Running Lean, 3rd Edition

Chapter 4. Stress Test Your Idea for Feasibility

Feasibility: Can you build this?

Product roadmaps have traditionally been used for feasibility testing and rollout planning. But product roadmaps assume you know what you’ll be building for the next 18–24 months, which you don’t. This is where traction roadmaps come in.

Don’t create a product roadmap. Use a traction roadmap instead.

Unlike a product roadmap, a traction roadmap isn’t output oriented, but outcome oriented. You already learned about an outcome-oriented metric in the previous chapter, which fits the bill perfectly: traction. You also know how to measure it three years into the future with your minimum success criteria.

But while three years is the right time frame for sizing the viability of your idea, for the reasons covered in the last chapter, it’s still too far out into the future for determining your idea’s feasibility—i.e., how you’ll pull it off.

You need a way to break your MSC goal into shorter-term milestones. These intermediate milestones will help you visualize your journey as more manageable stages and chart a stage-based rollout plan. That’s what we’ll cover in this chapter, which focuses on stress testing feasibility (Figure 4-1).

Figure 4-1. Stress testing feasibility

Charting a Traction Ramp

In the last chapter we saw Steve struggling to figure out how he’d meet his goal of having roughly 1,600 customers by year 3. How would you suggest he model the first three-year ramp for his product: linearly, nonlinearly, exponentially?

The ramp can’t be linear because the shortest distance between two points is a straight line. Growing a product linearly would require already having the perfect plan to execute. A perfect plan in the world of startups is a myth.

The diffusion of innovations theory discussed in “Estimate the required number of active customers” in Chapter 3 posits that market share for a new idea follows an S-curve. The first half of this S-curve is the familiar hockey-stick trajectory and the right answer to how you should model the first three years of your product rollout ramp (Figure 4-2). Remember that with your MSC, your objective is getting a little beyond product/market fit (the inflection point in the hockey-stick curve).

Figure 4-2. The S-curve and adoption life cycle

A hockey-stick trajectory isn’t only for startups. All new product adoptions, whether at a startup or a large company, follow a similar trajectory, starting with a flat portion that gets increasingly steeper over time, until it eventually reaches market saturation or gets disrupted by something else.

Since your MSC goal pegs the number of customers you’ll need at the three-year mark, you need just one more input assumption in order to model the ramp to your goal: growth rate.

What’s a good growth rate to use for an early-stage product: 3x/yr, 5x/yr, 10x/yr, or something even higher? When asked to pick a growth rate for their traction roadmap, a lot of entrepreneurs steer toward a smaller number, but this isn’t necessarily the best strategy.

Take a look at Figure 4-3, where I chart traction roadmaps using three different growth rates.

You may have been surprised to see that using a smaller growth rate actually requires a higher customer acquisition rate at the beginning than using a larger growth rate. A 10x model requires half the number of customers at year 2 and a quarter the number of customers at year 1 than a 5x model!

Figure 4-3. Three ways to hit your goal

This is because your three-year endpoint is fixed by your MSC goal and cannot change. All you are changing with your growth rate assumption is the slope of your hockey-stick curve. When confronted with this counterintuitive way of thinking about growth rates, many teams I coach change course and veer toward using a higher growth rate.

You want to be careful not to go too far the other way, either. I find that the right starting growth rate should strike a balance between learning and scalability, and I recommend that you set your starting growth rate to 10x/yr for the first 3 years.

While using a 10x/yr growth rate may only seem appropriate for hyper-growth startups, that isn’t true. Remember that every company in the world starts at the same place—with a single customer. If you plan on growing from 1 customer to at least 100 customers in the first 3 years, you can use a 10x model:

• Year 1: 1 customer

• Year 2: 10 customers

• Year 3: 100 customers

Steve Charts His Traction Roadmap

Steve decides to use the recommended 10x growth rate for his traction roadmap, which results in the chart in Figure 4-4.

