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🌱 5-Bit Fridays: Negotiating more equity, the evolution of using data, the “Supply Features Trap” of marketplaces, new problems after PMF, and reverse trials for PLG companies
👋 Welcome to this week’s edition of 5-Bit Fridays. Your weekly roundup of 5 snackable—and actionable—insights from the best-in-tech, bringing you concrete advice on how to build and grow a product.
Happy Friday, friends 🍻
As usual, another busy week. The highlight over here at How They Grow (AKA, me at my desk), was this: The CMO at Klarna (as well as Klarna’s official account) reading and sharing last week’s deep dive on Klarna. That was awesome. 🙏 If you missed that analysis on the company winning the BNPL market, not to worry. Here’s a link.
In other news, NYC just passed San Fransisco (for the first time ever) in startup count. The beginning of a new startup land? Also, Instagram is snapping at Twitter’s heels, coming for their share of text-based social media. Twitter is arguably weaker than usual right now, and Zuck probably sees this as an opportunity for an attack on a social vector that Meta doesn’t have a real stake in. Let’s see what happens. 🍿
Otherwise, if you’re in the US, you have a long weekend to get to. So, no more dilly-dallying.
Here’s what we’ve got this week:
How much equity should you have? Concrete tips to help you get paid.
The four stages of using data to manage your startup
The supply features trap of marketplaces
The new problems every marketplace faces after product market fit
A guide to reverse trials for PLG companies
Small ask: If you learn something new today, consider liking this post or giving it a share. I’d be incredibly grateful, as it helps more people like you discover my writing.
Let’s get to it.
(#1) How much equity should you have? Concrete tips to help you get paid.
When it comes to getting compensated, there are four main levers to consider during salary negotiation — whether that’s when starting a new job, or gunning for a promotion. They are:
Base (cash): This is your flat annual salary
Variable (cash): Commission, or a bonus based on your and the company's performance
Title (status): Your seniority within the org
Equity (ownership): The option to purchase a percentage of the company in the future, at a specific price, which is linked to the company’s underlying value. Typically an RSU or ISO.
When negotiating, it’s super unlikely you’ll be able to max out all of those things. A high base usually comes with a tradeoff of less equity. Ideally, you’ll be able to max out the one that really matters to you and land decently with the rest. FYI, benefits could be considered there (i.e. punting for more vacation), but it’s usually a non-factor compared to those levers.
Ultimately, what you choose to prioritize depends on your personal situation — not everyone is down for riskier equity instead of cushy pay.
But, we’re all in the business of betting on the products we work on to some degree, no? That means ownership in them matters. In a truly stellar post by, he gives us a guidebook on how to max out the Equity lever in a negotiation — giving us more skin in the game.
As CJ rightly says:
Negotiating your equity grant at a startup can be SUPER intimidating. It’s maybe the single most important negotiation you'll have in your career.
Yet most startup employees forget that the most concentrated asset in their personal financial portfolio, other than maybe their home, is their Incentive Stock Option grant. And it’s probably sitting in a desk drawer, collecting dust.
So, with that reminder, let’s do a rundown of CJ’s steps toward winning more equity points. AKA, let’s help you get paid.🤑
Understand the Employee Stock Option Pool (ESOP): The first step is to understand the company’s equity structure. This means figuring out how crowded the cap table is and how much equity is still available for new hires like you. CJ’s advice is to look at 4 (publicly available) variables here:
Number of founders: Fewer founders means more available equity points
Time since last fundraise: The more recent a fundraise, the larger the ESOP will be, as companies need to make the pool bigger after a round to attract new talent
Age of company: Earlier-stage companies have more equity to give away (unless they’ve given away unnecessarily/loosely to dev shops, overzealous uncles, etc)
Employee count: More people means a more crowded cap table (the more people at the party, the less cake you’ll get to eat)
Differentiate yourself: “Once you understand the equity structure, you can start to negotiate. Ask what other members of the team joining at a similar time with a similar skill set and seniority are receiving. This should become your theoretical floor for your negotiation, as you start to differentiate yourself.” From there, pick specific skill sets you can address relative to the company’s lifecycle.
Get the numbers: Ask for the number of options, strike price, fully diluted percentage of equity, cash value of equity grant, post-termination exercise period, and vesting schedule. This is essential as you do your homework and forecast what this stake is worth.
