Tag Archives: product

The Monetization Playbook We Used at Eventbrite

One of the problems we faced when I joined Eventbrite was that Eventbrite had a pretty low take rate, meaning when event creators sold paid tickets on our platform, we took a very low percentage. And when creators sold free tickets on the platform, which are the majority of tickets, we didn’t make any money. You need an incredible amount of volume to make profits with low take rates, like say Paypal or Stripe, and even though Eventbrite has tremendous scale, it wasn’t enough to make material profits. We needed a way to make more money for the value we created if we wanted to be a successful company i.e. a new monetization playbook. I’ll discuss below the playbook we built, and some of the process to get there.

A tweet I made when I was working through this at Eventbrite.

At the most abstract, there are two ways to grow profitable revenue for a network business:

  • Make more revenue per customer
  • Increase network volume

Eventbrite had tried to improve its revenue per customer before I arrived as Chief Product Officer. The company did what most SaaS companies do in this position: hire a consulting firm, work closely with them for a period of time, and decide which of their recommendations to implement. This created the first packaging model for Eventbrite. Instead of one package, there were now three that had different features for paid event creators. This all sounds very non-controversial, but the impact was pretty negative for Eventbrite. While revenue initially went up, over time, growth slowed as event creators eventually churned due to the new prices, which doesn’t just impact retention, but also acquisition, as the marketing they do for their events is the main driver of new creators. Revenue per customer went up but network volume slowed. Not a great tradeoff. In addition, no one chose the first plan, and free creators still paid Eventbrite nothing.

Eventbrite was pretty constrained on how to adjust levers in the pricing model. As a transactional business with payment processing costs, you tend to have both fixed and variable fees. As you drop one or the other, you basically decide whether you’re hurting pricing for event creators with smaller or higher priced tickets. Companies like Orb have since come along to make bigger changes in pricing more manageable, but our system at the time was very brittle and rigid.

So the company tried to re-ignite the network volume growth the pricing change hampered, but with a pricing model where two thirds of creators still don’t pay and a sub-10% charge for paid tickets that made many forms of paid growth unviable. 

The product team rushed in to create new value for event creators, but this just put more pressure on the take rate as you’re now trying to maintain more software for the same amount of revenue. Everything the product team recommended default shipped to all pricing plans as well, which meant we weren’t improving the value of more expensive plans over time. The baseline reasoning for this is to maintain pace with competition, which, if you hear it in your company, might mean we’re not investing in the right features that can’t create differentiation. But also I think this default practice reflected a lack of comfort with pricing recommendations and becoming more business oriented in our approach to product development. Or maybe just laziness.

This laziness extended beyond pricing the new features, but also marketing them. Many features lacked awareness with our creator base, creating a deadly loop of product development.

We needed to shift into a new model that forced product teams to think about both pricing and marketing features as a core part of their job, and it was of course my job to teach them how to do it. The model needed to look more like the below:

Obviously, you cannot charge for every feature. Sometimes, we do need to keep up with competition, but changing the default is important to getting a team to help the company actually become a sustainable business. 

If we get in the habit of charging for the value we create, the main question is how to do so at both a feature / product level and an overall offering level. The first question to actually ask is does the feature or product create value that is more important than how much we could monetize it. How we thought about answering this question is:

  1. Does it drive virality? If so, we’re incentivized to give it away.
  2. Does it drive activation to long term retention? If so, we’re incentivized to give it away until activation is achieved.
  3. Does it drive lifetime value? If so, compare lifetime value gains vs. willingness to pay to determine if we should charge for it.
  4. If something does not drive virality, activation, or lifetime value, but people have willingness to pay, we should charge for it.
  5. What is the cost to support this (eng costs, unit economics)? Does the value provided in the above questions cover for that cost? If not, don’t build it or sunset it. 
  6. Do the costs have economies of scale? If so, can sharing these economies improve lifetime value? If so, refer back to rule no. 3.

If we decide to charge for something based on answering the above questions, the next question is how. We used the following 2×2:

Low # Creators Use High # Creators Use
High Willingness to Pay Add-On Higher Package
Low Willingness to Pay Don’t Build Core Package

What creates low willingness to pay for things that a lot of creators will use is that they are available elsewhere in the market. While you of course need to build some of those, that is not where enterprise value is created. Enterprise value is created in the top row where there is differentiation and therefore higher willingness to pay. Answering these questions while working on the initial concept of a product vs. at the end when it’s already been built is a good prioritization element in and of itself.

The reason the 2×2 defaults to packages is that if you apply a willingness to pay framework too literally, monetization can become too complex. Pricing ever feature a la carte quickly turns into something approaching the toothpaste aisle at a drug store. In theory, there is a reason for why there are a hundred plus varieties with different features and price points, but no customer will go through the work to understand all of that. So there is still a bundling art form that product collaborates with marketing on to make the packages viable and digestible at perhaps the sacrifice of some individual feature willingness to pay. Frameworks are great for 80%, but I always preach that product folks can’t let the framework absolve themselves of responsibility for using their brain to make the company successful. 

If you want to learn more about pricing and packaging frameworks and how to measure willingness to pay, I highly recommend Reforge’s Monetization & Pricing program. If you’re not sick of me, the Pricing Strategy and Advanced Growth Strategy programs I helped create are also starting again soon.

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Currently listening to my Dune: Part Two playlist.

Is My Second Product Failing?