Figure 4-4. Steve’s traction roadmap

He’s greatly relieved to see that with a 10x model, he only needs to acquire 17 customers in year 1 versus 500+ customers with his original linear model. But that relief is short-lived. As he turns his attention to the righthand portion of the hockey-stick curve, a new worry comes over him.

He pulls out his phone and texts Mary a screenshot of his traction roadmap with a note below it: “How am I going to acquire 1,500 new customers in year 3? That’s three times the customer acquisition rate I was originally worried about!”

He gets the following reply from Mary: “You have to walk before you can run. Focus on the lefthand side of the curve before the right, and use the numbers on your traction roadmap to formulate a now-next-later rollout plan.”

Steve: “Hmm…okay, but even the year 1 goal, while smaller than what I was originally thinking, is still a stretch. I’m not sure how I’ll acquire 17 customers when my product won’t be ready for another 9 months. That’s just 3 months to get 17 customers.”

Mary: “You’ll just have to find a way to get those customers sooner :)”

Steve: “I don’t know how to do that :(”

Mary: “Let’s meet for lunch tomorrow to discuss.”

Steve: “I can’t wait.”

Mary: “In the meantime, here’s a thought exercise to work through. Imagine that you are an aspiring first-time restaurateur looking to open a new restaurant. It goes without saying that the food business is risky—most new restaurants don’t survive their first year. Also, most restaurateurs, like entrepreneurs, typically have their solutions figured out. They have the perfect menu, the silverware and napkins all picked out…all they need is an investor to write them a big fat check and they’ll be in business. The problem, of course, is that no one wants to take a chance on a first-time restaurateur because of all the risks associated with starting a new restaurant. Sound familiar?”

Steve: “lol…very funny.”

Mary: “The key to breaking this catch-22 is prioritizing your starting risks versus your scaling risks. Your homework is to come up with what’s immediately riskiest for this restaurateur and to formulate a now-next-later rollout plan.”

Steve: “Okay, I’ll give it a go…”

Mary: “Since we’ll be talking food, maybe we could meet at the new taco place that just opened around the corner for lunch tomorrow. I heard it’s pretty amazing.”

Steve: “I love that place…but we’ll need to get there early to beat the lines. Otherwise, it could easily be a one-hour wait.”

Mary: “11:30 works for me…see you then.”

Formulating a Now-Next-Later Rollout Plan

Many entrepreneurs are understandably in a rush to get to the righthand side of the hockey-stick curve. They do this by trying to go fast on everything. But going fast on everything doesn’t necessarily make you go faster. Rather, it’s a recipe for getting lost faster, because it’s easy to lose focus and fall prey to the premature optimization trap.

Some examples of premature optimization include:

• Trying to optimize a product for thousands of users at the outset before you have any users

• Hiring a VP of sales before you have any customers

• Raising funding before you have traction

Premature optimization is one of the top killers of startups because it prioritizes the wrong risks at the wrong time, which depletes your already limited resources for achieving product/market fit. The way to avoid the premature optimization trap is by embracing a Continuous Innovation mindset.

Mindset #4

Right action, right time.

At any given point, there are only a few key actions that stand to have the biggest impact on your business model. Your job is to focus on those key actions and ignore the rest. This is the essence of the right action, right time mindset.

Isn’t there a danger of being too short-sighted? As an entrepreneur, you need to be able to simultaneously plan for the long term while acting for the short term. But as the startup journey is inherently shrouded in a fog of uncertainty, we can often only see what’s immediately in front of us and struggle to make clear plans too far into the future. That’s okay—this is where the now-next-later rollout plan comes in.

The idea behind the now-next-later plan is viewing your traction roadmap using three time horizons that roughly align with the three distinct segments of the hockey-stick curve: a flat section, followed by an increasingly steeper section that continues until you hit a noticeable inflection point, where the curve shoots up. Each of these segments represents a specific stage in the product life cycle, visualized in Figure 4-5:

1. Problem/solution fit

2. Product/market fit

3. Scale

You use your traction roadmap to determine the traction goal you need to meet at the end of each of these time horizons. You then attempt to formulate a plan for each time horizon. As can be expected, your now plan should be the most concrete, your next plan less so, and your later plan the fuzziest.

