re going to tackle. If you’re missing that first crucial ingredient, it’s best to keep your company building dreams on a low simmer while you work to figure one out.

Draw from a deeply personal problem and assemble the founding players.

That thesis took root in Tabb’s previous experiences. “I’d seen the need for a product like Looker over several years and kept returning to the idea. After playing founding roles at multiple companies, I knew that businesses needed a real-time understanding of their data. I was always building these one-off, custom tools to help others look at very narrow, specific datasets and I realized that there had to be a much better way of doing this,” he says.

As First Round partner Bill Trenchard’s relationship with Tabb dates back to 2004, he’s able to corroborate this story. “I benefited from Lloyd’s approach to data myself back at LiveOps, when I was the CEO and he was the CTO, and again when we teamed up at 

If you’re building the same thing over and over again, that’s a signal that there might be a startup idea in there

,” says Trenchard. “But Lloyd was particularly well-equipped to tackle this idea. Now that I’m an investor, I’ve found that the best founders go deep on a problem, understand all its nuances, and really immerse themselves in the customer’s pain point. That’s Lloyd to a T. Understanding people and their problems really deeply is critically important — and it’s why Lloyd is often fond of saying that ‘

Tabb’s co-founder, Ben Porterfield, had a similar firsthand experience with the early idea for Looker. “I met Lloyd through my friend 

. Lloyd was trying to find folks to help out at ReadyForce and I was in. We spent the next two years driving up together to Menlo Park from Santa Cruz, so I got to know him really well,” says Porterfield. “People from all departments at ReadyForce were using a tool Lloyd had built to help understand the staffing data, so when we both left the company to play around with a few startup concepts, I knew this idea of his had legs. We were certain that we had fit from the start — we knew engineers would find this tool valuable because Lloyd had already proven it.”

After agreeing to team up, this pair bought Llooker.com for $15 — 

’s funny idea for misspelling a domain name, Tabb says — and got to work on creating an MVP. Tabb focused on building the language engine and driving early sales, while Porterfield worked on the platform and the underlying tech stack.

“Ben was a great choice for me as a co-founder,” says Tabb. “He’s a fantastic engineer and he has a great temperament. The best VPs of Eng I’ve met are unflappable and Ben had that trait in spades. He also knew the current tech stack — I was in my forties and he was a 20-something at the time, so he was more familiar with all the modern tools we needed to be using. With that in place, we were able to build a 

 simple version of Looker and start to iterate.”

It was this nascent product that first sold current Looker CEO Frank Bien on the company. “Lloyd and I both lived in Santa Cruz. I remember meeting him in late 2011 at a small coffee shop called 

. It’s a bigger chain now, but that was the first outpost,” says Bien. “I knew he was onto something that could make all of the data we were swimming in 

. I ended up joining another company which was eventually acquired by 

, but I kept in touch and met up with Lloyd every couple of months. I wanted to play a role in what they were building.”

Continuing conversations with players like Bien gave the Looker team crucial sounding boards in the early days. This fledgling team knew their success hinged on their ability to leverage advice in their weak spots. 

 was huge for us in these early days. He joined part-time to take on the more rote business tasks, but he also acted as my consigliere, helping me think through all the thorny issues,” says Tabb. “After a day of sales calls in San Francisco, I'd often call Marc on the way home and review how they went, going over in detail what worked and what didn't in each of the meetings. We had lots of conversations about pricing and go-to-market strategies. I can be really indecisive sometimes and Mark would help me get to a decision.”

For Porterfield, Jim Everingham filled that advisor role. “I’d been an engineering manager before, but this was the first time I was going to lead the org. Lloyd always said that Jim was the best engineering leader he’d ever worked with, so the ability to lean on him was incredible. I’d meet with Jim on a regular basis and 

 on how to think through technical problems and organize the team,” says Porterfield.

With a strong idea in sight, many founders’ first impulse is to start raising funds. But Tabb found it helpful to self-sustain and delay hitting the investor circuit at first.

“My biggest piece of advice to early-stage founders on fundraising is 

,” says Tabb. “I see so many founders out there trying to raise with just an idea on a slide. We waited almost a year to raise money, until we knew it was a venture business — not every startup is, and you don’t want to get locked in. We were cranking along with customers and revenue. And it wasn’t all nailed down, but we had figured out enough of how go-to-market might work to know that it was workable. That made our seed raise in the summer of 2012 so much easier. 