Understand the vesting structure: Simply, this means how long you need to wait until you can actually buy your equity units. Usually, you need to stay with a company for a year to get the first 25% (known as a 1-year cliff). From there, typically you earn the rest of your equity for the next 3 years. “The longer the vesting period, the less attractive it is to you as an employee. The less frequent the vesting cadence (monthly vs quarterly), the less attractive it is to you as the employee.”
Research the tax consequences: Nobody likes IRS surprises. So, anticipate when you’ll get hit with a tax bill, based on the security type (i.e. RSUs hit you with a tax bill at vesting, while ISOs have tax implications when you buy them).
Understanding dilution, and how it impacts you: When more shares are created and issued (i.e. to raise money), the stock pool gets bigger, making the units you have “dilute”. Theoretically, the value of the company goes up enough each round for your diluted stock to still be worth more than before. “Benchmark how much dilution is normal, and then forecast how your outcome and ownership may change over time.”
Be realistic: Play hard, but be fair with your ask. That’s why doing your homework matters so much. If you overshoot and seem greedy, that can hurt your goodwill.
Do your homework on company performance: This is my absolute favorite excerpt, as CJ wrote. 👇
Don't go into the negotiation without doing your homework on the company's past performance and track record. Take the time to research the company’s historical growth rates, as well as its past capital events, to get a better idea of the company’s value. If the company is already worth $2 billion, you aren’t going to walk in as the new treasurer and get half a percent. It just doesn’t work like that.
You should also size up if you think the company is relatively over or undervalued based on other companies in the space. If you know the company is crushing it, but hasn’t accepted a super premium valuation, that means there could be a lot more upside still on the table. Therefore, you’d be willing to potentially take less equity because you know the valuation hasn’t popped yet. I’ve personally made it a point to try to identify companies like this to work for in my career.
On the other hand, if you know the company raised at a nutty valuation, plastered all over Tech Crunch headlines, that’s literally an advertisement to NOT join. Why? Because you’re TOO LATE!
The greatest trick startups ever played was using fundraising events to convince employees to join. It’s antithetical to join a startup right after a round if you are trying to maximize your equity outcome.
Yes, you may be de-risked to a certain extent, now that there is cash in the bank, but your strike price is going to be higher on your options to reflect that new flashy valuation. I’ve seen many employees take a big role right after a big round, only to see their options “underwater” (or valued at less than their strike price) when the economy takes a dip. Company valuation is a double-edged sword, so beware.
Super insightful, and while that last bit is seemingly obvious when reading it, it’s something I’d bet is overlooked by many people.
All in all, great advice on winning more equity during negotiations from a tech CFO. For more of it, I highly suggest joining CJ’s newsletter,
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(#2) The four stages of using data to manage your startup
I’ve shared many insights from others on data in this newsletter. Last week we spoke about metrics, and we often talk about data moats, data network effects, decision-making using data, experiments, yadda yadda yadda.
Data is good. But, it’s a tool, and tools can be used wrong.
So, how you use data must evolve as your company does — over-reliance on it will inevitably hurt a product, but data ignorance will too.
According to Sarah Tavel (GP at Benchmark, ex-GP at Greylock, and former PM at Pinterest), there are four stages of using data as a startup grows up. This visual she put together sums it up really nicely. 👇
Let’s unpack each stage of the four stages in a bit more detail:
1) First principles> than data.
In the first stage of a company, you can’t use data to figure out what to build. In this pre-data world, you must first begin the wandering search of trying to build a product that users want.
Why can’t you rely on data?
Well, in the beginning, you’re just too small. Not enough users exploring a big enough surface area of your product. So, if data was your main compass in terms of what to build, you’d end up optimizing around a small local maxima.
Instead, doing user and market discovery through interviews and surveys, etc, will give you a better sense of how to evolve the product beyond your early adopters.
2) Deciding what to measure.
The second stage happens when for the first time, you actually have both the ability to measure engagement of your product, enough users to achieve statistical significance quickly enough, and also the ability to do experiments with what is probably a rudimentary experiment framework. The question then is: what matters?
Last week we defined what a good metric is. In short, it’s one that whether it goes up, down, or stays the same, you’re going to take action.
