Market Sizing Guide

A startup’s potential scale is bound by its future market size, and consequently “what is your market size?” is one of the determining questions that most VCs ask early stage entrepreneurs.

Why does market size matter? 
How to estimate your market size? 
What is the exact definition of market size? 
When should market size be estimated for? 

If you only take a few things from this article: 

  • Big companies can only exist in big markets. 
  • Estimate market size using a bottom-up approach not top-down. In other words, multiply the number of customers by the average revenue per customer per year (which you can estimate through multiplying transaction volume by price).
  • Present your total addressable market and your future revenue for 5+ years into the future. 

At Pear we consider an entrepreneur’s clarity of thought and enduring ambition as more important than the market size number in a pitch deck. The objective of market sizing is to demonstrate that you are targeting a big market opportunity that you understand deeply. Startups have many unknowns and market sizing is a rough estimation, so keep it simple. 


1. Why does market size matter?

Big companies can only exist in big markets. 

Founder perspective: If you want to build a company that has a DoorDash-sized impact on the world then make sure to commit yourself to a sufficiently large market opportunity. 

Investor perspective: If you want to raise capital from VCs then you need to convince them that your company will generate an exit value that returns their fund. In your pitch you should aim to convey that your startup has the potential to capture at least hundreds of millions of dollars of high-margin revenue within the next decade, within a multiple billion dollar market. 


2. How to estimate market size?

Use the bottom-up approach to estimate market size. Multiply the number of customers by the average revenue per customer per year. 

There are top-down and bottom-up approaches to estimating market size. The bottom-up approach is more convincing because its uses assumptions that are substantiated through other aspects of your pitch, such as customer definition, revenue model, and GTM. VCs also prefer bottom-up because the underlying assumptions can be tested and validated. Ideally you will only use top-down to sanity check the magnitude of your bottom-up estimate.

Keep your method simple and easy to explain. The underlying assumptions for market sizing will remain rough until you have ascertained your exact target customers and revenue model, so avoid undue complexity. 




3. What is the exact definition of market size?

There is no single definition of market size. Early stage companies typically present three estimates for TAM / SAM / SOM. I encourage you to skip the acronyms and present bottom-up estimates for your total addressable market and your revenue in 5+ years. 

We surveyed 30 VCs to better understand how other investors evaluate a startup’s potential scale. We learned that earlier stage investors tend to prefer market sizing presented as TAM / SAM / SOM, whereas later stage investors care more about recent revenue growth as well as estimates for future revenue. 

  • Seed VCs generally value TAM estimates more because they have limited additional information to inform decisions. We heard from seed investors that: “TAM / SAM / SOM works fine as long as the methodology is clear.” 
  • Series A VCs told us that they care more about future revenue even when looking at seed stage startups: “A bottom-up build of future revenue is more useful than basing SOM on a hypothetical % share of TAM or SAM.”
  • Growth-stage VCs pay less attention to market sizing and we heard that: “TAM is a crude indicator. Revenue growth is a better signal. It’s hard to grow 200% at scale if there’s a small TAM.”


The textbook definitions for TAM / SAM / SOM are vague.

You might have seen TAM / SAM / SOM in an article or a pitch deck. If you Google these acronyms and read a few articles then you’ll likely find varying fuzzy explanations (one example depicted below). Pitch decks often include a market sizing illustrated as three bold numbers within three concentric circles, without context or assumptions. Such minimalism saves one from committing to definitions that we might be unsure about, but it also forfeits this opportunity to present a compelling narrative that convinces investors. 

Based on our survey of 30 VCs, it seems that many investors do not know the exact definitions either. So when presenting your market sizing, explicitly state your methodology rather than assume universal definitions.  (These survey results also had me wondering whether later stage VCs are more honest about their knowledge gaps! )




The textbook estimation approach for TAM / SAM / SOM is TOP DOWN ⚠️

Marketing textbooks generally describe the TAM / SAM / SOM approach as: first estimate your largest possible market for $ TAM, of which estimate a narrower proportion that fits your company for $ SAM, and then estimate the % market share you can reach to get to $ SOM. However, the % market share assumption is often an unsubstantiated afterthought, which results in a meaningless estimate for your company’s potential revenue. This approach encourages top-down thinking and is unconvincing. 

The most consistent feedback in our VC survey was to dissuade founders from presenting their future revenue based on % market share of a large addressable market. So, what’s a better approach?



Present bottom-up estimates for your total addressable market and for your revenue in 5 years.

The market sizing definitions stated below are based on consensus across surveyed VCs. However, interpretation still varies so state your assumptions. Also, enough with the acronyms — replace the jargon with more meaningful descriptors. 

Some of the surveyed VCs encouraged founders to drop the conceptual distinction between TAM and SAM. However, opinions on this topic were mixed. Instead of presenting both TAM and SAM, you could communicate your plan for incremental expansion across customer segments and products.

eg. Start with an initial wedge of selling product A into customer segment X. Then expand through cross-selling product B into the same customer segment X. Followed by expanding further through selling products A and B to a new customer segment Y.



4. When should market size be estimated for?

Estimate your market size for 5+ years into the future. 

Surveyed VCs most commonly wanted to see market size estimates for 5 years into the future. If your company is many years away from IPO and/or riding a longterm industry trend (eg. transition to electric vehicles) then it’s more appropriate to estimate 7-10 years into the future. 

A significant proportion of the VCs surveyed also prefer to see market size now as well as 5 years into the future. If you’re targeting a rapidly growing existing market then you can highlight that growth through presenting both now and in 5 years.



Guidance on the underlying assumptions

Now that you understand how to estimate your market size, you need to source the underlying assumptions to feed into the bottom-up equations. You can follow the following steps to arrive at assumptions for calculating total addressable market, initial addressable market, and future revenue.

1) # of Customers

1.1) Define your customers

Precisely focus on the customer segment that will contribute the majority of your potential revenue. 

eg. While DoorDash could claim that every person in the US could potentially order food online, a more convincing segment might be: “Americans in the age range of 25-40, who are employed, and eat out at least once per week.”

If level of demand and/or willingness to pay varies significantly across your customer-base then you should capture such variation in your estimation through customer segmentation. Though keep it simple and only consider segments that will contribute significant revenue. 

eg. Market sizing assumptions can differ significantly between SMB and Enterprise customers. If we’re selling a product into both then we should segment the market. (100K SMBs x 5 seats x $10K per seat = $5B) + (1K Enterprises x 100 seats x $20K per seat = $2B) = $7B total.

