👋Hey Sahil here! Welcome to this bi-weekly venture curator newsletter. Each week, I tackle questions about building products, startups, growth, and venture capital! In today’s newsletter, we dive into -
Deep Dive: How To Measure If Your Startup Has Network Effects.
Quick Dive:
STARS Framework To Evaluate Feature Engagement & Satisfaction In SaaS Products.
Airbnb’s 11-Star Experience Framework.
Should I Fire Myself As A CEO? From Former CEO Of CB Insight.
The definitive list of things startups did that didn’t scale.
Major News: General Catalyst Acquires Indian VC Firm, Former Open AI Scientist Sutskever Launches New AI Company, Anthropic Releases ‘Most Intelligent’ AI Model & Softbank's $150 Billion Blunder.
Best Tweet Of This Week On Startups, VC & AI.
VC Jobs & Internships: From Scout to Partner.
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TODAY’S DEEP DIVE
How To Measure If Your Startup Has Network Effects.
Twitter. Amazon. Apple. Meta. Uber. Salesforce. Tesla. These are some of the most impactful and significant companies in the world.
Each one is very different in a lot of ways, but there’s a single property that defines them all and lies behind their success: Network effects.
All these big companies used network effects as a moat to build long-term sustainable businesses. If you look at the study by NFX you will find that - “network effects are responsible for 70% of the value created by tech companies” since the Internet became a thing in 1994. Even though they are only a minority of companies, companies with network effects end up creating the lion’s share of the value. If you are wondering what the network effect means -
Network effects are phenomena by which the value of a product or service increases when the number of people who use that product or service increases.
Example: We use WhatsApp because the majority of the people in our network use it and vice versa. As value for each member increases as more members joins.
One of the biggest misconceptions around network effects is confusing growth with engagement and it’s often spoken in a binary way: either you have them, or you don’t. In practice, most companies’ network effects are much more complex, falling along a spectrum of different types and strengths. They’re also dynamic and evolve as products, users, and competition change.
For founders, it’s important to understand the nature of your company’s network effects — including deciding on the set of metrics that help you understand what’s working or not. So in this writeup, we are sharing 4 main categories to measure whether your startup has a network effect: acquisition, competitors, engagement, and economics-related metrics.
Acquisition-Related Metrics
Organic vs. paid users: What percentage of your new users are organic?
In network-effect businesses, the proportion of organic users should increase over time as the network becomes more valuable, encouraging users to join independently. This applies to both direct-side networks (e.g., Facebook) and two-sided marketplaces (e.g., Airbnb, eBay).
While paid acquisition remains important, especially in new markets, sustainable growth leads to reduced reliance on it as the network reaches critical mass. The key is to become less dependent on paid acquisition as the network's value grows.
Source Of Traffic:
As networks grow, they should become destinations, generating more internal traffic and transactions. Tracking the ratio of direct vs. external traffic helps measure this growth in value.
Initial flow: External research > Restaurant website > OpenTable widget
Mature network: Direct to OpenTable for research and booking
This shift indicates users find more value in the platform itself.
Medium shows a similar pattern, with increased read time originating within the site as content and user base grew.
Both illustrate the "come for the tool, stay for the network" principle, where initial utility evolves into a comprehensive ecosystem that users prefer to start and stay within.
Time series of paid CAC: How much do you need to spend to acquire supply?
While network effects should theoretically reduce customer acquisition costs (CAC) over time, real-world factors like marketing channel competitiveness and substitute availability can influence this trend. For example, ridesharing platforms face increasing driver acquisition costs due to substitutes, while OpenTable saw decreasing restaurant acquisition costs as it consolidated demand.
It's important to distinguish between network effects and virality. Network effects increase product value with each new user, while virality refers to user-driven growth. Though often present in network-affected businesses, virality can exist independently and doesn't guarantee sustained growth without underlying network effects.
So, companies that are great at viral growth but don’t have network effects can grow quickly but flame out just as fast.
Competitor-Related Metrics
Prevalence of multi-tenanting: How many of your users also use other similar services? How many users are active on similar services?