Figure 4-5. The three stages in the product life cycle

If you use a 10x growth rate, each stage is roughly one order of magnitude larger than the previous stage. What’s not obvious is recognizing that these stages also drive what’s riskiest in your business model. This is a key insight for formulating a stage-based now-next-later rollout plan—one that prioritizes your riskiest assumptions systematically.

Mindset #5

Tackle your riskiest assumptions in stages.

Let’s walk through these three stages, discussing at a high level the objectives, typical timeline, deliverables, and strategies for navigating each of them.

Stage 1: Now—Problem/Solution Fit

While no one enjoys the flat section of the hockey-stick curve, with the right mindset you learn to see it as a gift. The first step to practicing the right action, right time mindset is realizing that you can’t get to the right side of the hockey-stick curve without first going through the flat section.

In a product’s earliest stages, you need to decelerate, not accelerate.

The flat section of the hockey-stick curve is where you uncover the key insights or secrets that allow you to build something unique and valuable. You do this by taking the requisite time to deeply understand your customers, uncover real problems worth solving, and test possible solutions using a Demo-Sell-Build process.

The counterintuitive insight here is that you don’t need a working product to acquire paying customers.

The end deliverable of this stage is an evidence-based go or no-go decision to move forward to the build stage of your idea (stage 2).

By the end of this first stage, you specifically need to:

• Have a clear understanding of your customers’ needs (and wants)

• Know the smallest thing you need to build to deliver value to your customers (your MVP)

• Have secured sufficient tangible commitments (e.g., advance payments, letters of intent) from customers

Achieving problem/solution fit typically takes three to six months for most products. We’ll cover the detailed steps for achieving problem/solution fit in Chapters 7 through 11.

Stage 2: Next—Product/Market Fit

By the end of stage 1, you should have a clear definition of a product you know customers will want, rather than just hope they want. You then spend the next several weeks or months building out the first iteration of your product (your MVP) and getting ready for launch. The initial objective is racing toward demonstrating value delivery—i.e., determining whether you’ve built something that customers want. You do this by continuously improving your product using continuous feedback loops with your customers.

The counterintuitive insight here is that you don’t need lots of users to hit repeatability in your business model.

Driving repeatability in your business model is the key deliverable of this stage. This is also where you cross the inflection point of the hockey-stick curve and start setting your sights on accelerating growth, which prepares you for entering stage 3.

Achieving product/market fit typically takes 18 to 24 months for most products. We’ll cover the detailed steps for achieving product/market fit in Chapters 12 through 14.

Stage 3: Later—Scale

Some level of success is guaranteed after hitting product/market fit. The question is how much. In the Scale stage, there is a marked shift in strategy where your focus changes from getting the product right to pursuing growth. During this stage, you use several optimization experiments to test many possible growth strategies and campaigns.

The counterintuitive insight here is that even at this stage, going fast at everything is a recipe for getting lost faster—you need to focus on one engine of growth at a time.

The goal of this book is to help you navigate the journey from concept to just beyond product/market fit. I’ll share some high-level guidelines on pursuing growth and life beyond product/market fit in Chapter 14.

Steve Gets a Lesson on Right Action, Right Time

Mary grabs the last available table at the taco restaurant and signals Steve over. As he pulls up a chair and sets down his lunch, he lets out a sigh and comments, “Wow, look at that line. It’s already out the door and snaking around the corner. And it’s only 11:45. We got here just in time.”

“Yup. Ever since this place got written up and featured on a few lists, it’s been like that every single day.”

Mary waits for Steve to settle in and then asks, “So how did you do with the challenge question from yesterday? What do you think are the riskiest assumptions facing a first-time restaurateur?”

“Well, look at this place,” Steve replies. “Surely, a good product and a good location are the ticket. As they say in real estate, it’s all about location, location, location.”

“Are you sure starting with a good location is a smart idea for a first-time restaurateur?” asks Mary.

She adds, “Good locations come at premium prices, which means the runway for making the restaurant a success is a lot shorter and the stakes a lot higher.”

