The Marketplace Startup Dilemma: How Startups Overcome the Chicken-and-Egg Problem. | VC & Startup Jobs.
Why LTV/CAC Isn't Great for New Startups ? & Framework to Choose Right North Star Metrics.
👋 Hey Sahil here! Welcome to this bi-weekly venture curator newsletter—where we dive into the world of startups, growth, product building, and venture capital. In today’s newsletter -
Deep Dive: The Marketplace Startup Dilemma: How Startups Overcome the Chicken-and-Egg Problem.
Quick Dive:
How To Choose the Right North Star Metrics for Your Startup?
Why LTV/CAC Isn't Great for New Startups (And What to Use Instead)?
Y-Combinator: These are signs of Fake Product Market Fit.
Major News: ElevenLabs launches its speech-to-text model, Bybit offers $140M bounty to track down stolen $1.4B in Crypto, Amazon unveils Alexa+ & More.
20+ VC & Startups job opportunities.
📬 VENTURE CURATORS’ FINDING
My favourite finds of the week.
Peter Walker on the percentage of the company sold by funding in 2024.
How to prove to investors that this is the right time and you're the right company?
Dickie Bush on how to tighten feedback loops.
Andreessen Horowitz breaks down the AI voice agent market - what’s real, what’s just hype, and where it’s all heading.
8 unique paths founders take to reach Product-Market Fit.
Fundraising Simplified: What to Show Investors at Each Stage.
Big changes in AI to watch for over the next 12 months.
A bad habit of early-stage startups.
2700+ US angel investors & VC firms contact database (Email + LinkedIn Link).
Dealroom’s latest report dives into the early trends shaping AI investments worldwide.
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📜 TODAY’S DEEP DIVE
The Marketplace Startup Dilemma: How Startups Overcome the Chicken-and-Egg Problem.
When you are launching a two-sided platform, you need supply and demand. And it creates a big problem for founders, putting a high barrier to entry. You need to find not just users, but two different types of users (which is x2 times harder).
Users on one side of the business model find the platform useful only if the other side also exists.
For example, people buy video game consoles only if there are games they can play. Game developers make games for a console only if there are enough people who use it.
A lot of founders have this question- How to solve The Chicken and Egg problem? So We have reviewed several very popular marketplaces, that everybody knows to show how they successfully addressed this issue.
Tinder. 15,000 first users
Everybody heard about Tinder. Today (in 2024) Tinder facilitates over 13 million matches daily. But how it was in the first days of service? How did they overcome the nightmare of all marketplaces? Here is an interesting story behind it.
Whitney Wolfe, Co-Founder of Tinder, packed her suitcase and set off to travel around the country.
In each city, she made a presentation to the women's communities, motivating them to register in the app, and explaining how it's cool and useful.
Then she gave presentations to male communities, showing the app that is full of those local girls, who already registered.
When Whitney returned from a trip, 15,000 first users already used the application.
“Her pitch was pretty genius. She would go to chapters of her sorority, do her presentation, and have all the girls at the meetings install the app. Then she’d go to the corresponding brother fraternity—they’d open the app and see all these cute girls they knew...”
In 2014 Whitney left Tinder and founded Bumble (the biggest competitor of Tinder).
Dating? This girl knows how to make money on it.
Airbnb. 6,000 first users
Today Airbnb is a huge company with a $95B valuation. But in 2008 they were just starting with an idea of air beds and breakfasts for strangers. And like all marketplaces, they faced The Chicken and Egg problem:
Landlords are not interested in adding their apartments to the service without visitors.
Tenants have nothing to rent on the platform.
How did Airbnb get out of the situation? (it’s already a legendary example)
They created a script that scanned all landlord’s offers on Craigslist and collected their emails.
Then the same script sent a letter on behalf of a pretty young girl who "liked the apartment so much" and asked the owner to add his apartment to Airbnb.
So Airbnb quickly acquired 60,000 first landlords, which solved The Chicken and Egg problem.
What’s more, they made it easy for users to post listings on Craigslist through Airbnb with one click.
Later Craigslist closed this backdoor for collecting emails, but the main idea in the approach. If you need first users, you can simply copy them from somewhere. Besides, such holes appear regularly at many services. You just need to check such opportunities.
Uber
Uber’s story began in Paris in 2008. Two friends, Travis Kalanick and Garrett Camp, were attending the LeWeb, an annual tech conference the Economist describes as “where revolutionaries gather to plot the future".
Rumour has it that the concept for Uber was born one winter night during the conference when the pair was unable to get a cab. Initially, the idea was for a timeshare limo service that could be ordered via an app. After the conference, the entrepreneurs went their separate ways, but when Camp returned to San Francisco, he continued to be fixated on the idea and bought the domain name UberCab.com.
To get their first drivers, Travis cold-called black car drivers and offered to pay them an hourly rate while they tried out the platform. 3 of the 10 drivers that he called, agreed to give it a try.
Then, to incentivize passengers, they offered free rides at local events in the tech-savvy San Francisco community and worked hard to make each experience the best it could be. According to Kalanick, word of mouth was the biggest driver of sales and Uber spent almost nothing on traditional marketing.