1.2) Estimate the total number of customers

The most relevant data sources vary by business category, eg.

  • Consumer: If you can define your target customer (or user) segment in terms of socio-demographics then use the US Census Bureau data on US Population.
  • Vertical-specific B2B: You can identify the number of relevant companies within an industry vertical using US Census Bureau data on US Companies.
  • Enterprise: If you your future revenue will be concentrated in big enterprise accounts then identify what proportion of companies within the Fortune100 or Fortune500 would buy for your product.

Additional example data sources:

  • Research reports (eg. Gartner, Forrester, McKinsey, BCG)
  • Government agencies (eg. Bureau of Labor Statistics, Bureau of Economic Analysis, US SBA, trade.gov, Bureau of Transportation, Dept of Housing, US Dept of Agriculture)
  • Industry bodies (eg. National Business Group on Health, National Restaurant Association, National Association of Realtors)
  • NGOs (eg. UN Dept of Economic & Social Affairs, UN World Tourism)
  • S-1s of companies in your category

1.3) Estimate the number of customers you could acquire in 5 years

You will acquire only a proportion of total customers We need to estimate a realistic number of customers captured by around the time you IPO, so 5-10 years from today. Its best to build this bottom-up through considering how many new customers you’ll be able to acquire (and retain) per year over the next 5-10 years.

Sense check your implied market share. Look at the market shares of existing dominant players in your category and in adjacent categories. Also consider strength of network effects, how entrenched established players are, and ease of distribution (eg. capturing 10% of the Fortune100 is more realistic than capturing 10% of 100,000 SMBs). 

Our expectations for high market shares are often anchored in the dominant consumer-facing FAANGs, yet many other categories are more fragmented or under-penetrated. Pitch decks often state a 10% potential market share. Yet when tech companies IPO they have typically only attained a 0.1% to 2% share of their addressable market. See the chart below based on data in S-1s of recent tech IPOs.


2) $ Avg Revenue per Customer per Year

2.1) Select your revenue model

Identify the most relevant revenue model for your business. You might foresee multiple revenue streams, however its best to keep it simple and focus the market sizing on your core revenue stream.  See the table below for examples (though this is not an exhaustive list).

Each revenue model is split into transaction volume multiplied by pricing.

2.2) Estimate Transaction Volume

This requires you to understand your target customer’s behavior. (eg. How many product will they consumer per year, How much data storage will they require, How many seats per company, etc.)

2.3) Estimate Pricing

Pricing is an artform worthy of volumes, and in practice you might chose to implement multi-tiered pricing differentiation to maximize your profits. But again, for estimating market size, minimize the complexity. 

There are three main approaches to pricing. Value-based pricing is generally the best approach. 

  • Value-based: Estimate how much value your customer attains from your product, and charge a proportion of that value. You can charge more through: (i) creating greater value for your customer; and/or (ii) heightening your customer’s perception of that value creation. Attribution is as important as the actual impact of your product. A rule as thumb is that you can charge in the range of 10-30% of the value you create for your customer.
  • Competitor-based: If your industry has pre-existing comparable products to yours then your customers are likely anchored in pre-existing price ranges. When pitching to VCs you might need to explain why your product has a premium or a discount to comparable products.
  • Cost-based: Estimate the cost to deliver your product and add a margin. Fixed costs are generally low in tech companies, and this is more relevant to operationally intensive and/or asset heavy companies.

How to best apply these approaches varies by the business category, eg.  

  • B2B SaaS: Value-based pricing is the optimal approach to determine your customer willingness to pay. (eg. If your products increase your customer’s profits by $1M per year on a perpetual basis then you can charge a $100-300K per year subscription fee.)
  • Consumer: Anchoring to the price of adjacent products often informs the attainable price, unless your brand is highly differentiated. This also applies to consumer fintech. (See this article)
  • Healthcare: B2B healthcare solutions sold to payers or employers usually have a fee per member per month revenue model, and you can charge a proportion of the cost savings you deliver. D2C healthcare solutions usually a fee for service or a monthly subscription revenue model, and ideally you identify relevant reimbursement CPT codes. Healthcare also includes several more complex pricing models. 
  • Marketplaces: The % take rate in a marketplace is driven by how much demand you can drive to your customer and how much easier you make it to run their business, as well as how competitive your market is. (See Lenny’s newsletter)

Now multiple those assumptions out. Hopefully you arrive at a big number for the addressable market. If not then revisit your assumptions as well as your overall vision. 


Additional Tips 

  • Think long-term and dream big. Most successful startups create or change markets, so you should estimate hypothetical demand for your product in 5-10 years, not just existing demand. Also consider how you will expand average revenue per customer through solving additional problems for your target customers.
  • Global market size is rarely relevant. If your strategy is US-focused then state an addressable market for the US only. If you’re starting in a smaller country and your strategy focuses on multiple similar countries then state an addressable market for those countries and be ready to explain your international go-top-market plan.
  • Ensure that the revenue you are presenting is annual. (eg. If there are 10M potential customers for your product and they might buy your product every two years on average, then you have 5M target customers per year. 
  • For marketplaces, include take rate in the market size calculation rather than presenting overall GMV. For financial products, include fees rather than presenting total transaction value or AUM. If your B2B startup is helping your customer to increase their profits, then you can only charge a proportion of that impact. 
  • Read S-1s of public companies with similar customers or products as your company. Search the SEC database for a public company of interest and open their ‘S-1 Prospectus’. S-1s are a goldmine of information and provide insights well beyond market sizing. 

About Pear VC

Pear partners with entrepreneurs from day zero to build category-defining companies. Our team has founded eight companies and invested early in startups now worth over $80B, including DoorDash, Gusto, Aurora Solar, Branch, and Guardant Health. We use this knowledge to provide founders with hands-on support in product, growth, recruiting, and fundraising. If you’re building a category defining company then reach out to our team.

The Data and Analytics Playbook for Startups


Ali Baghshomali, former data analyst manager at Bird, hosted a talk with Pear on data and analytics for early stage founders. We wanted to share the key takeaways with you. You can watch the full talk here

While a lot has been said around building go to market and engineering teams, there’s not much tactical coverage for analytics teams. Yet analytics is one of the most fundamental and crucial functions in a startup as it launches and scales. 

When should you start seriously working on analytics?
Why should you work on analytics?
Who should you hire?
What should be in your analytics stack?
What are some case studies of company analytics operations?
What should you do moving forward?


When should you start seriously working on analytics? 