Mary waits for a nod from Steve, then goes on, “Furthermore, a good location alone doesn’t guarantee success. Surely you’ve been to bad restaurants in great locations, and vice versa.”

“Are you saying the location isn’t important?”

“Not at all. A good location helps with growth—but that’s a scaling risk, not a starting risk. At this point in our story, our restaurateur has an unproven product. So their starting risks should center around value delivery, not growth acceleration.”

Mary lets that sink in and then continues, “The reason I picked this place, apart from the amazing tacos, of course, is that while they are growing like crazy and have a number of prime locations today, that’s not how they started. Do you know their origin story?”

Steve shakes his head.

“The founder, Jack, started with a food truck on the east side of town, which as you know is not exactly prime real estate.”

Steve jumps in. “I remember reading about that now. I’m guessing that because starting with a food truck is a lot cheaper and faster than opening a brick-and-mortar restaurant, it allowed him to more quickly test his food concept. Was the food truck the MVP for the restaurant?”

Mary nods her head. “Exactly. The trap too many entrepreneurs fall into is premature optimization. They imagine their finished product being used by hundreds or thousands of customers and try to bring that to life. This prioritizes the wrong risks and leads them to work on the wrong actions at the wrong times. At the earliest stages of an idea, you don’t need lots of users, just a few good customers—your early adopters.”

“So what would you say was his riskiest assumption when he was starting? The food?”

“In a manner of speaking, yes, but there’s more to it than just cooking up a bunch of food and driving around town trying to sell it. The first battle with any product is getting the attention of customers. You remember the Innovator’s Gift? Innovation is fundamentally about causing a switch. Come lunchtime, in this town there are over a hundred lunch options within a three-mile radius. Why would anyone choose to go to the food truck?”

“Word of mouth?” Steve thinks aloud.

“Word of mouth comes later. You have to first grab the attention of your first batch of customers (your early adopters) with a unique value proposition. Once you have their attention, you need to deliver something different and remarkable. If you manage to do that, then word of mouth kicks in.”

“Sure, that makes sense. But how do you actually get customers to the food truck? Did the founder invest in a huge branding campaign or already have a huge social media following?”

“Nope. Let me show you.” Mary takes out her phone, pulls up an early photo of the food truck, and shows it to Steve.

“Tell me the first thing you notice.”

Steve looks at the photo and sees a huge banner that spans the top half of the food truck.

“Korean BBQ Tacos?” he answers.

“That’s exactly right. But that isn’t the name or logo or even tagline of the restaurant—the things we product people love to obsess over. What is it?”

“Their unique value proposition?”

“Bingo. Here in Texas, if you offer great BBQ or great tacos you’ll grab the attention of foodies—the early adopters, in this case. If you do both well, that’s even better, but there are already a number of good places doing just that. But if you add a new twist—Korean BBQ tacos—that’s unique and attention-grabbing. That’s the kind of different that foodies and influencers want to be the first to sample, then tell others if it’s good enough.”

Mary pauses to take a sip, then goes on. “So let’s put all the pieces together here. The biggest risk for a first-time restaurateur starts with attention. It starts by asking, what’s the unique value proposition of your product? What’s it for and who’s it for? In this case, the founder decided to target foodies and chose a food truck because it’s a much cheaper and faster vehicle (literally) for reaching this audience and testing his concept. That was his now plan, which he put into action in days, not weeks or months.”

“Did he also come up with a next and a later plan at the same time?”

“Yes, he did. But they were pretty high-level. He had always envisioned opening up multiple restaurants around town as his next plan, and had ambitions for moving into other cities and building a nationwide brand as part of his later plan.”

“How long did the founder run the food truck?” Steve asks.

“In his case, not very long. Not unsurprisingly, his original concept wasn’t the one that took off, but he discovered his winning concept through dozens of small iterations in the early days of the food truck. He hit on some great recipes and then word of mouth kicked in. Within four weeks of opening, long lines started forming even before the food truck opened for lunch service.”

“That quickly?” Steve asks.