Understanding user multi-tenanting is crucial for network effect businesses. Users may utilize similar or adjacent services, which can impact your product's usage and margins. Competitors might replicate your network and add features that reduce the need for your product.
Measuring multi-tenanting can involve user polls, analyzing churn patterns, or directly searching for user profiles on other platforms. Once identified, companies can implement strategies like subscriptions or bonuses to improve retention and reduce competitor usage.
It's also important to assess the activity level of multi-tenanting users. A user might have a profile on a competing platform but not be active, presenting an opportunity for your service to better meet their needs. This "Trojan Horse" strategy allows new networks to be built from underserved market segments.
Switching or multi-homing costs: How easily can users switch to or use multiple networks, and what value do they gain?
The ease of switching between competing networks depends on the friction involved in the onboarding process. Products with high upfront investment required from users, such as personal styling services, create a moat against competitors as users are less likely to multi-tenant. Conversely, products with lower activation energy, like Uber Eats, can more easily attract users from competing networks.
The value users can get from the start of a new network is also a key factor - networks with strong user data and social graphs, like Facebook, have higher switching costs compared to job listing marketplaces where employers can easily post openings on multiple sites.
So Potential metrics could be the time required to complete a competitor’s onboarding flow; the ease of getting to the minimum threshold or “magic number” for a product to be useful (e.g. 10 friends for Facebook); and so on.
Engagement-Related Metrics
User Retention Cohorts
Newer user cohorts should have better retention than older cohorts if a product's value increases with more users (network effects). However, this is often not the case as early adopters tend to be the most engaged users. Other factors like competitors, hyperlocal network effects, or negative network effects can also impact cohort retention.
Core Action Retention Cohorts
Retention based on users taking a core action (e.g. posting content on Nextdoor) is more indicative of network effects than just measuring logins or app opens. Newer cohorts should have improving core action retention as the network density grows.
Dollar Retention & Paid User Retention Cohorts
Subscription and paid products should see increasing dollar retention and paid user retention among newer cohorts, as paying users value the product more. A product with network effects should become more valuable over time.
Retention by Location/Geography
For businesses with local network effects, the oldest markets tend to have better retention than newer markets. As each geography matures and builds network density, retention improves in those markets.
Power User Curves
Analyzing power user curves (L7 & L30 charts) shows if users are becoming more engaged over time. If a product is gaining utility with more users, there should be a growing share of users shifting to higher-frequency engagement buckets or a more right-leaning power user curve.
Economics-Related Metrics
Pricing power: How much are you able to charge for your product? What would your customers be willing to pay to stay on the network?
As participants receive greater value from the network, they are willing to pay more to have access to the network, in the form of subscriptions, listing fees, take rates, or other monetization mechanisms. Over the lifetime of a network effects business, the business can evolve from not being monetized at all, or potentially even subsidizing demand or supply; to turning on monetization; to having the ability to increase prices with minimal churn on either side.
Unit economics: How is the business doing?
Improved network effects often appear in improved unit economics over time. This is a result of declining incentives that businesses need to offer to different sides of the market, a lower share of paid users, and an overall improvement in pricing power. For businesses with local network effects, the impact of network effects should show up in unit economics over time, on a market-by-market basis.
This is because in a given market, CAC should decrease and the organic share of users should grow over time. For businesses like Thumbtack or Instacart, which have network effects at the local level, tracking the unit economics over time per market is helpful because you’ll see the relationship between market age, network density, and profitability.
That’s it.
QUICK DIVES
1. STARS Framework -
To Evaluate Feature Engagement & Satisfaction In SaaS Products.
Startups have always had difficulty evaluating what new features to build. The team at Bucket created a useful framework they call STARS to measure user engagement and satisfaction for a feature.
Segment: Define the target audience for the feature to ensure the results are actionable and comparable across features.
Tried: Measure feature awareness and determine if the target audience is aware of the new feature.
Adopted: Measure feature activation by setting a threshold for when an account is considered to have truly adopted the feature, filtering out casual users.
Retained: Measure feature retention over time to understand how many accounts continue using the feature.
Satisfied: Measure the satisfaction of retained accounts through qualitative feedback like CSAT scores. This ensures the feature is not just being used, but that customers are truly satisfied with it.