You should start thinking about your analytics platform when your company is nearing product launch. After your product is live, you’ll receive an influx of data (or at least some data) from customers and prospects, so you want to be prepared with the proper analytics infrastructure and team to make the most of this data to drive business growth. 

If you are just starting out and would benefit from working with analytics but don’t have much in house, consider using third party data sources, like census data. 

Why should you work on analytics? 

If done well, analytics will pay back many, many times over in time, work, money, and other resources saved as well as powerful insights uncovered that drive meaningful business growth. 

Who should you hire? 

In conversation, people often use “data scientist” and “data analyst” interchangeably. While fine for casual conversation, you should clearly understand and convey the difference when writing job postings, doing job interviews, hiring team members, and managing data teams. 

Data scientists work with predictive models through leveraging machine learning. Data analysts, in contrast, build dashboards to better display your data, analyze existing data to draw insights (not predictions), and build new tables to better organize existing data. 

For example, at Spotify, data scientists build models that recommend which songs you should listen to or add to particular playlists. Data analysts analyze data to answer questions like how many people are using the radio feature? At what frequency? 

Similarly, at Netflix, data scientists build models that power the recommendation engine, which shows you a curated dashboard of movies and TV shows you may like as soon as you log in. Data analysts would conduct data analysis to determine how long people spend on the homepage before choosing a show. 

Unless your core product is machine learning driven, you should first hire data analysts, not data scientists. In general, a good rule of thumb is to have a 3:1 ratio of data analysts to data scientists (for companies whose products are not machine learning driven). 

For early stage startups, stick to the core titles of data scientists and data analysts rather than overly specialized ones like business intelligence engineers because you’ll want someone with more flexibility and who is open and able to do a wider range of work. 

What should be in your analytics stack? 

Here are examples of tools in each part of the analytics stack and how you should evaluate options: 

  • Database: examples include BigQuery and Redshift. Analytics databases are essentially a republica of your product database but solely for analytics. In this way, you can do analytics faster without messing up product performance. In general, it is advisable to use the same database service as your cloud service. 
  • Business intelligence: examples include Looker and Tableau. Business intelligence tools help you visualize your data. They connect to your analytics database. You should pick a provider based on pricing, engineering stack compatibility, and team familiarity. Don’t just default to the most well known option. Really consider your unique needs. 
  • Product intelligence: examples include Mixpanel and Amplitude. Product intelligence tools are focused on the product itself, rather than the over business. Specifically, they are focused on the user journey. They get code snippets inserted from the product. Because they don’t encapsulate the full code, you should consider this data to be an estimate and use the insights drawn more directionally. Product intelligence tools can be used to create charts, funnels, and retention analyses, and they don’t need to be connected to other databases. 

What are some case studies of company analytics operations? 

Helping Hands Community is a COVID inspired initiative that services high risk and food insecure individuals during the pandemic. 

  • Team: 7 engineers, no data analysts
  • Product: basic with 1000 users
  • Stack: Google Cloud, Firebase for product database, BigQuery for analytics, Google Data Studio for business intelligence, and Google Analytics for product intelligence 

Bird is a last mile electric scooter rental service. 

  • Team: 50+ engineers, 30 analysts, 8 scientists, 6 analyst managers
  • Stack: AWS for cloud, Postgres (AWS) for product database, PrestoDB for analytics, Tableau and Mode for business intelligence, Mixpanel for product, Google Analytics for website, Alation for data, DataBricks for ETL, and Anodot for anomaly detection (you generally need anomaly detection when ~1 hour downtime makes a meaningful difference in your business) 

What should you do moving forward? 

Make a data roadmap just like you make business and product roadmaps. Data roadmaps are equally as important and transformative for your startup. List the top 5 questions you foresee having at each important point along this roadmap. Structure your data roadmap in a way that your stack and team addresses each of the questions at the point at which they’re asked. 

We hope this article has been helpful in laying the foundations for your analytics function. Ali is available to answer further questions regarding your analytics strategy, and he is providing analytics and data science consulting. You can find and reach him on LinkedIn here

5 Guidelines for Introducing Product Management to Your Company

This is a recap of our discussion with Nikhyl Singhal, VP of Product at Facebook, former CPO at Credit Karma, and former Director of Product Management at Google. 

Watch the full talk at pear.vc/events and RSVP for the next!

Product management can be an elusive topic, especially as its definition changes as the company grows. Early on, product management is focused on helping the company get to product market fit. Once the company achieves it, product management can change dramatically depending on the type of product or service, the organizational structure, and the company’s priorities. We brought Nikhyl Singhal to demystify the product management process and share insights on when, how and why to add product management into your company.

Jump to a section:

In the “Drunken Walk” Phase, Product Managers Should Really Be Project Managers

For Founders Working on Product Market Fit, Maintain Healthy Naivete

If You’re Not a Product Person, Find a Co-Founder Who Can Own Product Market Fit

Introduce Product Management When Founders Shift Priorities

Look for Product Managers Who Can Scale with the Organization


In the “Drunken Walk” Phase, Product Managers Should Really Be Project Managers

While employees at early stage companies may have Product Manager as their title, they should really be owning project management and execution.

Product management, or the goal of helping that company get to product market fit, should be owned by the founders. 

It’s partially an incentive problem. Founders, as Singhal notes, are usually the executives with a larger share of ownership.

“They’re the ones that the investors have really placed money in and the extended team in some ways just aren’t at the same level in scale as the founders,” Singhal says.

However, execution and distribution are team responsibilities–and Singhal considers them much more of a utility than a strategic function. Understanding the allocation of responsibilities in founders versus product managers in early stage companies can be crucial to success.

“I actually embrace this and I [would] suggest, “Look, there’s no shame in saying that we need to bring in product managers to really own a lot of the execution.”

For Founders Working on Product Market Fit, Maintain Healthy Naivete

For early stage founders, Singhal says not to discount naivete. He recounts from his own experience that while others had insider perspectives or felt jaded, his own beliefs helped propel him through company building, ultimately helping him found three companies in online commerce, SAAS, and voice services. 

“I think that the lesson, if I were to pick one, is that healthy naivete is a critical element to finding product fit and actually fortitude around some of those ideas that are, ‘Hey, the world should work this way,’” Singhal reflects. “‘I don’t quite understand the industry, but I want to focus on that user, that business problem, and go forward on it.’”

If You’re Not a Product Person, Find a Co-Founder Who Can Own Product Market Fit

“The speed to be able to go through product fit is so essential for being able to efficiently get to the destination in the final course of action for the company,” Singhal says.