“Yup, and it went bonkers after that. He started selling out every single day, which caught the attention of a few food critics. Once they covered and featured the food truck, the lines got even longer. He had to figure out a way to handle all this demand, which led him to put his later plan into effect.”

“Opening another food truck!” Steve jumps in.

“Yup. He opened another food truck very close to the location we’re in. A food truck was still a cheaper way to get into this market. As you know, rents here aren’t cheap. That food truck started selling out too, which made for a killer early traction story that he used to raise money from investors. Within nine months of starting his original food truck, he had converted both of them into two brick-and-mortar locations. And I think he has three more locations coming. The rest, as they say, is history—”

Steve cuts in. “Weren’t his investors worried that Jack wouldn’t be able to scale the business? After all, running a food truck is quite different from running multiple brick-and-mortar locations, much less building a nationwide brand.”

“I’m sure they were, but those are exactly the kind of risks that investors love to get involved with—scaling risks versus starting risks. The initial challenge for any product is solving for demand. Once you can generate sufficient demand, the supply side is usually solvable too.”

“By supply side, you mean building a product?”

“Yes, exactly. Another way of putting this is that demand-side risks have to do with the customer (desirability) and market (viability) risks, while supply-side risks are typically product (feasibility) risks.”

“Sure, that makes sense,” Steve acknowledges.

“I’m sure Jack had all kinds of scaling risks as he grew his business from two food trucks to a dozen restaurants, from staffing to training to branding. But once you have a good core validated product, these are less risky and often solvable obstacles. Think back to the early days of Facebook, YouTube, and Twitter. In their journeys from thousands of raving early adopters to hundreds of millions of users, they all had massive scaling risks that they too managed to overcome. Remember Twitter’s fail whale?”

Mary notices Steve’s eyes widen.

“Avoid premature optimization,” he says. “This has all been very enlightening…but I’m still trying to process how to apply this to my product.”

“Whenever you encounter a case study like this one, it’s important to separate principles from tactics,” Mary explains. “While growing a food business can be tactically quite different from, say, growing a software business, the underlying principles behind the tactics are universal. They can be applied to any kind of product.”

“Are these principles truly universal, though? I can see how they worked for a restaurant, but the MVP for food is a few hours of cooking. What do you do when you’re building products that take months or years to build?”

Mary smiles. “You were always the hardest one on the team to convince. But you’re right. So let’s take it up a notch and consider a product that does take years to build—an electric car.”

Mary takes another sip of her drink, then continues. “Tesla. If you were Elon Musk with a vision of building the first affordable electric car in 2006, how would you formulate a now-next-later rollout plan?”

Just then Mary’s phone goes off.

“Lunch break is officially over. Take a stab at applying these principles to the Tesla launch and let’s meet for coffee tomorrow.”

And with that, Mary is out the door.

Steve Learns About Wizard-of-Oz MVPs

“So, how did you do with the Tesla rollout plan?” Mary asks Steve the next day at their usual coffee hangout.

“I already knew some of the Tesla launch story and was able to put a few pieces together, I think,” Steve responds.

“Let’s hear it.”

“Okay, here goes. First, I’ll admit that if you had asked me this question before our conversation yesterday, I would have probably listed technology, design, manufacturing, charging infrastructure, and branding as the riskiest assumptions for an upstart car company—especially one led by a founder with no prior car-building experience. After yesterday’s conversation, however, I was able to identify all of these risks as supply-side risks, not demand-side risks. So then I applied the Innovator’s Gift and instead asked: why would anyone want to switch to an electric car?”

Mary nudges Steve to keep going.

“I’m guessing for some it might have been cheaper energy costs, and for others it was reducing their carbon footprint.”

“That’s very good, Steve. What Elon Musk certainly had going for him in 2006 were two switching triggers: increasing awareness of climate change and rising gas prices. These switching triggers had already led to some switching behavior, from traditional combustion-powered cars to hybrids within certain subsegments of the car-buying population—his potential early adopters. The problem with hybrids though is that they still rely, at least partially, on fossil fuels. Complete independence from fossil fuels, or achieving zero emissions, was the promise of an affordable electric car.”