If you build value, it takes the fundraising process from how good you are at pitching yourself to a place where you can simply say ‘Ask the people who are using us for their opinion about it.

If you can’t demonstrate value, you’re asking investors to take it on faith. You’re asking them to take it on who you are, as opposed to the proof points sitting in front of them.

As the investor on the receiving end of that fundraising tactic, Trenchard agrees that it was effective. “When we were deciding whether or not to invest in their seed round, Lloyd sent me a list of 10 customer references — many of them were First Round-backed companies. I talked to each and every one during diligence, and I was blown away. The love they had for the products was off the charts, 

they would have been very disappointed without it

MARKET: STAND OUT IN THE CROWD BY DOUBLING DOWN ON BIG BETS AND DEMOING HOW YOU’RE DIFFERENT

The other key element of Tabb’s early pitch was the single email he sent to Trenchard that outlined the broader industry trends that Looker was hoping to take advantage of. 

“Looking back on this pitch email now, those 216 words encapsulated everything we were trying to do and all the things we got right,” says Tabb.


Here, Tabb and Porterfield team up to dissect a few of the specific bets and trends outlined in the email:

 seemed like the winning horse,” says Tabb. Porterfield grounds this bet in the context of 2011: “We thought that SQL would be the lingua franca for analytics, which wasn't entirely obvious back then. Hadoop was at the peak of its hype, and there was a chance analytics would head in that direction. We could have put a lot of effort into supporting Hadoop to hedge our bet, but that would have been a mistake.”

 “I created LookML to serve as the basis of our platform. It’s an abstraction layer, the sequel to SQL. My thought was that if we could simplify the problems with SQL and evolve the data language, it would be easier to use,” says Tabb. But banking on data analysts learning a new language was anything but a surefire move. “This was a scary one,” says Porterfield. “I remember those early existential questions: ‘Can analysts learn this language? Will they 

 to?’ Looking back now, it seems more obvious that developer-style tooling and workflows would be embraced. These days, there’s a lot of 

 that any time you can provide tools that increase someone’s leverage, they will adopt them. But that wasn’t clear at the time.”

. “We were betting that databases would eventually scale almost infinitely and would go to the cloud. That seems obvious now, but back then, Redshift and BigQuery were just getting started,” says Porterfield. “This shift was huge for Looker, and frankly, it was a bit of a surprise when it first happened — you could suddenly work with lots of data in a fast database in the cloud, and it was relatively cheap.”

 “The difference between reporting and operational analytics is latency, the freshness of data,” says Tabb. “Most data analytic tools are high-latency tools: They extract the data into something else, which means that that movement takes time, so what you're looking at happened yesterday. Say you're looking at a list of items that didn't ship yesterday. Old data doesn't help. You really need to know if they 

 something about it. Latency, in many cases makes data useless. It’s the same with companies. They need to access all the things that are going on right now, research 

 they’re happening and then make decisions.”

This last insight was particularly key for Tabb. “In the online world, you can't see what's going on physically. I've always thought 

building a tech-based startup is a bit like flying

, where the conditions are clear and you can see the runway, and there's 

, where visibility is poor so you have to rely on your instruments,” says Tabb. 

“All tech businesses are IFR. You can't see your customer. But the problem in 2011 was that most companies didn’t have instruments. So they were trying to fly, but they couldn’t see very well. And our bet was that if we could bring them tools to see, their businesses would take off. Several early Looker customers turned into unicorns. I’m obviously a bit biased here, but I think part of that is because they had a really good handle on their data. They could fly.”

When you’re flying a plane in a storm, you have to go by instruments, not your eyes. Same with startups — if you can build a tool that can help companies “see,” you’ll be in business.

Don’t shy away from a crowded market, even if it’s tough to deliver a differentiated message.

While the business intelligence space had several established players, the Looker team found that a 

seemingly crowded market can counterintuitively make a great hunting ground

“The benefit of crowded markets is that you 

 that there’s demand for products and that customers are willing to pay. The downside, of course, is you need a crisp insight to cut through,” says Trenchard. “When I got their pitch email in 2012, the Looker product was still in its very early days, but it was clear that the bet was solid and different enough to go the distance.”