Thus, while it’s possible for founders to take other roles early on, purely outsourcing product fit to the rest of the team is still not a wise decision.

“If you’re not the person who’s owning product fit and you agree that product fit is sort of job number one, what I would say is—find a co-founder who can be essentially the owner of product fit. The reason why I use the term co-founder is for the economics to work.”

Introduce Product Management When Founders Shift Priorities

One issue founders often face with product management is determining when to introduce it. Introducing it too early may lead to conflicts internally, while introducing it too late means the company may have missed out on the prime time for strengthening execution. 

Again, product management is dependent on the founders’ backgrounds. For founders who have backgrounds in product, as long as there is clarity and predictability around what will happen, the company may proceed without product managers. The most common case for introducing product management, however, is when founder priorities need to shift from product fit to scaling the organization.

“This could be establishing new functions,” Singhal notes, “Or fundraising or thinking through acquisition. Marketing is also an important area, or thinking through company culture if the company starts to scale. At this point, if you fail to bring in product management, you’ll see dramatic reductions in efficiency.”

Look for Product Managers Who Can Scale with the Organization

For early product manager hires, companies should consider both the growth curve of the company and the growth point of the individual. Especially for companies that may be in hypergrowth, it’s important to have a mindset that “what’s gotten us here isn’t what gets us there.” This means the product management team must be adaptable. 

Being aware of how product management interacts with other functions is also crucial. 

“Product tends to essentially sit between the power functions of the organization as it deals with scale and growth,” Singhal says. It could be between marketing analytics and product engineering, or sales and product, depending on what the company’s business model is. 

Lastly, founders need to examine their own career trajectories in transitioning product power to teammates. It can be a tough emotional decision, Singhal acknowledges, but this question should be asked early on.

“I think that it’s almost a psychological question around: what is the person’s career ambition as a founder? Do they see themselves as moving into a traditional executive role? Shall I call it CEO or head of sales or head of product? If the goal of the person is to expand beyond product, then I think that the question really deserves quite a bit of weight,” Singhal says.

15 Mistakes Startups Make When Building Their First Engineering Teams

This is a recap of our discussion with Pedram Keyani, former Director of Engineering at Facebook and Uber, and our newest Visiting Partner. Keep an eye out for Pedram’s upcoming tactical guide diving deeper into these concepts.

Watch the full talk at pear.vc/speakers and RSVP for the next!

Mistake #1: Not Prioritizing Your Hires

The first mistake managers encounter in the hiring process is not prioritizing hires. Often, when faced with building a company’s first team, managers tend to hire for generalists. While this is a fine principle, managers must still identify what the most critical thing to be built first is.

“The biggest challenge that I see a lot of teams make is they don’t prioritize their hires, which means they’re not thinking about: what do they need to build? What is the most critical thing that they need to build?”

Mistake #2: Ignoring Hustle, Energy, and Optimism

People naturally prefer pedigreed engineers — engineers that have worked at a FAANG company, for example, or engineers that have built and shipped significant products. But for young companies that might not have established a reputation yet, they’re more likely to attract new college grads.

“They’re not going to know how to do some of the things that an engineer who’s been in the industry for a while will do, but oftentimes what they have is something that gets beaten out of people. They have this energy, they have this optimism. If you get a staff engineer that’s spent their entire career at—name-your-company—they know how to do things a particular way. And they’re more inclined to saying no to any new idea than they are to saying yes.”

So don’t worry too much about getting that senior staff engineer from Google. Often, bright-eyed, optimistic young engineers just out of school work well too. 

Mistake #3: Not Understanding Your Hiring Funnel

Managers must be aware of how their hiring funnels are laid out. No matter what size of company or what role, a hiring manager must treat recruiting like their job and be a willing partner to their recruiters.

Get involved as early as sourcing. 

“If they’re having a hard time, for example, getting people to respond back to their LinkedIn or their emails, help put in a teaser like, ‘Our director or VP of this would love to talk to you.’ If that person has some name recognition, you’re much more likely to get an initial response back. That can really fundamentally change the outcomes that you get.”

Mistake #4: Not Planning Interviews

Once a candidate gets past the resume screen to interviews, that process should be properly planned. Interviewing is both a time commitment from the candidate and from the company’s engineering team. Each part of the process must be intentional. 

For phone screens, a frequent mistake is having inexperienced engineers conduct them. 

“You want the people who are doing the phone screens to really be experienced and have good kinds of instincts around what makes a good engineer.”

For interviews, Pedram suggests teams have at least two different sessions on coding and at least one more session on culture. 

To train interviewers, a company can either have new interviewers shadow experienced interviewers or experienced interviewers reverse shadow new interviewers to make sure they’re asking the right questions and getting the right answers down.

Mistake #5: Lowering Your Standards

Early companies can encounter hiring crunches. At this time, hiring managers might decide to lower their standards in order to increase headcounts. However, this can be extremely dangerous. 

“You make this trade off, when you hire a B-level person for your company—that person forever is the highest bar that you’re going to be able to achieve at scale for hiring because B people know other B people and C people.”

What about the trade-off between shipping a product and hiring a less qualified teammate? Just kill the idea. 

“At the end of the day, these are people you’re going to be working with every day.”

Mistake #6: Ignoring Your Instincts

Failure #5 ties into Failure #6: Ignoring your instincts. If there’s a gut feeling that your candidate won’t be a good fit, you should trust it. 

“The worst thing you can do is fire someone early on because your team is going to be suffering from it. They’re going to have questions. They’re going to think, ‘Oh, are we doing layoffs? Am I going to be the next person?’” 

Mistake #7: Hiring Brilliant Jerks

During the hiring process, managers may also encounter “Brilliant Jerks.” These are the candidates that seem genius, but may be arrogant. They might not listen, they might become defensive when criticized, or they might be overbearing. 

The danger of hiring brilliant jerks is that they’ll often shut down others’ ideas, can become huge HR liabilities, and won’t be able to collaborate well within a team environment at all. 

So when hiring, one of the most important qualities to look out for is a sense that “this is someone that I could give feedback to, or I have a sense that I could give feedback to you.”

Mistake #8: Giving Titles Too Early

Startups tend to give titles early on. A startup might make their first engineering hire and call them CTO, but there are a lot of pitfalls that come with this.

“Make sure that you’re thoughtful about what your company is going to look like maybe a year or two year, five years from now. If you’re successful, your five person thing is going to be a 500,000 person company.” 

Can your CTO, who has managed a five person team effectively, now manage a 500,000 person team?  

Instead of crazy titles, provide paths to advancement instead. 