“Yeah, I like how you position that as part of a much bigger vision,” says Steve. “So the first order of business for Tesla was then testing its unique value proposition, which I’m guessing Elon Musk did by sharing his zero-emissions vision with enough people to get them excited and make them pay attention.”

“That’s right, but they took it even further. They got people to preorder their first electric car before it was even built, using a Demo-Sell-Build process,” adds Mary.

“That’s the part I don’t get. I understand how you can apply Demo-Sell-Build to a food concept, but a car, especially an electric car that’s relying on technology yet to be invented, takes years to build. How do you iterate and test quickly?”

“Ah…But did they build an entire car out of the gate?”

A puzzled look comes over Steve’s face. “Do you mean the roadster?”

“Yes. The first car that Tesla launched, the Tesla Roadster, wasn’t even a car that they built—at least not entirely. While the Tesla Roadster had the Tesla emblem on it, the design and chassis were licensed from another car company: Lotus Motors. Now, why would they do that?”

“To get the car to market sooner?” Steve muses.

“Exactly. Compared to most car companies that take 10 years to launch a new car from concept to market, Tesla pulled this off in just 2 1/2 years. That’s moving at light speed in the car industry. What I love about this case study is that it emphasizes that while speed of learning is key, it’s also relative. You only need to outlearn your competition to win.”

“I love that,” Steve interjects.

“But speed to market was only part of the story here. Not having to design, build, and manufacture an entire car allowed them to prioritize testing their next riskiest assumption and ignore the rest. Can you guess what that was?”

“The electric battery?” Steve asks.

“Yup. Designing, building, and manufacturing a car from scratch, while a lot of work, wasn’t an insurmountable risk. Lots of car companies already know how to build production-ready cars. None of them, at the time, knew how to build production-ready electric cars. That’s what was different and worth prioritizing.”

Steve jumps in. “So by licensing an existing car and retrofitting their battery into it, they avoided the bulk of the known work and prioritized the unknown work. They didn’t need to hire automotive engineers or build a large factory. They could just focus on building an electric battery, stick that into an existing car, and sell that. I know I’m simplifying, but that’s genius.”

“Yup—and that was their now plan. By the way, this approach of cobbling together existing solutions in an MVP is a commonly used validation recipe in the Continuous Innovation Framework called a ‘Wizard-of-Oz MVP.’ It was first popularized and codified into a pattern during the early days of the Lean Startup movement.”

“Wizard-of-Oz? I’m guessing it’s named after the movie?”

“Yes. The essence of this validation pattern is to fake it until you’re ready to make it. In other words, reduce the scope of your initial MVP by cobbling together existing solutions, instead of building everything from scratch.”

“How do you ensure defensibility if you’re cobbling together existing solutions?” Steve asks.

Mary responds, “Remember that the goal is still delivering on a unique value proposition. That unique value may come from a novel approach to assembling existing solutions where the whole is greater than the sum of its parts, or it may come from a novel component to the assembled solution that you provide. In the case of Tesla, it was the latter. They electrified an existing car with their unique battery technology, thereby delivering a new UVP that customers wanted.”

Mary notices Steve staring into space, and stops talking to get his attention.

“Sorry. My mind is racing. I think I might be able to apply the Wizard-of-Oz MVP pattern to speed up my product launch. I’ll have to think more about that…I’m still not clear on how Tesla managed to balance customer demand against its technical risks, though. I mean, they were taking preorders for a car that was going to be relying on technology that was still being invented. Wasn’t there a huge risk that they would get overwhelmed by customer demand and make promises that they couldn’t deliver on?”

“Yes, there certainly was that risk, which they managed using a stage-based now-next-later rollout plan.”

Mary sees a confused look come over Steve’s face, so she elaborates further. “Elon Musk promised the world an affordable electric car in 2006, but the first car Tesla launched, the Roadster, was the opposite of that, with a starting price of over a hundred thousand dollars. Theoretically, they could have retrofitted their battery into any car. Why did they pick a really expensive sports car and not something more affordable like a Kia, a Volkswagen, or a Ford Mustang?”