This was exactly the point Tabb was trying to get across. “It was — and still is — 

a crowded market of similar products taking different approaches

. Yes, there are a bunch of products in the market that access data. They all sound the same. But there wasn't anything that did what we were trying to do,” he says.

Porterfield agrees. “We weren’t scared of how crowded the market was because we realized we weren’t actually going up against much. Many of the early stage venture-backed tech companies we were trying to sell to had no existing solution — they relied on developers writing SQL when they needed to ask questions about their data. And that gave us a serious edge,” says Porterfield. “It almost gave us permission to work on what would best enable 

 version of the world — Lloyd used to call it our religion — and that meant we spent more time enabling analysts and focusing on features we wanted to build.”

Still, communicating what made Looker different was particularly tough in the early days. “Part of the problem was that the output at the end seemed similar to other tools on the surface — dashboards and reports — so it was hard to describe, without getting too technical, why it was a better solution. You had to experience it,” says Porterfield.

Tabb concurs. “For probably too many years, we didn't have a 

. I remember after we raised our Series B round, one investor said to me ‘Wow, this is the first company I've seen get this far without one,’” Tabb laughs.

But it wasn’t for a lack of trying. “We knew we were very different architecturally, but you couldn't see that by looking at the website. And while we used terms like ‘business intelligence’ in sales convos because it’s how we got invited to the party, that wasn’t how we’d describe what we were doing,” says Tabb. “It would be like describing the early version of Google to someone as ‘You type words into this box and then a list comes back and you click through it.’ Technically correct, but there’s so much more beneath the surface.”

This messaging challenge led Looker to maintain a rather spartan digital presence. “Up until our Series A, a full year and a half after we had been doing business and generating customers, Looker’s website was just seven lines a text — and it was a joke,” says Tabb. “It was a mock definition of the word ‘looker’ and there was a play button icon that played audio of someone pronouncing it. We did something similar 

 as well. It makes me laugh now thinking about how we were cash flow positive and 

It was an intentional choice, though. “We didn’t think anybody was just going to show up on the web and find us, even though channels did turn out to be really important later. And we thought that all the websites looked the same,” says Tabb. “

So instead of leading with marketing, we tried to build a fanbase — either you’d never heard of Looker or you’d be a huge fan and you’d be in on the joke.

SALES AND GTM: LOCK DOWN A MODEL AND SCALE UP THE MACHINE

Because of the challenges in describing the product’s value, Tabb and Porterfield doubled down on figuring out how to 

“The most stressful part was that it wasn't clear what we were going to 

 with the product. Open source it? Build a services company?” says Porterfield. Tabb agrees. “We didn’t know what the right delivery mechanism was going to be,” he says. “We could have been a customization-heavy service, where it’s almost like consulting and you’re deeply embedded with your customers. Or we could have gone the product route, where we just sell the tool and it’s all in the customer’s hands. 

 was critically important. “As founders, we often feel like we have to have the answer to everything, that the unknowable is unacceptable,” he says. “

When you don’t know something, resist the impulse to plow ahead and wait to see what works.

the decisions we didn’t make were often the ones that totally changed our business

Conventional wisdom says you need to lock in a business model early on, but we found that wasn’t true. Not deciding can in fact turn out to be the best decision you’ll ever make.

With open space ahead of them, the early Looker team tested a different methodology with every new customer. Here are Tabb’s thoughts on how they explored these different models:

 “Liftopia was an early customer that didn't have a data person on their team. We treated this initial engagement as a consulting contract. When they needed something, they called. We were operating as a for-hire-data-team,” says Tabb. “We also did other consulting gigs where we helped customers move their data from a MongoDB database into an analytical one, and while that worked, it wasn't nearly as good.”

 “With another early customer, the buyer was very familiar with the database marketplace and tools, so he was a great person to just hand the software over to and let him try on his own,” says Tabb. “It worked well — they’re still a customer today actually — but we saw that this self-serve approach led him to miss a lot of the value that we'd captured with other customers.”

. “Another one of our early customers was 

. We tried a consulting engagement with them at first because the product was so early that it wasn't baked enough to actually have anyone use it. So I had to go in there and hook it up to their data and teach their engineers how to program in LookML by sitting next to them,” says Tabb. “Eventually they were up and running, and as we added other features, they were able to use the product on their own.”