“Give people roles that let them stretch themselves, that let them exert responsibility and take on responsibility and let them earn those crazy titles over time.”

Mistake #9: Overselling The Good Stuff 

When a team’s already locked in their final candidates, young companies might be incentivized to oversell themselves to candidates—after all, it’s hard to compete against offers from FAANG these days. But transparency is always the best way to go. 

“You need to tell a realistic story about what your company is about. What are the challenges you’re facing? What are the good things? What are the bad things? Don’t catfish candidates. You may be the most compelling sales person in the world, and you can get them to sign your offer and join you, but if you’re completely off base about what the work environment is like a weekend, a month, and six months in, at some point, they’ll realize that you are completely bullshitting them.”

As Director of Engineering at Facebook, Pedram made sure to put this into practice. After mentioning the positives and perks of the job, he would follow up with “By the way, it’s very likely that on a Friday night at 9:00 PM, we’re going to have a crazy spam attack. We’re going to have some kind of a vulnerability come up. My team, we work harder than a lot of other teams. We work crazy hours. We work on the weekends, we work during holidays because that’s when shit hits the fan for us. I wouldn’t have it any other way, but it’s hard. So if you’re looking for a regular nine to five thing, this is not your team.” 

Make sure to set expectations for the candidate before they commit. 

Mistake #10: Focusing on the Financial Upside

Don’t sell a candidate on money as their primary motivation during this process. 

“If the key selling point you have to your potential candidate is that you’re going to make them a millionaire you’ve already lost.”

Instead, develop an environment and culture about a mission. Highlight that “if we create value for the world, we’ll get some of that back.”

Mistake #11: Getting Your Ratios Wrong

Companies want to make sure that they have the right ratio of engineering managers to engineers. Each company might define their ratios differently, but it’s important to always keep a ratio in mind and keep teams flexible.

Mistake #12: Not Worrying About Onboarding

Once a candidate signs on, the onboarding process must be smooth and well-planned. Every six months, Pedram would go through his company’s current onboarding process himself, pretending to be a new hire. This allowed him to iterate and make sure onboarding was always up to date. 

“It’s also a great opportunity for you to make sure that all of your documentation for getting engineers up to speed is living documentation as well.”

Mistake #13: Not Focusing on Culture

Culture should underscore every part of the hiring process. It can be hard to define, but here are some questions to start: 

  • How does your team work? 
  • How does your team solve problems? 
  • How does your team deal with ambiguity? 
  • How does your team resolve conflicts? 
  • How does your team think about transparency and openness? 

“Culture is something that everyone likes to talk about, but it really just boils down to those hard moments.”

Mistake #14: Never Reorganizing

Failure #14 and #15 really go hand in hand. As many companies grow, they may forget to reorganize. 

“You need to shuffle people around. Make sure you have the right blend of people on a particular team. You have the right experiences on a team.” 

Again, keep your ratios in mind.  

Mistake #15: Never Firing Anyone

Lastly, and possibly the hardest part of hiring, companies need to learn to let people go. 

“People have their sweet spot. Some people just don’t scale beyond a 20 person company. And, you know, keeping them around is not fair to them and not fair to your company.”

What It Takes To Go from 0 to 1: From 0.5 to 1 (Part IV)

This post is Part 4 of our four-part series on What It Takes To Go from 0 to 1.

Let’s assume you’re a SaaS company that’s now raised some seed money. You’ve raised from us and you have two customers, and the founder has done all the sales. 

Now your task is to prove two things:

  1. someone who is not you can sell your product
  2. you can optimize and scale that process 

This usually involves building a sales team or setting up a reliable marketing channel. The test for this stage is whether you have a reliable formula for your growth. That is, you should be able to confidently say, “if I do X, I will get Y new customers / users / revenue.”

Pure growth is not enough. You can be growing super fast, but if you have no retention, we know that growth will die. Again, you may be tempted to buy a bunch of ads right before your raise to spike your growth for two months, but smart investors know that that’s not real growth. 

You may be surprised to learn that pure revenue is also not enough. We have found that post-money valuation of series A companies and their monthly recurring revenue is not correlated at all:

If product love is the one thing that matters most to us at 0.5 stage, what is it for the 1 stage? That you’re going to be a “big company.” We are looking for predictors of success. You can think about it as the “second derivative” of your growth. 

In this stage, we want to make sure that people not only love your product, but that they love it enough to pay you enough money to make it profitable to grow. 

Even if you’re at only 200K in MRR, if we can see that you’ve gone from one person using your product to 20 people using your product, and those people are using you every day and they can’t live without you, you’re probably a great company, like Slack! They didn’t need to have 1 million in ARR for investors to know that people loved that product and that it was going to stick around.

Series A investors are going to look at your scale metrics, like LTV (lifetime value) to CAC (cost of customer acquisition).

To get to Series A from the seed stage (or from 0.5 to 1), the most important thing you need to do, as a seed founder, is to make a plan and measure your progress. Determine where you want to be in four quarters, then walk backwards and figure out what you need to achieve that. 

Look at everything you need to do. 

For example, if you want to be at $1 million ARR, with some amount of cash for six months at the end of Q4, you might determine that you need to hit $100K in revenue by Q2. You probably are also going to need enough leads by Q2. If this is a SaaS business, you may want to hire a salesperson for that. And if you have all these customers, you’ll likely want to support them and keep them happy, so you might have to hire a customer success person in Q3. To have something to sell in the first place to get to that Q2 revenue, you might need to have an MVP by the end of Q1 that requires hiring a certain amount of engineers.

You’ll need to do all this math to find out what that plan all costs and how much cash you need to have for it. It is likely you’ll need to iterate on this plan, which is why you also need to measure everything as you proceed. What gets measured gets done, and the bonus is that it’s easier to measure things at the seed stage! We love data driven CEO’s, and we even encourage founders to display their key metrics to everybody in their company. When you go out to raise our series A, you can just take your dashboard to the investors!

Once you have a plan, and you have your measurements, you’ll need to put those two together to figure out whether you’re on track or not. This sounds very simple, but we’ve had many founders suddenly call us with three months of cash left out of the blue. Force yourself to send reports to yourself, to your team and to your investors. When you do that, you’re actually committing to something, and that makes you true to the plan. 

Here at Pear, we make every one of our seed companies run through a planning exercise at the very beginning of our partnership with them, and we review the plan every quarter. When our founders are in trouble, we review it every week, so we can figure out what our goals are and what we need to hit. 