“I’m talking old school word of mouth, you know at the water cooler in the office, at a restaurant when you’re paying the bill, at a party with friends — ‘Who’s Ubering home?’ 95% of all our riders have heard about Uber from other Uber riders.”
— Travis Kalanick
“When someone sees the ease of use, the fact that they press a button on their phone and in under 5 minutes a car appears, they inevitably become a brand advocate.”
Entering new markets.
Since Uber’s main competition was taxicab companies, the startup researched which cities had the biggest discrepancy between supply and demand for taxis. They then launched during times when that demand was likely to be the highest, for example during the holidays when people tend to stay out late partying. It also ran promotions during large concerts or sporting events, when big crowds of people all needed cabs at the same time, and an individual might be more likely to take a chance on an unfamiliar company named Uber.
In that way, the company acquired a large group of customers in one swoop. “First, they figured out how to get a bunch of customers all in one night, when the demand was high. Then, they made sure this first group of users had a great experience and brought in the next wave of customers via word-of-mouth,” says Teixeira. The company banked on the fact that once users realized how easy it was, it was only a matter of time before they started using it to go to work, then shopping for groceries, and so on.
Launching in situations of high demand and low supply also helps startups acquire the right type of customers—those early adopters who might be more forgiving of a company while it works out the kinks.
But what is the secret?
Besides the fact that you need first users (and you see that startups use different hacks how to get these users on the board, creativity should help you overcome this problem too), you have to satisfy these users with your service.
It can be quality, speed, usability, great support, etc. A long list of possible options. But the main idea - make your customers really happy.
Some founders say that getting your idea off the ground “it's a numbers game”. But it’s not. It’s a way how you treat your first customers. Do they stay happy by sharing your link with all their friends, or writing a 1-star review in App Store?
Paul Graham, Co-Founder of Y Combinator, wrote a famous essay
“Do things that don’t scale” where he described all the main things for early-stage startups. He said:
“All you need from a launch is some initial core of users. How well you’re doing a few months later will depend more on how happy you made those users than how many there were of them.”
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📃 QUICK DIVES
1. How To Choose the Right North Star Metrics for Your Startup?
“Your North Star Metric is your strategy, and your strategy is your North Star Metric.” Many first-time founders make mistakes in choosing the right metrics for their startups. Even some don’t have a clear idea about it and picked the wrong North Star metrics. Remember with your north star metrics, investors predict how you are thinking about the future. So it’s important to choose the right one.
It’s a single metric which can align the entire company around an overarching mission whilst simultaneously reflecting the value being delivered to customers.
Look at the mission statements of two of the world’s best-known companies, Google and LinkedIn:
Google – “Google’s mission is to organise the world’s information and make it universally accessible and useful”
LinkedIn – “Connect the world’s professionals to make them more productive and successful.”
The goal of the North Star Metric is to quantify this mission and express it in a way that can be measured and tracked. It is often used as a guiding light for the entire company to rally around – a sort of special key performance indicator (KPI).
What is a good North Star Metric?
Which metric, if it were to increase today would accelerate business flywheel? - Lenny Rachitsky
Here are some best practices for deciding on a good North Star metric:
Is it measurable?
It is simple, memorable and easily understood?
Is it a leading (not lagging) indicator of success?
Does it help your customers reach their end goal?
Does it apply to all customers and does it add value to all customers?
What is a bad North Star Metric?
There are also some poor metrics to use as a north star, the most common of which is revenue. Below are some characteristics you should aim to avoid when deciding on a north star metric:
Lagging indicators that show past performance, not future potential.
Metrics are hard for employees to influence or understand their impact on.
Metrics do not reflect customer value delivery, like vanity metrics.
Generic metrics that don't represent your unique business strategy.
Your north star should be unique to your business (or at the least your business niche) and should be an indicator of the problem you solve for customers, and the value provided to them.
Below we have provided examples of North Star metrics from many well-known brands. Notice how they are not all perfect, but generally, all reflect value delivered to the market.
So while choosing metrics for your startup remember - “North Star Metrics - if this metric grows, all other metrics will be growing too. Such as engagement, acquisition, and sales.” - Adam Wright.
2. Why LTV/CAC Isn't Great for New Startups (And What to Use Instead)?
Jeff Chang shared an interesting article where he talks about why LTV/CAC is not a good metric for new startups. I found it very interesting, so I'm sharing a summary and my thoughts on this.
LTV/CAC (Lifetime Value divided by Customer Acquisition Cost) is a popular way to measure if your advertising money is well spent.
“LTV/CAC, or Customer Lifetime Value to Customer Acquisition Cost, is a metric that measures how effective a business's marketing efforts are and how profitable it will be in the long term.
It's calculated by dividing the customer lifetime value (LTV) by the customer acquisition cost (CAC).”
While big companies use it successfully, it's not very helpful for startups. Here's why:
Main Problems with LTV/CAC for Startups:
Problem 1
Startup Survival Reality Most companies assume customers will stick around for 3-10 years when calculating LTV. But here's the truth: many startups don't even last that long. When your startup might not survive for several years, those long-term customer value calculations become meaningless.