Tracking everything is a bad idea. And if you get stuck measuring the wrong thing, you could easily end up wasting a lot of time on the wrong initiatives (as well as racking up unnecessary bills and seeding bad data governance practices).
So, as Sarah notes: “Stage 2 is about being super intentional about what is important to measure — forcing yourself to have the intellectual rigor and honesty to let go of any vanity metrics and really distill what the core action is for your product, and making sure you have an experiment dashboard that lets you understand how different segments of your users (i.e., new users, core users, casual users, dormant users) perform. Only then will you be able to truly make the right trade-offs with data.”
3) Data becomes a necessary (but, not a sufficient) tool.
The inevitability of growth is that your product has expanded beyond that early adopter power user that you understand deeply and likely represents a narrow demographic, to a broader, eventually more casual user.
In other words, if you focus too much on the power users that you’re familiar with, you may well be optimizing in a way that makes your product harder to grow. This is because early power users are very different from the broader market.
To continue to grow into new user groups, you (1) need to know who to ignore, and (2) need data to validate your product hypotheses. The dream team is data + user research. It’s the only way to accurately build for a user that isn’t you.
And why data is necessary, but not necessarily sufficient, is said perfectly by Sarah:
There are some decisions (e.g., big strategic initiatives) where you need to be prepared to see the data, acknowledge it, and then discard it. Remember: Data is not a substitute for judgment or strategy.
4) System impact.
If you are successful… and I’m talking really successful here… there comes a time when you have to stop optimizing for a metric, and start having to consider the impact of experiments or new features have on your overall product’s system, or, if you are REALLY big, the system outside your system.
For more of Sarah’s insights, take a look at her Substack,.
(#3) The supply features trap of marketplaces
Two of the biggest challenges for a marketplace involve suppliers. First, acquiring them. Then, keeping them.
Because of this constant concern and supplier obsession, marketplace operators are constantly trying to figure out how they can make their value proposition to suppliers more attractive and more competitive.
The only approach that actually matters though, is demand. Both owning it, and having a lot of it (more on this).
In a great essay by Gilad Horev (VP of Product at Checkr, Ex-VP of Product at Eventbrite), Gilad goes into a common pitfall PMs and founders working on marketplaces make. He writes:
Time and time again, product teams in marketplaces end up investing too much time into features that ultimately won’t be the differentiating factor for a supplier staying in the marketplace. And at the opportunity cost of investing time and resources into the core marketplace value proposition.
As I’m sure some adage goes: Feature factories hardly ever win.
In Gilad’s essay, he goes deep into what he dubs “the supply features trap”, covering why it happens and how to avoid it while building a marketplace.
Here’s the snackable version for you.
First, why do marketplaces build features for the supply side in the first place?
And of those 3 reasons…
This last vector of feature-building is where the supply feature trap door usually opens. 🪤
While all of these can be strong reasons to start building a supply-side feature, the mistake marketplaces often run into is over-indexing on features that are mainly driven by a SaaS-like value proposition. In a marketplace, it’s critical to realize that features have to benefit suppliers and the marketplace rather than just suppliers directly.
It can be very easy to fall into the trap that supply features create: you keep investing in the feature, and end up focusing on a SaaS-like value proposition. This means you are trying to deliver efficiency or service improvements, and your competition landscape grows tremendously. This comes at the cost of focusing on creating more marketplace activity through incremental demand, and enhancing marketplace liquidity. And that is truly the biggest value add for the suppliers.
So, how do you avoid the trap? 👇
Building features that tie back to core marketplace value
According to Gilad, there are three levers you can pull to make sure features drive marketplace value.
Evolve the feature metrics to focus on marketplace value. Yes, you need to make sure your feature actually works by tracking things like usage/engagement. But the main success factor needs to be connected back to core marketplace value — which should be thought of as:
How the feature increases demand
How the feature increases supply
How the feature benefits liquidity (creates more activity in the marketplace), like search-to-fill ratios, buyer or seller utility, etc.