Final Stretch: Don’t Mess Up the Actual Fundraising

Fundraising is a little like selling a house. If you’re trying to sell a house, but you don’t have your inspections complete and you haven’t cleaned up the lawn, you’re probably going to get a lower final price than if you’d done all your work—even if you’ve got a great house!

Put another way: no matter how great your numbers look, you still need to have a great pitch. You have to actually communicate with data. You have to have a rational ask.

Remember, there are much fewer Series A funds out there than angels and seed funds, so the stakes are higher with each meeting, and it’s much slower and more complex. You can’t hand wave. You need to have concrete, quantitative answers, and investors are going to take several weeks to get back to you. It could take longer. The good ones do it fast, but it’s not 30 minutes. 

Also, think through your process. Don’t contact 20 series A investors at once with your initial pitch. Stagger your pitches so you can iterate and revise between each, and save your top choice investors for last, after you’ve gotten feedback from the others.

This brings us to our final piece of advice for this process: diligence who you work with! We’ve seen founders get desperate and take money from investors they shouldn’t. We’ve done that personally on our own entrepreneurial journeys. It’s absolutely painful. You have to know who you’re fundraising from.

If you’re considering us as partners on this long journey, we hope you’ll take the time to get to know us, just as much as we promise to take the time to get to know you. 

What It Takes To Go from 0 to 1: From 0 to 0.5 (Part III)

This post is Part 3 of our four-part series on What It Takes To Go from 0 to 1.

To review: at 0, you have a big idea. At 0.5, you have a product that you, as the founder, can sell, and you’re seeking seed stage financing. 

What we’re looking for here is product love. For us, all that matters is — do people absolutely love this product? Is there a group of people out there who can’t live without this product? 

Your product needs to be at least 10x better than anything out there, and ideally, you can show us that this group is willing to pay for it. To us, it doesn’t matter at this point exactly how much they’re willing to pay, and to some extent, even if they’re not willing, it could still be okay. We really want to see the love most of all.

Now, we do use some metrics and signals to try to determine that level of “love,” and it actually has nothing to do with how much revenue you have, nor acquisition.

Qualitatively, a good rule of thumb is that when a company is at 0.5 stage, they are no longer changing their website (or sales Powerpoint, or app) to acquire and retain a new customer. They’ve found a value proposition that works. 

For a SaaS company, we generally want to see some very happy customers. 

For a consumer company, it’s all about retention. 

If you’re building an app, for example, you might be tempted to put your app out there and get as many downloads as possible. Maybe you’ll think about buying ads to juice those download numbers. To us, none of it matters unless you’re retaining the user. From our perspective, buying ads to inflate your downloads is just throwing money down the drain. 

The top 10 apps, all of which are huge companies, retain their users at 60% after a year. The crappy ones retain under 10%. It goes to show that the hard part of a consumer app is to keep somebody using it for a long time. If you can do it, it’s a good sign that you’ve got that product love we’re looking for.

Another important signal we look at for consumer businesses is your retention of super fans—people who are dying for your product. A good example of this was Pinterest. Early on, Pinterest users were on the site all the time and they were obsessed. 

So, we’re not just looking at retention of the average user, but also retention of those super fans. We want to see how much those super fans love you.

To navigate this stage, you’ll need a rapid experimentation mentality, cycling through as many new hypotheses as possible to land on the right value proposition. Make your operations scrappy and fast. Build and kill features, get to the simplest MVP possible.

To sum: be fast and listen to the data. Do not fall for fake signals. Remember the only real thing that matters is: do people (truly) love your product?

What It Takes To Go from 0 to 1: Step 0 (Part II)

This post is Part 2 of our four part series on What It Takes To Go from 0 to 1.

Let’s say you’re at 0 and you’re thinking about starting a company. You’ve got an idea. 

The first thing you need to do—if you’re committed to going down the path of venture financing—is to figure out if you are in a big enough market for venture financing. 

If you’re not in a big enough market, and you go down the venture path, the money will unfortunately end up hurting you. Your company won’t scale with the capital, even though you might have built a great company if you hadn’t fundraised. 

There are a few ways to go about determining if you’re in a big enough market. One is a bottoms up analysis. But, as you may have heard, if you’re creating a new market, like Airbnb and Uber, you really won’t know how big the market is. These companies are typically the ones that turn out to be “unicorns.”

From what we have seen, the one thing that such companies all have in common is that the founder has great ambition. They don’t just want to make a quick exit or a lot of money. They want to fundamentally change something that they think is broken, or they just really want to make it big. 

There’s a fire in them, and it shows in their presentation. To be honest, there is a little, “We know it when we see it,” but we’ll try our best to describe what it looks like here.

The Difference Between a Good and Great Founder

So, let’s assume a company comes to us, and they’re pre-seed. Maybe they don’t have a product yet, and they want to sell lead gen software for real estate brokers. 

A bottoms up analysis would sound like this: “We’re going to be able to charge people $5 a month. There’s about 2 million real estate brokers in the US, so we’re going to hit 120 million in revenue in a given year if we sell to everybody.” 

We’ll know immediately that that’s a small company, because you have to sell to everybody, and we would need an 8x multiple to get to 1 billion. 

Of course, no smart founder would come and show us that. They would probably say something more like, “I’m going to charge $50.” Then the numbers get bigger, which looks nice, and then it’s our job to figure out if we believe that you’re actually going to be able to charge $50. We’ll ask questions around that. But at least now we know it’s a big market. 

The CEO’s of a potential “unicorn,” however, approach this differently. They say something more like, “Listen, real estate is broken and I’m going to build a marketplace where most transactions happen. Maybe I’m going to build a SaaS enabled marketplace and I’m going to sell some software, but fundamentally, I’m going to take a percentage of all the transactions in real estate.” 

We start thinking: “Hey, we want to back this person, they’re going to change real estate. They’re not just selling a tool to somebody.” 

We’ll admit this is very subtle, but it’s critical because it determines what type of company you can become. 

The point is, if you want to start this path, your ambitions need to be big.

When we see a founder with ambition of this scale, our job is to figure out if we believe that this person can attract the talented co-founders and early team members they’ll need to execute and actually achieve those big ambitions.

What It Takes To Go from 0 to 1: The Big Picture (Part I)

This post is Part 1 of our four-part series on What It Takes To Go from 0 to 1.

You may have heard that seed funds have grown significantly in the past decade, and that there are many more angel investors these days, plus incubators and accelerators. 

While this is exciting, what it also means for you is that getting to Series A is harder than ever. That’s because the same VC’s from more than 10 years ago are still the only ones offering Series A financing.