“Hmmm…I want to say they were going for premium branding or profits maybe, but I’m guessing there’s more to it than that?”

Mary smiles. “There sure is…this was all part of a carefully orchestrated three-stage rollout plan that played out over three different car models—all designed to prioritize tackling the right risks at the right time. Elon Musk vaguely described this rollout plan as his ‘secret master plan’ in a blog post in 2006. He further explained his master plan during the keynote launch of the Model 3. You can still find a replay of this keynote online. If I remember correctly, he covers the rollout plan around the three-minute mark.”

Steve makes a note to catch the replay while Mary presses on.

“Yes, the biggest risk for the first car was electrification. And licensing an existing car rather than building a new car was the first key component to their stage 1 or now plan,” she explains. “The next key component of the plan was picking the right car. Why the two-seater Lotus Elise roadster, and not some other car? What happens to a product’s demand when you set the starting price three times higher?”

“It goes down?” Steve answers.

“Exactly. By launching their first car using a premium sports car brand, they were creating a highly desirable car that everyone could see and want, but only a few could afford and get.”

“So they were never aiming to enter the mainstream market with the first car?” Steve asks.

“Nope. Remember the diffusion of innovations bell curve. They were solely focused on targeting the early adopter market, and in this case they used premium pricing very effectively to play the hockey stick. The roadster was a high-price, low-volume car. They only sold 500 cars a year for a few years and then stopped production.”

“So this was a learning MVP then?” Steve asks.

“That’s right, Steve. Stage 1 was all about testing their MVP—in this case, their battery in a shell of a sports car.”

“I see it now. Chances are high that someone who can afford to place an order for a seven-figure car already has multiple cars in their garage and wouldn’t be relying on this car as their primary vehicle. They’d be willing to wait up to two years for delivery and would drive the car very differently than a mainstream customer.”

“That’s exactly right. Having fewer customers also meant they didn’t need to be distracted by building scalable infrastructure—dealerships, charging stations, or service centers. They ‘concierged’ those aspects of value delivery.”

“And I’m guessing that once they de-risked the battery sufficiently, they leveled up to stage 2 and took on the luxury sedan market with their Model S?”

“Yes. This was a less high-priced, mid-volume car that they still rolled out incrementally with preorders. While they were rolling out the Model S, they took new sets of risks like manufacturing their own cars, and building charging stations, retail stores, and other infrastructure.”

“And I’m guessing the Model 3 was their stage 3—the affordable electric car for the mainstream market,” Steve adds.

“You got it. By the time they announced the Model 3, a lot of the infrastructure to handle the mainstream market was in place. More importantly, they’d sufficiently de-risked the idea of electric cars for the mainstream to literally buy in. The Model 3 car launch was the biggest product launch, securing 250,000 preorders within a span of 2 weeks.”

“Yeah, I remember reading about that launch. So by intentionally going slower at the beginning, they were able to cross the chasm and go much faster later. Now I get what you meant by playing the hockey stick. Did these stages follow a 10x traction model as well?”

“Yes. Elon Musk is known to be an exponential or 10x thinker, and these rollouts were textbook 10x. You can probably still find a few charts floating around online that depict Tesla’s traction roadmap forecasts for selling 500,000 vehicles across this 3-car rollout over 10 years.”

“10 years? That’s a much longer time horizon than the three years I’ve been using.”

“Sure. When building cars or rocket ships to Mars, one does need to adjust timelines. There’s nothing wrong with having a big vision that could take 10 years to realize. Your metaverse vision is no different. But remember that in order to make your vision actionable, you need to break the journey into smaller time horizons. Don’t forget that Tesla was still able to take preorders for their car just weeks after announcing it. No matter the type of product, you should aim to achieve problem/solution fit within the recommended three-month time box because you’re not yet building out the product at this stage.”

“Got it. And even during the build stage, Tesla took a huge shortcut with their Wizard-of-Oz approach,” Steve adds.