Leverage a product team charter to connect to the marketplace value. “A product team charter is a short document where a team recasts their understanding of the team goal and how it connects to the company strategy. So in any business model, it can be an effective tool to ensure the team’s scope and objectives are well-defined and in line with the business goals. However, it is even more effective and helpful for product leaders that are balancing a lot of complexity. Reviewing a team charter can be a quick gut-check on if the team is connecting to marketplace value or is pursuing feature excellence in more of a silo”
Stay focused on outcomes and user problems, not features. Again, feature factories are problematic and seldom win. As Gilad says, “While it might seem like the fastest or easiest way to get a feature working, putting a product team on a feature creates a laser focus on how to create the best version of that feature. Instead, product leaders should focus teams on a goal or user problem, and the feature can be a part of the product roadmap to achieve the goal or solve the problem.” When you rally a team around the why (not the what), it’s a forcing function for them to think about trade-offs around value, rather than trade-offs around supplier satisfaction with a shiny new tool.
To bring those three levers together:
And while we’re here talking about marketplaces…🫖
(#4) The new problems every marketplace faces after product market fit
First, welcome to Substack,! 👏
I’m a long-time fan of Dan’s and have been reading most of his essays when they come out. Dan is currently Head of Strategy and Analytics at Faire (a $12.4B marketplace), a Partner at Reforge, a great writer, and then some. He very recently joined the new economic engine for culture that is Substack, and one of his first posts, of course, was on marketplaces.
So, here I am (1) spreading Dan’s wisdom on marketplaces, and (2) nudging you to check out his stuff. I promise you won’t be disappointed.
In a recent essay, Dan shares his insights on how the constraint/problem landscape changes for marketplace startups once they get over the 0 to 1 hump.
A great deal of digital ink has been spilled on how to solve the chicken-and-egg problem and take a marketplace from 0 to 1. The typical advice is: focus on creating liquidity above all else, spend heavily on customer acquisition and incentives to bootstrap the network, and price low to reduce friction.
And it’s good advice! Until it rapidly becomes dangerous advice. When you reach product market fit and GMV begins to scale rapidly, all of the sudden your resources are pointed at the wrong metrics. Your unit economics are upside down. Direct competition ratchets up.
Below are five lessons I’ve learned observing many marketplaces make this transition, and how you can avoid learning them the hard way.
Liquidity is the only thing, until it isn’t
Customer acquisition is wildly inefficient
Day 1 pricing is a blunt instrument
TAM expansion is harder than it looks
Network effects will not save you
When I read those 5 tidbits, I was hooked. So, here’s what you need to know about each, with my favorite excerpts from Dan.
Problem #1: Liquidity is the only thing, until it isn’t
Liquidity simply means how quickly and reliably your customers can find what they are looking for. Remember the toilet paper wars of 2020? Grocery stores had low TP liquidity…a rough time.
When marketplaces are getting started, liquidity is the product. It needs to be obsessed over. For buyers, supply is everything — if you don’t have people selling (i.e. vendors, drivers, homes to rent), you can’t even consider having PMF. But, as Dan argues, once you hit PMF, liquidity may well not be the constraining factor anymore. Your attention should shift elsewhere. 👀
Every marketplace that gets to sufficient scale wakes up one day to realize that supply is no longer the only thing. The liquidity metric they are tracking (search-to-fill rate, customer wait times, etc.) starts to asymptote, and adding supply increasingly does less to improve the customer experience.
Finding and acting on the next constraint in the business is a key part of transitioning effectively to the scaling phase. The key question to answer is “what is the rate limiter on new buyers converting, and on existing buyers spending more?” One way to find out is to simply ask them. Eugene Wei outlines how Amazon did this in the early days:
“We had two ways we were able to flush out this enemy. For people who did shop with us, we had, for some time, a pop-up survey that would appear right after you'd placed your order, at the end of the shopping cart process. It was a single question, asking why you didn't purchase more often from Amazon. For people who'd never shopped with Amazon, we had a third party firm conduct a market research survey where we'd ask those people why they did not shop from Amazon.”
In Amazon’s case, that rate limiter was shipping fees, and it launched them on a multi-decade journey to reduce those fees.
Problem #2: Customer acquisition is wildly inefficient
Once you have a reasonable line of sight on your unit economics, it’s worth doing the math to start charting your path to sustainable customer acquisition. You may be farther away than you think.