Problem 2:
Customer Acquisition Changes Over Time
Early success can be misleading
Example: You might spend $1 to make $3 with your first ads
But when you try to scale up (like spending $1 million), the profits don't scale the same way
Why? Because:
Early customers are usually easier to get
Ad platforms find cheaper customers first
Target audiences get saturated
Problem 3:
The Data Problem New startups face a critical issue: they simply don't have enough data. Without years of customer history, it's impossible to make accurate predictions about:
How long customers will stay
How much they'll spend over time
Whether current customers behave like future ones
How product changes affect customer behaviour
Problem 4:
Resource Drain Getting accurate LTV/CAC numbers isn't simple. It requires:
Hiring data analysts or scientists
Breaking down different customer types
Creating complex prediction models
Constant updates and monitoring
A Better Solution: Focus on the Payback Period
What is the Payback Period? - It's simply how quickly you get back the money you spent on ads through revenue. This works better for startups because it's more immediate and practical.
How to Use Payback Period:
Good Target: Get your money back in less than 6 months
Best Case: Make back your ad money immediately or very quickly
Simple Way to Judge Success:
Good Example: Spend $1,000 on ads → Make $1,000 in the first month
Bad Example: Spend $1,000 on ads → Make $500 first month, $300 second month, etc.
Rule of Thumb: If it's not clearly profitable right away, it might not be worth your time
Don't get caught up in complex metrics just because big companies use them. For startups, the rule is simple: if your ads aren't making money quickly (ideally within 6 months or less), focus your energy elsewhere. You want advertising that pays for itself fast, not promises of long-term returns that might never materialize.
3. Y-Combinator: These are signs of Fake Product Market Fit.
Nowadays - It's pretty common for companies to claim they've nailed product-market fit when, in reality, they're quite far from it. I'd argue this is a major cause of downfall, especially for post-seed companies.
Many founders often face a dilemma where they believe that they have achieved product-market fit based on some factor, but honestly, it’s just a "Fake Product-Market Fit." What are those factors?
Firstly, some founders believe they've hit the jackpot when they secure funding from impressive individuals or renowned funds. The thought is that if these influential people or funds chose their company, it must mean they have product-market fit. This belief is surprisingly widespread.
Secondly, there's a trend where companies manage to raise substantial amounts, even before having a product that people truly love. Interestingly, instead of focusing on users and improving the product, founders often shift their focus to building the company, which is usually not the best move.
Thirdly, there's this phenomenon we call "magical thinking". It involves ignoring obvious facts or not bothering to measure crucial metrics that would reveal whether product-market fit exists. For instance, not understanding churn or the payback period after acquiring a customer. If you're not aware of these numbers, it's easy to convince yourself that you've achieved product-market fit when you haven't.
Lastly, there's a tendency for people to delude themselves into thinking they've achieved product-market fit simply because they don't want to face the idea that they might need better engineering or product improvement. Admitting that improving the product is challenging, they find it easier to just believe their product is already good.
Most of the founders fell into this trap of ‘Fake Product Market Fit’. Not only this - one of the most common misconceptions is that
“Product-Market Fit Means Only You've To Build A Product That Your Users Want.
In reality - Product-market fit typically feels like your product is gaining traction with profitable usage.
”your product is attracting users, and word of mouth or advertising channels are working well. Users love your product, and they're sticking around.
However, there's a catch.
Parts of your product that you didn't build to scale are starting to break. It could be software components or operational aspects, but something is faltering because it wasn't designed for this level of success.”
You might be thinking what’s this profitable usage about?
“It means these users are the ones you want, and economically, they make sense. You're not spending a lot for a user who only brings in a fraction of that cost. There's no crazy three-year payback period or anything like that.
So, for genuine product-market fit, we need both of these components:
a product that's breaking in a good way due to increased usage
users who align with the economics you desire.
To achieve genuine product-market fit, founders need to determine if they have a real connection between their product and the market, instead of a fake one.
THIS WEEK’S NEWS RECAP
🗞️ Major News In Tech, VC & Startup Funding
Perplexity launches a $50M seed and pre-seed VC fund, managed by F7 Ventures co-founders Kelly Graziadei and Joanna Lee Shevelenko. (Read Here)
Perplexity, known for its natural-language search engine, is launching a new web browser called Comet, entering a market long dominated by Google's offerings. (Read Here)
Apple shareholders voted down a proposal from a conservative group seeking to end the company’s DEI initiatives, aligning with Apple's board recommendation. (Read Here)
Chegg has filed a federal lawsuit against Google, alleging that Google's "AI Overviews" harm Chegg's website traffic and revenue by using its content without permission. (Read Here)
Amazon’s new AI-powered assistant, Alexa+, aims to handle everyday tasks like booking appointments and ordering groceries using a more advanced, agentic AI model. (Read Here)
Bybit is offering a $140M bounty, rewarding anyone who helps trace and freeze the $1.4B in Ethereum stolen in last week’s record-breaking hack. (Read Here)
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Good stuff, liked the not PMF part, pretty interesting.
just out of curiosity, what do you thing is a better NSM for uber, since rides per week is still lagging indicatir.