“No matter the delivery mechanism, my initial sales pitch always came back to this concept: ‘Ask one of my existing customers, and they'll all say it's life changing. And then let me show you something you didn’t know about your own data to demonstrate value,'” says Tabb. 

We knew the only way to be successful was to show the goods. You know the saying, “All hat, no cattle”? We had to have the cattle.

This insight eventually led Looker to land on a slight tweak to a classic SaaS model: 

a free trial, but one that combines heavy pre-sales implementation efforts with the crucial tactic of using the customers’ actual data in the demos.

“We thought we were deciding between product and service, but not deciding unlocked a third way: Selling as a product 

 forward-deploying during the free trial so it felt like a customized service,” says Tabb. “When selling the product, we used the demo as a chance to build a proof of concept, so we didn’t have a dummy sales pitch version — we always asked the prospect for an actual dataset to play with. Then, if we could get our prospects to use the product as much as possible in the free trial, we could comfortably ask for money later.”

This also informed the early product strategy. “We built monitoring tools into the product incredibly early on to make sure that we could tell if they were using it. I’d only try to close the deal once we saw the product starting to catch on around the organization — I’d never go ask for money until there was clear momentum of usage,” says Tabb. “The downside of this approach was that some of the trials ran a 

 long time. If we didn’t see that momentum, I’d go back in and be more hands-on to make sure they understood how we could answer all of their data questions.”

You need to work double time to demonstrate value very quickly pre-sales. If you can prove value early on, then the money will always show up in your bank account later.

Replace yourself to scale faster — even if you’re the CEO.

At this point in Looker’s journey, in between their seed and Series A rounds, Tabb notes he relied heavily on the board. “Our board worked with us hand-in-hand on all of the ‘What’s next?’ thinking we needed to do. A major early focus was on sales,” he says. “We had product/market fit and we had about 20 customers, things were working — but we needed to be thinking about the next phase.”

As a member of Looker’s board from pre-seed all the way through acquisition, Trenchard recalls these conversations vividly. “I remember this one board meeting during their seed stage, where Marc Randolph, 

 and I were talking sales with Lloyd,” says Trenchard. “I drew out the entire sales machine process on a whiteboard, going through building a funnel, qualifying a lead, building out the sales team. Lloyd went quiet for a little bit and then said, ‘

I don’t know how to do any of that and I don’t really want to figure it out. I need to replace myself.

 We need an amazing CEO and I’m going to go find that person.’ And within weeks of that meeting, Lloyd had already introduced me to Frank Bien.”

 left Trenchard impressed. “Very few founders are willing to let go of the CEO reins at their Series A. The ability to see your own weaknesses, recognize the import of something and make changes so quickly is really rare. Lloyd made a very rational decision and it changed the whole trajectory of the company,” Trenchard says.

“At the time, it was a great product heading in a really interesting direction, but they unlocked an entirely different vector of growth when Frank joined. It was also unique that Lloyd stayed very much engaged in the business,” says Trenchard. “I’ve seen some startups struggle with keeping the founder involved once they’re no longer CEO, but Lloyd flourished in the CTO role. He and Ben built a really great team that continued to push the product, which allowed them to keep their edge.”

“When the board told me it was time to hire a sales team, I knew I was in over my head. I immediately thought of Frank. We had kept talking about the business after that first meeting in a coffee shop and I knew he was interested in joining Looker, but I took my time to decide and spent hours talking with Marc Randolph about bringing him on. It wasn’t a shotgun marriage. 

Truthfully, it was hard to relinquish the CEO hat — and candidly, I brought Frank on as President at first because I was nervous about taking it off

But the process of raising their Series A convinced Tabb that Bien had earned the CEO title. “We’d raised $2 million in our 2012 seed and I was pretty cheap — by the time Frank had joined as President a year later in the summer of 2013, I’d only spent $400k of what we’d raised. I remember thinking we had four years of runway,” Tabb laughs. “Then Frank came in and started hiring all of these sales folks and amazing leaders. I remember sitting with him in the conference room just a few months later where told me that we’d have to raise money because we’d be out in December, which was an ‘Oh shit,’ moment for me.”

Their initial plan was to take the same approach that had worked so well during the seed round. “We went to go see another investor we knew personally. And they wanted to invest — but at the last round’s valuation. So we went to the First Round office to ask Bill for advice. He coached us in such a direct and spot-on way,” says Tabb.