Some useful questions to ask as you go through this exercise:
Are there any acquisition channels that are working? If so, it may be that you need to prune inefficient channels, or tighten paid spend on the ones that are not working.
Are you spending heavily on both sides of the marketplaces? To make dual-sided economics work, most marketplaces don’t. Often, one side is much less constrained than the other, e.g. Etsy spends almost nothing to acquire sellers. Others get there by building a large organic channel on one side, e.g. Thumbtack, Expedia and many other SEO powerhouses.
Are customers failing to activate or retain? You can check benchmarks here. If you are far from this, it is a good place to start learning and experimenting.
Are your unit economics constraining customer LTV? If contribution margin is too low, you may need to focus on taking cost out of the system, tuning incentives, or increasing price, the latter of which is the focus of the next section.
Problem #3: Day 1 pricing is a blunt instrument
New marketplaces (especially first movers in their industries) often set commission pricing in a vacuum. There may be some signal from legacy incumbents, and they can make some assumptions around the value props they will provide and related cost structure. But early pricing is largely… made up.
I think that’s spot on. Unfortunately, early-stage pricing is often just a thumb-suck, as founders skip systematic diligence around how to price and charge for their product's value (learn more).
Anyway, as marketplaces scale beyond PMF, they get a lot more data about their unit economics, the evolving competitive set, and people's propensity to pay. And Dan has observed that “this usually illuminates that there is a lot of value left on the table.”
Because of the prevailing wisdom around how to create liquidity, most marketplaces err on the side of pricing too low to start. But updating pricing is not just about extracting value from existing transactions (capturing “consumer surplus”). It is also about lowering prices in other areas to allow expansion into customer segments, markets, or types of transactions that were previously blocked by onerous pricing (capturing “deadweight loss”).
Dan argues that it’s usually more effective to come at the problem of updating a marketplace’s pricing structure by considering three heuristics.
Growth model constraints: “One of the most illuminating monetization exercises is simply to ask: what part of our model is most holding back growth? The next question is, can we remove friction from our key constraint?”
Segmentation. Once at a decent size and with stable unit economics, operators can start slicing and dicing their users to find opportunities to monetize better. “The core line of attack is usually supplier segmentation. What underlying traits change the economics of selling on the marketplace and thus their willingness to pay? Commonly this includes geography, category, and customer size.”
Disintermediation. Simply, this means users are bypassing the marketplace. When that happens, it’s usually symbolic that price > value, and there’s a need to either add value or lower price. “Ask yourself: where is disintermediation tending to cluster? Are certain categories of suppliers more likely to disintermediate?”
Problem #4: TAM expansion is harder than it looks
Dan argues that raw TAM (Total Addressable Market Size) is often used as a core factor in deeming whether a marketplace should expand to a new market. However, just because something is big does not mean it should be prioritized.
I don’t like networking, but this was the first example that came to mind. Imagine you’re a savvy networker (not me), and your goal is to professionally mingle with as many people as possible. So, you head online to find events coming up. You find one with the most people and prioritize that.
Except, by focusing on this big TAM, you ignored the fact that it’s an event with predominantly German speakers (which you don’t speak), and it’s a laundry convention (which you know nothing about).
You’d be far better off going to a smaller meet-up with people like you.
That oversimplified example highlights two things that Dan notes are much more important when predicting the ability to find PMF in a new market.
Innovation Risk: Essentially, how similar is this new market to the original one you’ve achieved PMF in? “Is the industry structured similarly in terms of the level of fragmentation, competitive set, and economics? Are consumer behaviors (and thus their needs) similar? If so, your existing business model is much more likely to work.”
Leveraging existing strengths: For marketplaces, their strength at this stage of expansion is mostly their existing network of customers. So the question becomes “where is there sufficient overlap with my existing network that I already have the supply side, the demand side, or both?”
It’s the combination of low innovation risk and high overlap with current strengths that’s the sweet spot of TAM expansion. Of course, assuming the TAM is sufficient enough for success to be worthwhile.
Problem #5: Networks effects will not save you
The most obvious form of defensibility for marketplaces is two-side network effects: more supply drives more demand, and vice versa.
So, once folks get past the 0 to 1 stage, they think that scale and this network effect together become their moat.