Take for example, a fund like Accel. The number of Series A companies that they fund has remained more or less constant. But now, there are more startups graduating from incubators and seed funds—so there is more competition for Series A funding than there was 10 years ago.

To put it into perspective: if you consider 2012 numbers, 631,817 new companies were started. Of those, only 4,671 received seed funding. Then, only 1,153 made it to Series A. That’s 0.18%. If you were a high school hockey player, you would have a higher chance of making it to the NHL. 

Moreover, the rounds themselves have shifted. The valuations of seed and Series A companies have (way) more than doubled in the last decade. This is because the companies that get funded are now older and farther along—meaning that the journey is also now longer.

Even with all this, we know that your goal as a founder is not merely to make it to Series A. You have ambitions to build a category-defining company. As you probably know, our world likes to refer to such companies as “unicorns,” defined as companies with valuations of at least $1 billion.

In 2012, there were only 22 unicorns—that’s 0.004%. Perspective again: that’s 10x harder than being a high school basketball player trying to make it to the NBA (where your odds are 0.03%).

The Startup Journey

Now, all these numbers are not to discourage you as a founder! We simply want you to know the reality of just how hard this journey is before you embark on it. We think you should know exactly what you’re getting into. 

All that said, we’re committed to helping more founders get there, and we want to share what, in our opinion, it takes to do it. 

As much as the funding landscape might have changed, the startup journey itself has not changed all that much in the last 10 years.

At 0, you are in the idea stage. We would call this the “pre-seed” stage.

At 0.5, you have a product that you, as the founder, can sell. We call this “seed.”

At 1, you have a valuable product that a team can predictively sell, without the founder.

What It Takes To Go from 0 to 1: A Four Part Series

This image has an empty alt attribute; its file name is slide-18-1024.jpg

At Pear, we consider ourselves 0 to 1 venture capital. We partner with entrepreneurs from day zero to build category defining companies. 

You might be thinking: so many other VC funds have said all that too, but then they keep telling us that we’re “too early” or asking for “traction.” 

So what do we really mean when we say day zero? What are we actually looking for? And what is this 0 to 1 you keep talking about? How do I actually get to 1? 

Taken from Mar’s much-loved talk, we think this philosophy will shed some much needed granular and specific light on all of these questions!  

You can also read the original deck here: pear.vc/landscape or watch Mar’s original talk here: youtube.com/watch?v=B0seWrrz3Dg.

The Series

PART 1 — The Big Picture
It’s hard to get financing when you’re in the 0 to 1 stage. Even harder to become a category-defining company. We think it’s important to really understand why that’s true. You should know exactly what you’re getting into and be prepared for the reality of how hard this journey is before you embark on it.

PART 2 — Step 0: Make sure you’re in a big enough market for venture financing
Did you know you might not even need venture capital? We’ll break down what “big enough market” means quantitatively and qualitatively, and how you can communicate that effectively to venture capitalists.

PART 3 — From 0 to 0.5: Show us product love
At 0, you have a big idea. At 0.5, you have a product that you can sell and you’re ready for seed stage financing. What we look for at this stage is product love. Believe it or not, there are quite specific signals for this that we’ll explain here.

PART 4 — From 0.5 to 1: Prove that you can systematically sell your product
You’re readying yourself for Series A now. Your task is to prove two things: (1) someone who is not you can sell your product (2) you can optimize and scale that process. The most important thing you need to do is make a plan and measure your progress. We’ll walk you through it.

Can You Describe—Exactly—the Problem You’re Solving?

This is an excerpt from partner Nils Bunger’s talk for Pear Accelerator S20.

Pear Accelerator is a small-batch program, where our partners and mentors work hands-on with exceptional founders through the journey to product-market fit. Learn more: pear.vc/pearx

The tricky thing about finding product-market fit is that it’s easy to be misled (and to convince yourself) by false signals. As Ajay mentions in his talk, the worst case scenario is when customers are lukewarm about your product. They will seem excited about it and talk about the features they want, but when push comes to shove, they won’t buy the product, no matter how many features you add at their request.

The hard truth is: that’s because they don’t actually need or love it. If this is happening to you, you haven’t found product-market fit.

Again, from Ajay’s talk, if your users love your product, they’ll tell you.

So how can you get to that point of love? How can you set yourself up for success and avoid getting caught in the feature spiral of death — where you’re building and shipping but no one is buying?

Customer development. Or, verifying your insight before building your MVP. This talk is about how you do that, step by step.

Jump to a section:

The Mindset

How to Form A True Problem Hypothesis

How to Win Your First Validation Meetings

How to Extract Real Insight From Your Meetings

Tying It All Together

OMG It’s Starting to Work!


The Mindset

“Fall in love with the problem, not the solution.”

— Uri Levine, co-founder of Waze

You first have to understand that customer development is not sales. You’re not selling your product yet. You’re not trying to convince potential customers that you have the correct solution.

Customer development is anthropology. You’re studying your customers. You’re trying to answer the question — do these people actually have a problem? How do they describe it? What would it take to solve it? You’re trying to probe and get real data to confirm whether your insight about a solution is correct.

And to do that, you need to deeply understand the problem your customers have.

Prepare to spend a lot of time here, going in a circle from hypothesis to validation back to hypothesis over and over again. It might feel frustrating, but it’s far better to be stuck in this loop, learning about your problem, rather than being stuck in the product feature loop where you’re wasting time, money, and energy building things that don’t work. You want to stay in the customer development loop until you achieve repeatable sales or clear cut metrics that say that you’re onto something.

Form A True Problem Hypothesis

A problem hypothesis has these basic parts:

  • Problem: A concise, specific statement of the problem you solve
  • Audience: Focused set of people who you think desperately have this problem
  • Reasoning: What makes this problem something that people in the audience need solved?

The problem is “the what.” The two key points for a well-articulated problem statement is that it is (1) specific to something that is solvable and (2) in your customer’s language. If you don’t have either of these, you don’t actually know what the problem is.

For the audience, the most important thing is to be narrow and tight — who has the problem most acutely? Keep narrowing down your ideas until you have defined a concentrated pool of users with the most acute need.

For any amateur chefs out there, you can think of this like a reduction sauce — start with a big pot of some kind of juice and stir over the stove for many hours, slowly evaporating all the water and slowly concentrating the flavor of that juice. What’s left behind is the deep essence of the ingredients.

You want to be finding your group of customers with the deepest, most desperate need for a solution.

Finally, you need to double check yourself and make sure you have sound reasoning for your hypothesis. Do you know why your audience wants or needs that problem solved? Again, specificity is the key here.