Here’s the play Trenchard told Tabb and Bien to run: “Bill told us to go see six partners in one week — not more because we didn’t want to be overshopped. After we explained the deal to investors, if they were interested, they’d bring us into the partner meeting the following week,” says Tabb. “So Frank and I put our nice shirts on and we drove up and down Sand Hill Road. We saw seven firms — and the next week we had six partner meetings, and ended up going with 

All these years later, Trenchard reflects on this advice he gave back in 2013: "

We started Pitch Assist to gather fundraising wisdom for our companies

 because we observed that many founders were caught off guard by how they need to change their strategy for their Series A,” he says. “

Fundraising advice changes based on several factors. You can’t always borrow someone else’s playbook or copy-and-paste what worked in the last round.

 At its Series A, Looker had strong proof points in a big market, and the play we helped them put together was the best option for them at the time. And Frank and Lloyd nailed the execution.”

After fighting in the fundraising trenches together, Tabb realized that Bien deserved the CEO title. “Looking back now, 

I firmly believe the reason that Looker has executed to the degree that it has is because we brought in professional management

,” says Tabb. “It was obvious that Frank was a man of very high integrity and he knew enterprise software — he knew how to execute in this space. So while it felt like I was taking a risk by turning over the CEO role and committing us to the enterprise route, I reminded myself that 

startups are all about risks — and he was a much better bet than I was

As a repeat founder, I often like to say that I’m a one-man band. I’ve played all the instruments and can do just about every startup job. But that doesn’t mean I do them all well. Keep the instrument you’re best at, and give the rest away.

From Bien’s perspective, the early timing of his arrival smoothed over bumps that he’s seen other startups run into when transitioning to professional management. “I’ve seen professional CEOs brought in to replace the founder, but it was always four or five years in, which creates a very different dynamic. At Looker, I wasn’t a founder — I take the founder designation very seriously — but I did join around 10 or 12 employees with a small revenue stream, which is pretty unusual,” says Bien.

“Joining at such an early phase made it so much easier. It was very peaceful and natural. If you do that replacement later on in the startup’s life, then usually the whole management team is being blown out. And even if the founder stays on, it can be a weird dynamic. It can get ugly and negative. But because we made that transition so early on at Looker, we avoided all of that.”

Commit to a model — and learn it inside and out.

Now in place as CEO, Bien’s task was to figure out how to scale Tabb's early sales approach. He had to turn the scrappy, promising startup into a model-driven company.

“When I stepped into Looker in mid-2013, there was a lot of early traction,” says Bien. “Lloyd already had won a solid base of customers with another early employee, 

, but there were still a lot of unknowns. Should we sell for $500 a month or $5,000? It was all up in the air — and there were no spreadsheets or slide decks to be found,” Bien laughs. “In my mind, it was similar to Marketo’s model, meaning it wasn’t going to be just a couple hundred bucks a month. We needed to be in the neighborhood of $25,000 a year per customer.”

To convince prospects to sign on to that size of a deal, Bien knew Looker needed to have a sales team in place. “Lloyd’s early sales intuition was right, you had to do a robust proof of concept. It reminded me of 

. We wanted to do an inside sales motion of that. Those salespeople had to be supported by an embedded technical person,” says Bien.

“For most companies in the data space, pre-sales folks are like plumbers, they're just hooking stuff together. We tried to take a different approach at Looker by asking prospects for a dataset and then putting economics or math majors to work. In the early days, they wore all the hats: They were pre-sales, post-sales, customer support. Eventually, those became separate roles as we scaled,” says Bien.

In demos, don't just show the customer the answer they’re looking for. Also show them what they should be asking, what they don’t even know.

This approach certainly wasn’t low-lift. The Looker team had to push back on concerns from investors and advisors that this sales motion wasn’t scalable. “Every VC told us not to do it this way,” laughs Bien. “They were certain that it would never scale.”

Trenchard was in that camp. “I’ll admit I thought it was a slightly odd approach at the time, as I was focused on helping them scale the business, but I knew they’d figure it out,” he says.

But Bien was confident because he’d done the math. “After crunching the numbers, we saw that by the time we had 2,000 customers, we could be doing $100 million dollars in ARR, and on the path to going public. That was the model from 2013 on. And as a side note, we were pretty spot on — we’re at 1,700 customers now, so we actually did a little better than we predicted,” says Bien.