“That’s right, with discipline and a little creativity you can almost always reduce the scope on your initial MVP. I’m sure we’ll have lots more to discuss there when the time comes.”

“That makes sense. Though, I’m still unclear about how you extrapolate the traction roadmap for problem/solution fit from year 1 to three months—especially if you aren’t going to be ready with a product to sell by then. Do you always have to take preorders?”

“That’s a great question, Steve. The goal is getting as close to making a customer as possible, and taking advance payment with a preorder is about as close as you’re going to get at the problem/solution fit stage. That said, not all products and customer relationships are conducive to preordering. In those cases, it’s perfectly okay to use an earlier ‘customer making’ step in your customer factory, like starting pilots or trials ,or collecting leads.”

“Of course, the customer factory…that makes total sense. I’m guessing that I would use my customer conversion rate estimates from my Fermi estimate to determine those?” Steve asks.

Mary nods. “You got it.”

“Neat. I know our time’s up. This was a pretty inspiring case study and my head’s still spinning a little. I’m going to work on my now-next-later plan this afternoon at the office.”

Mary smiles. “My pleasure, Steve. Keep me posted.”

Steve Formulates His Now-Next-Later Rollout Plan

Back at the office, Steve is ready to think through his now-next-later plan. His first order of business is extrapolating his year 1 throughput goal of 17 customers down to 3 months in order to determine his problem/solution fit success criteria.

He pulls out his original Fermi estimate inputs and gets to work:

• Minimum success criteria: $10m ARR in 3 years

• Pricing model: $500/mo

• Customer lifetime: 4 years

• Customer acquisition conversion rate: 1%

  • User acquisition conversion rate (trials): 10%

  • Trial to paid conversion rate (upgrades): 10%

• Referrals: 20%

To keep the math simple, he assumes that the year 1 ramp, being largely flat, is okay to model linearly. He then uses the conversion rate assumptions from his estimate to translate those into the graph in Figure 4-6.

Figure 4-6. Steve’s problem/solution fit success criteria

He thinks through his options. By the three-month point, he will need to be either:

• Closing two paying customers per month (rounding up)

• Starting 20 trials per month

• Collecting 200 leads per month

Since he was already leaning toward using a subscription model for his product with a 30-day trial, he decides to use the trials metric as his problem/solution fit criterion. This means he will need to get 20 software companies to start a 30-day trial for his product with $500/mo pricing and continue to sign up 20 new software companies every month thereafter for the first year in order to hit his year 1 goal.

To pull this off he’ll need to really scope down his MVP, but he’s optimistic after learning about the Wizard-of-Oz MVP pattern. Steve believes he can go a lot faster and build something unique and valuable by starting with a plug-in solution to an already popular platform used by thousands of software companies, instead of trying to build an entire platform by himself. This will be his stage 1 (now) plan.

Like Tesla, he’ll eventually expand on his UVP and lead people to his own platform (stage 2). His big metaverse vision will play out in stage 3. He catches himself daydreaming about stage 3 and stops himself.

He outlines his now-next-later plan in an email and fires it off to Mary. A couple of hours later, he gets a text message from her.

Mary: “Nice work on the traction roadmap and now-next-later plan. I suggest sharing your business model design with a few advisors and friendly investors for feedback.”

Steve: “Isn’t it too early?”

Mary: “No, it isn’t. Notice I said for feedback, and not to raise funds. The challenge most early-stage founders face is communicating their idea clearly and concisely to others. Framing your initial conversation around feedback is a great way to practice, form relationships, and evolve toward a killer pitch.”

Steve: “My last round of pitching didn’t go too well. We just went around in circles. I got defensive a few times and it felt like a waste of time for everyone.”

Mary: “Don’t beat yourself up. A lot of founders struggle with getting others to see what they see at the outset. You have a much clearer story now, and the best way to refine your model even further is to start sharing it with others.”

Steve: “Do you have some tips for how to structure these early conversations?”

Mary: “Yes I do :) Look for an email on communicating your idea clearly and concisely.”