This is an easy way to create groups for your users and correctly evaluate feedback based on where the users are at. I like this framework because it’s structured like a funnel and is very measurable.
2. Airbnb’s 11-Star Experience Framework
In the early days of Airbnb, co-founder Brian Chesky would consistently ask users “What can we do to surprise you? What can we do, not to make this better, but to make you tell everyone about it?”
If you’ve been a subscriber for a while you know that NPS does not give insider insights (though it does have its uses), and Brian created his framework to understand the experience his users were having on the platform instead of just using NPS as well.
He calls it the 11-star experience framework:
It sets a benchmark for what each level of user experience is like within your product that you can refer back to over time.
Trying to prioritize new features?
Consider how much each one moves your users closer to an 11-star experience.
To nail this you need to be deeply in tune with your users — you need to understand not only their wants and needs but also their expectations.
Most importantly, what are their non-negotiables and what are the things they likely can’t even think of — but would blow their minds?
3. Should I Fire Myself As A CEO?
Andreessen famously said, “If you don’t have anyone on your founding team who is capable of being CEO, then sell your company.”
It’s true early on — having a “professional CEO” is a bad idea. They won’t have as much skin in the game as a founder. There’s a reason founder-led companies grow faster.
With that said, it isn’t true forever.
Speed is your biggest advantage early in your startup’s journey, but the delta between its importance and the importance of other things like..
At some point, you need to expand your skillset and evolve from “founder” to “CEO,” and not every founder pulls it off.
Even if you’re not the CEO, your actual responsibilities will naturally change as the team expands from under 10 to over 100 or 1,000. But how do you know when it’s time (if ever)?
Consider how excited you are about the company’s future and how equipped you are to execute it. These are variants on skill vs will or attitude vs aptitude frameworks if you've seen those
If you fall into quadrant 3 (above), it may be time to consider stepping aside. Cuz sometimes, "different wars need different generals".
4. Definitive list of things startups did that didn’t scale.
Previously we have shared a detailed article on how successful startups Use Paul Graham’s “Do Things That Don’t Scale” Strategy to build their startup. Where we have shared a details strategy on how successfully startups like - Doordash, Airbnb, Groupon, Reddit, Cruise etc. use this strategy.
In Simple words “Do things that don’t scale” means -
“Early-stage startups should focus on learning and validating their business model rather than on growth. This means doing efficient and effective things, even if they don't scale well. For example, a startup might initially make manual sales calls to each customer, rather than building a complex automated sales system.”
But check this list, it’s shared by Inari's founder, where he included - 100+ startups “Do things that don’t scale stories”. A must-read guide, download here.
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THIS WEEK’S NEWS RECAP
Major News In VC, Startup Funding & Tech
OpenAI acquired Rockset, a search and database analytics startup, in a stock deal valued at a few hundred million dollars to bolster its enterprise product infrastructure. More Here
Stability AI appoints Prem Akkaraju as the new CEO. Akkaraju, along with a group of investors including Sean Parker, is providing a cash infusion to save the company. More Here
SoftBank Group founder Masayoshi Son on Friday lamented what turned out to be a $150 billion blunder: selling shares in Nvidia years before the chip maker became one of the world's most valuable companies. More Here
PayPal Ventures led a $20 Million funding round in the NYC-based embedded financing platform - Gynger. More Here
General Catalyst, a Silicon Valley VC firm, partnered with local Indian VC firm Venture Highway and allocated $500 million to $1 billion for investments in Indian startups. More Here
Ilya Sutskever, the former chief scientist at OpenAI, has launched a new AI company called Safe Superintelligence Inc. (SSI) with a former Y-Combinator partner. More Here
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TWEET OF THIS WEEK
Best Tweet I Saw This Week
1 billion users
Never ran an ad
Only 30 full-time employees
Founder is the sole director, equity holder and product manager (works directly with every engineer and designer)
No HR
Do you know which startup this is? - It’s Telegram.
Check out the full interview here(Telegram founder and Tucker Carlson)
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POLL
Which section in the Venture Curator Issue is your favourite?
Insightful article, but it's too long. I wish it's a little short and engaging with clear examples. Thank you much for educating us. :)