But you have to know your model backwards and forwards to make a bet like that.

 We knew the margins on the costs we were sinking into pre-sales support made sense. But if we had been unsure whether it was going to take 2,000 customers or 100,000 customers to reach the $100 million run rate, that would have been an incredibly risky move — we very easily could have been lighting our VC dollars on fire.”

If you’re confident in your model, you don’t have to worry whether you can support all the resources you’re sinking into sales — you can do the math and find out.

With the numbers ironed out, Bien started assembling an early sales team to execute. “Based on the customers we were targeting, we knew we could take advantage of being in the First Round community and get connected with all of these potential customers that were disrupting their industry based on data, like thredUP and HotelTonight,” says Bien.

Looker Office 2.jpg

Looker went from a single customer to more than 1700, from a small, scrappy team in Santa Cruz to a 700-person company spanning eight offices around the globe. Now on the heels of its acquisition by Google, co-founders Lloyd Tabb and Ben Porterfield, CEO Frank Bien, and first investor Bill Trenchard come together to fill in the details and dig into the lessons learned from Looker's founding story.

The Inside Story of How This Startup Turned a 216-Word Pitch Email into a $2.6 Billion Acquisition

— a startup building the fastest email experience in the world.

We’ve all heard that product/market fit drives startup success — and that the lack thereof is what’s lurking behind almost every failure.

For founders, achieving product/market fit is an obsession from day one. It’s both the hefty hurdle we’re racing to clear and the festering fear keeping us up at night, worried that we’ll never make it. But when it comes to understanding what product/market fit 

 to get there, most of us quickly realize that there isn’t a battle-tested approach.

In the summer of 2017, I was waist-deep in my search for a way to find product/market fit for my startup, 

. Turning to the classic blog posts and seminal thought pieces, a few observations stuck out to me. Y Combinator founder Paul Graham described product/market fit as when you’ve 

 it as when users spontaneously tell other people to use your product. But of course, the most cited description comes from this passage in Marc Andreessen’s 2007 

“You can always feel when product/market fit is 

 happening. The customers aren't quite getting value out of the product, word of mouth isn't spreading, usage isn't growing that fast, press reviews are kind of ‘blah,’ the sales cycle takes too long, and lots of deals never close.  

And you can always feel product/market fit when it

 happening. The customers are buying the product just as fast as you can make it — or usage is growing just as fast as you can add more servers. Money from customers is piling up in your company checking account. You're hiring sales and customer support staff as fast as you can. Reporters are calling because they've heard about your hot new thing and they want to talk to you about it. You start getting entrepreneur of the year awards from Harvard Business School. Investment bankers are staking out your house.”

For me, this was the most vivid definition — and one that I stared at through tears. 

We had set up shop and started coding Superhuman in 2015. A year later, our team had grown to seven and we were still furiously coding. By the summer of 2017, we had reached 14 people — and we were 

 coding. I felt intense pressure to launch, from the team and also from within myself. My previous startup, 

 by LinkedIn in less time. Yet here we were, two years in, and we had not passed go.

But no matter how intense the pressure, I wasn’t ready to launch. Common practice would be to "throw it out there and see what sticks,” which may be fine after a few months of effort when the sunk cost is low. But the “launch and see what happens” method seemed irresponsible and reckless to me — especially given the years that that we had invested.

Further compounding the pressure, as a founder, I couldn’t just tell the team how I felt. These super-ambitious engineers had poured their hearts and souls into the product. I had no way of telling the team we weren’t ready, and worse yet, no strategy for getting out of the situation — which is not something they would want to hear. I wanted to find the right language or framework to articulate our current position and convey the next steps that would get us to product/market fit, but was struggling to do so.

That’s because the descriptions of product/market fit I found were immensely helpful for companies 

. If, after launch, revenue isn’t growing, raising money is tough, the press doesn't want to talk to you and user growth is anemic, then you can safely conclude you don't have product/market fit. But in practice, because of my previous success as a founder, we didn’t have problems raising money. We could have gotten press, but we were actively avoiding it. And user growth wasn't happening because we deliberately choosing not to onboard more users. We were pre-launch — and we didn’t have any indicators to clearly illustrate our situation.