If you don’t have a good idea of why people might need a problem solved (perhaps your reasoning is a bit circular — “this customer has this pain and they want it solved, because it’s painful”), it might not really be a problem in the first place. There are plenty of problems people have that they’re okay with tolerating, because solving them is more effort than it’s worth. Or, you’ve just made up a problem that doesn’t exist.

You’re looking for an urgent problem, or if you’re on the consumer side, you’re looking for people who are just itching for something new.

Win Your First Validation Meetings

Once you’ve got a solid hypothesis, you have to validate it. That means you need to collect data from unbiased people who don’t know you, which means you will need to lean into cold outreach.

While customer development isn’t sales, during this phase, you will need to put on your shameless salesperson hat a bit to get the meetings you need.

There’s no single answer to how you should reach out. This is actually a core part of your learning during this whole process. You will need to know the unique answer for your company: how do you reach the right people and what are you saying that activates them?

Your approach should have three key things:

  • A high response rate
  • Consists of your target audience
  • Generates a steady volume of meetings

Then, it’s really a numbers game. Aim for 10 first meetings per week, and then aim to keep increasing that number with iteration. Iterate your message. Iterate your audience. Keep reaching out.

You’re learning about your customers here and you’re also learning about your message, where your customers hang out, and what they respond to. These insights are just as valuable as the meetings themselves.

Extract Real Insight From Your Meetings

Alright! You finally get to talk to customers! But what do you say? How do you get good data from them? Nils offers this simple section structure:

  • First 10 minutes: Broad questions to learn the unexpected.
  • Middle 10 minutes: Specific questions. Learn about your problem statement.
  • Last 10 minutes: Reconcile and zoom out. Did what you hear in parts 1 and 2 match up? Why or why not?

Broad Questions Phase

During the broad questions phase, your goal is to learn context about the user and the general area of your company. Find out about the incentives and biggest problems on their mind before being influenced by your ideas.

Some example questions:

  • What do you do here?
  • Who else do you work with most closely?
  • How do you spend most of your time?
  • What’s the most important thing for you to accomplish?
  • What’s the biggest challenge you have in your job now?
  • What are the top 3 problems you face?
  • Have you bought products to help some of these problems?

Do not tell them about your product idea, or what problem you’re after in this phase. Just try to understand how your prospect thinks about their day and what they need to accomplish.

During this phase, because your questions are broad, you’ll likely get some broad answers in response that won’t be very actionable for you. Make sure to spend some time drilling down into these answers. Pick one of the problems they talk about that seem relevant to you and ask for more details, peel back the onion a bit.

Specific Questions Phase

Your goal in this phase is to gain data points specifically about your problem hypothesis. Does this person have the problem you came up with? How badly do they have that problem?

Some example questions, and what you’re really after in asking them:

  • Have you ever had <your problem hypothesis>? Tell me about that.
  • How do you deal with it now?
    → Why You’re Asking: If this is a real problem, they’re probably doing something to deal with it already. And if they’re not, it’ll be useful for you to find out why.
  • How painful is this problem for you? How often do you have it?
    → Why You’re Asking: If this doesn’t come up that often, then it’ll probably not be a priority for them to buy or find a product about it.
  • Have you ever looked for a solution to this problem? Have you bought anything?
    → Why You’re Asking: If they have this supposed problem but then haven’t thought about it enough to try doing a simple Google search for solutions, maybe they just don’t care about it all that much.
  • Would <direct competitor> solve your problem? Why haven’t you bought it?
    → Why You’re Asking: Don’t be scared of this one. Remember, you’re not trying to sell a product, and in any case, you will have to assume in the future that your customer is going to know about your competitors—so you might as well ask them about it for your own competitive intelligence.
  • If you could solve this problem, what would change?
    → Why You’re Asking: Why does solving this problem really matter to this customer? Why does it make their life 10x better to not have to deal with it? This is what your product is going to need to solve for.
  • How valuable is that change?
    → Why You’re Asking: Value is much better than talking about pricing. You’re now trying to get as close as you can to a quantified version of the answer to the previous question. Does it cost them a lot of time? Money? Does it allow them to save on not hiring extra people?

Reconcile and Zoom Out

Now you want to reconcile what the customer has said in both the broad and specific phases to double check that your data is valid. If your customer answered your specific questions in a way that makes it seem like you’re onto something, but didn’t bring the problem up on their own in the broad phase, you’ll really want to dig in and understand why. Maybe the pain isn’t as intense as you (or they) think it is, or maybe they think of the problem you came up with more as a piece towards solving those larger problems.

Finally, you’ll want to zoom out and understand how your customer buys products or solutions to their problems. Ask:

  • What would be the next step if you had a product that solves this problem today?
  • Do you actually buy these kinds of products?
  • If you tried such a product, what would you want to see to keep using it?

You want to try to make this part as concrete as possible for the customer, almost as if it really does exist. Walk through their new customer journey with this product in their life. You’re looking for that “WOW” moment, as described in Bob Tinker’s talk.

Tying It All Together

Do at least 5 user interviews with the same type of audience you outlined. If you’re not finding an acute, concentrated pain, go back to your problem hypothesis and revise either your audience, or your problem, and run through it all again.

If you are starting to find an acute pain, do 5 more user interviews and drill down to the next level of questions. Show some product mockups and see if the pattern you’ve been seeing holds up.

Ask about discrepancies between your interviews. For example, if the previous four of the five previous customers said something was extremely important, but your next five don’t mention it, just ask them about it — “These other three guys had this big issue around XYZ and that didn’t seem to come up here. Is there a reason? I’m curious about the differences between what you do and what they do.”

OMG, it’s starting to work!

The strangest thing happens when this process starts working: you’ll find yourself trying to have a customer development “anthropology” conversation, and your customer is trying to turn it into a sales conversation.

People start leading you, instead of you leading them. They want to buy this thing now, they want to know how they can try it out, they want to bring their colleagues to a meeting. It’s the WOW moment. If you can get it repeatedly with 3–5 customers, then it may be time to develop your MVP!

Two options for this phase:

  1. Create a “no-code” solution with phone / email / spreadsheets / whatever, and try selling the solution to a few.
  2. Try turning 3 of your most promising interviews into “design partners” to help co-create a solution. Try to get some written agreement in place.

In any case, we always recommend building the tiniest MVP you can, enough to go through a “build/measure/learn” loop, per Eric Ries.

We wish you luck and hope that all of you get to this magic moment! It’s our favorite part of the journey.