However, as Dan posits, “The truth is defensibility from network effects is more brittle than it seems”, and he calls out two reasons:
Rampant multi-tenanting: Suppliers usually have no issue using other marketplaces
Without a lot of supply variety, your benefits start to asymptote. Customers want variation, if they keep finding the same thing, further transactions won't make them as happy as the first ones did.
The combination of those two factors means that it doesn’t take all that much time or capital for someone else to brute force a similar network effect. As a result, most marketplaces must ultimately build a more durable moat by layering in one or more additional forms of defensibility. The key to winning is to be better at aggregating demand, because if you can do that supply has little choice but to follow.
And what are those other moats? A good place to start thinking about that is with 7 Powers: The Foundations of Business Strategy, by Hamilton Helmer.
For more top-shelf content like this by Dan, check out
(#5) A guide to reverse trials for PLG companies
Product-led growth companies (where the product must sell itself) often face the question when considering pricing: “Freemium, or Free Trial?”.
There are pros and cons to both. However, when you get to have your cake and eat it too, it’s a great day.
Luckily,recently wrote about how this notion of choosing either freemium or free trial is actually a false trade-off. There really is a way to enjoy the best of both worlds. 🍰
But, how can you have two pricing models like that? Don’t both aim to achieve the same thing (showcase product functionality without requiring a user to pay upfront), just differently?
Yes, and you can with a concept known as the “Reverse Trial”.
Let’s break it down: 👇
The Trial Approach: Your start by giving the user full access, and in the end (i.e. 7 or 14 days later), they either pay or lose access.
The trial aims to convert free users to paid through a stick approach to the decision (i.e. don’t lose out on what you’re already doing). The drawback is that it gates product value. For users who don’t switch to paid at the end, there’s no further product usage that may lead them to convert. Winning them back is a pain.
The Freemium Approach: Starts for free with an option to upgrade at any point
Freemium is great for PLG because it brings down acquisition friction in a big way, making the most out of your traffic. The drawback is that you need to do a lot to drive awareness of the premium features, leading to a lower free-to-paid conversion rate.
Okay, so what does a merger of the two look like? 🤔
The Reverse Trial approach puts customers in a trial with access to paid capabilities at the acquisition event, but if at the end of the trial they don’t go paid, users are downgraded to a freemium version.
This hits all the right notes of each approach, making it great for driving conversions. The customer gets to build habits with the paid features and invest in the product. And then it drives overall usage, as people can continue using the product for free if they don’t convert — making it much easier to convert them later as they are still engaged.
As Kyle describes:
You don’t need to choose between acquisition or conversion goals; you can pursue both! And you put your best foot forward with new users, giving them access to your most advanced features for a limited time. From a behavioral psychology standpoint, you start to benefit from loss aversion where the pain of losing something is twice as powerful of a motivator as the pleasure of gaining.
It’s no wonder that PLG giant Airtable, a workflow platform that lets your team build applications to run your most important business practices and is valued at $11 billion, has been an early pioneer of reverse trials
Here’s a great visualization of this he put together:
To see a real-life example of the reverse trial in action at Airtable, continue onto Kyle’s interview with Airtable’s Head of Growth, Lauryn Isford.
Also, earlier this week I added his newsletter,to my recommendations. It’s a truly excellent Substack focused on SaaS growth, go-to-markets, PLG, and pricing. To get Kyle’s posts, drop your email below.
🌱 And now, byte on this if you have time 🧠
For many years now, a group of researchers and activists have warned about the potential dangers of children using social networks. The warnings resonated with me emotionally, since so many people I know — young and old — have struggled with their own relationships to apps like Facebook, Instagram, and TikTok. It seems logical that what many people experience as a kind of icky feeling after too much scrolling manifests as something much more serious in others — particularly in young people.
— Casey Newton
Recently, US Surgeon General Vivek Murthy and his team synthesized more than a decade of research into risks posed by social networks, and they conclude that the potential for harm is significant.
Casey Newton fromunpacks and opines on some of the key findings.
And that’s a wrap for this week, folks.
As always, thank you so much for reading. If you learned anything new, consider tapping the like button in the footer, the share button below, or telling anyone you think would find my stuff valuable. Thank you.🙏
Have a great long weekend and Memorial Day if you’re here in the US.
Until next time.