The descriptions of product/market fit all seemed so post hoc, so unactionable. I had a clear understanding of where we stood, but I had no way of conveying that to others — and no plan for the part that should come next.

So I racked my brain for an answer on how to travel the distance between where Superhuman was and the high bar that we needed to hit. And I eventually started to wonder: 

what if you could measure product/market fit?

 product/market fit, then maybe you could 

 product/market fit until you achieved it.

Reoriented around this purpose and reinvigorated by the new direction, I set out to reverse engineer a process for getting to product/market fit. Below, I outline the findings that followed, specifically unpacking the clarifying metric that made everything fall into place and the four-step process we used to build an engine that propelled Superhuman forward on the path to finding our fit.

ANCHORING AROUND A METRIC: A LEADING INDICATOR FOR PRODUCT/MARKET FIT

On my quest to understand product/market fit, I read all I could and spoke with every expert I could find. Everything changed when I found 

, who ran early early growth in the early days of 

 and later coined the term “growth hacker.”

The product/market fit definitions I had found were vivid and compelling, but they were 

 indicators — by the time investment bankers are staking out your house, you already have product/market fit. Instead, Ellis had 

just ask users “how would you feel if you could no longer use the product?” and measure the percent who answer “very disappointed.”

After benchmarking nearly a hundred startups with his 

, Ellis found that the magic number was 40%. Companies that struggled to find growth almost always had less than 40% of users respond “very disappointed,” whereas companies with strong traction almost always exceeded that threshold.

Ask your users how they’d feel if they could no longer use your product. The group that answers ‘very disappointed’ will unlock product/market fit.

 in a 2015 open research project. 51% of these users responded that they would be very disappointed without Slack, revealing that the product had indeed reached product/market fit when it had around half a million paying users. Today, this isn’t too surprising, given Slack’s 

Inspired by this approach, we set out to measure what the responses would be for Superhuman. We identified users who recently experienced the core of our product, following Ellis’ recommendation to focus on those who used the product at least twice in the last two weeks. (At the time we had between 100 and 200 users to poll, but smaller, earlier-stage startups shouldn’t shy away from this tactic — you start to get directionally correct results around 40 respondents, which is much less than most people think.)

 survey asking the following four questions:

1. How would you feel if you could no longer use Superhuman?

A) Very disappointed 
B) Somewhat disappointed 
C) Not disappointed

2. What type of people do you think would most benefit from Superhuman?

3. What is the main benefit you receive from Superhuman?

4. How can we improve Superhuman for you?

With the responses collected, we analyzed the first question:

With only 22% opting for the “very disappointed” answer, it was clear that Superhuman had not reached product/market fit. And while this result may seem disheartening, I was instead energized. I had a tool to explain our situation to the team and — most excitingly — a plan to boost our product/market fit

FROM BENCHMARK TO ENGINE: THE FOUR-STEP MANUAL FOR OPTIMIZING PRODUCT/MARKET FIT

Determined to move the needle, I became singularly focused on ways to improve our product/market fit score. The responses to each survey question would be key ingredients in what became the framework for fulfilling our goal.

Here are the four components that comprised our product/market fit engine:

1) Segment to find your supporters and paint a picture of your high-expectation customers.

With your early marketing, you may have attracted all kinds of users — especially if you've had press and your product is free in some way. But many of those people won't be well-qualified; they don't have a real need for your product and its main benefit or use case might not be a great fit. You wouldn't have wanted these folks as users anyway.  

As an early-stage team, you could just narrow the market with preconceived notions of who yo

If you're wondering how other startups got their start:

The Inside Story of How This Startup Turned a 216-Word Pitch Email into a $2.6 Billion Acquisition

How Superhuman Built an Engine to Find Product/Market Fit

PlanGrid's Playbook for Startups to Crack Big, Established Industries

AppNexus' First Pitch — Before It Steered Around the Cloud to Win Big in AdTech

From 0 to $1B - Slack's Founder Shares Their Epic Launch Strategy

There’s a .00006% Chance of Building a Billion Dollar Company: How This Man Did It

Drew Houston’s Morph from Hacker to Hyper-Growth CEO

90% of Feedback is Crap: How to Find the Next Big Startup Idea

How Design Thinking Transformed Airbnb from a Failing Startup to a Billion Dollar Business