In my recent essay about second products, I talked about how the goal of building a second product is not the traditional concept of product/market fit we’ve all been raised on regarding startups. In some ways, our goal with a second product is easier, and in some ways, much harder. The goal is to inflect the growth of the entire company over time. This is hard, and does not usually unlock easily. This can make it tricky to evaluate as an engineer, designer, product manager or CEO if the investment will bear fruit and whether one should keep going. I’ll explain how I think about these decisions as a product leader and give more context on the journey of developing entirely new products inside a company.

First off, let’s spend a moment on this product/market fit point. If the job is to inflect growth for the overall company at some point in the not too distant future, product/market fit in the general sense may not be enough. Let’s say your second product builds a new product that is growing healthily with a smaller group of customers than the core business at a much lower price point. By general standards, the team found product/market fit, but will never be able to inflect growth of the combined company. This is failure inside a medium to large company. 

When the above example is interpreted correctly inside a company, this means teams start to take larger swings that, if they work, can possibly move the entire business eventually and open up new growth opportunities for the entire company. Bigger swings generally mean a longer amount of time building toward something that could work in the future. How much time and money has Meta spent on VR? Amazon on Alexa? Google on GCP? Extreme examples sure, but partially why they are extreme is these companies’ new product development has to move revenue growth at what are now some of the largest companies in the world.

While we are used to startups needing a decent amount of time to find product/market fit (sometimes taking many years), companies that are scaling do not generally think on such long timelines. OKR’s, which most teams use inside scaling companies, are generally measured in cycles of three to six months, which is much too short for a team investing in new products to evaluate success. 

A good team will push the company to give them a longer time to journey toward a new product that can be a success. But this just creates another problem. For a team working on what is usually considered one of the most important projects at the company, executives want to hear about “progress” or “milestones.” Are we on the right track, or are we lost in the wilderness?

Good teams working on new products will outline the assumptions they have to prove, in order, and a rough timeline on when they expect to either prove or disprove those assumptions. It’s a shot in the dark, but for territory that is yet unmapped, it gives the rest of the company some waypoints as well as a general estimate of how long the team expects to reach the destination. Even though the team usually knows nothing in reality.

I liken this journey to the myth of finding a rainbow that leads to a pot of gold. We’re saying the rainbow starts here, we expect it to take us through these peaks and valleys, and we think the journey will take a while with lots of unknowns. The most important thing for a team to do at this stage is state what the destination should hold. 

The company should know at this point it’s a potential new product with a large enough addressable market, price point, and whatever other strategic dynamics are important to the company that can lead to a new S-curve of growth. Much of the time, this list includes certain weaknesses of the current product suite the new product hopes to mitigate. Such examples are expanding the addressable market for the company, increasing frequency of usage, improving unit economics, etc. This list is important, but often missed in the new product development journey because product teams want to see where their insights take them. I would argue this is a mistake. The destination has to be worth it for a team that may spend over a year developing something new, so we need to align on what “worth it” looks like.

Okay, so you’ve mapped a destination representing a huge pot of gold awaiting the company when you reach it. You also need to map the milestones, usually proving/disproving assumptions in a certain order, and what you expect to achieve along the way. Updates to executives typically focus around these points in time. It’s a convenient way to discuss everything you’ve learned that may be important while getting a general pulse check with the executive team on their excitement around what the team has been working on.

Image by MariaAllen via Midjourney

Inevitably, some of these assumptions will be wrong. Uber thought the best way to launch a food delivery product was to copy some earlier stage startups called Spoonrocket and Sprig that had a limited menu of food that could be delivered in ten minutes. It turned out copying Doordash and Postmates was the more viable option. This sort of pivot is common, and still got them to the destination of a growth market with a large TAM that offset peak times for driver demand and allowed new types of supply that weren’t a great fit to core Uber to also make money driving. It also increased lifetime value and unit economics for the combined business. Pretty nice win.

When these assumptions are wrong, it does not mean the team has done anything wrong. It means the team is developing a feedback loop with the customer and the market and a learning culture inside the team. The waymarkers were wrong in the case of Uber Eats, but the destination was still correct, and the team eventually found a more accurate map to the destination. This is generally how it’s supposed to go.

But what if the learning culture you’ve developed inside the team and the feedback from the market and customer base is not getting you closer to the original destination? This is where things get concerning, and teams get confused on how to move forward. There are a few ways in which this can happen:

#1 The destination is further away than we originally imagined.

In this example, there are many more waypoints the team needs to travel to to reach the destination. Think of it as the road being much longer and windier to get to the pot of gold. At this point, teams need to remap the path, and examine whether the larger investment of time and resources is worth it to reach the destination. Much of the time, the project will get too expensive and time consuming for too small a reward. This happened to us at Pinterest. A team inside the company started building a Q&A product around the Pins people saved to the network. With every investment the team made, the path to success lengthened in many ways, such as the amount of moderation and quality control required when Pinners on the network started asking more questions and expecting serious answers. The team eventually recommended sunsetting the project, and they did. Other times, the reward is still deemed to be worth it, and the team keeps moving.

#2 The destination is the wrong destination or does not exist. 

This is a common problem when the new product investment is too business focused and not enough customer focused. Bigger companies can get attracted to product ideas that the band Soundgarden coined “pretty nooses”. As lead singer Chris Cornell (RIP) explained, a pretty noose is “just sort of an attractively packaged bad idea, pretty much, something that seems great at first and then comes back to bite you.” I’m sure you’ve seen them in every Y Combinator batch and in every product brainstorm. Everything the team learns as they work toward proving their assumptions on these ideas is that the market just isn’t there or that the customer pull is not strong enough. This happened with Tinder Social. Matching groups of friends just wasn’t a problem people were that interested in using software to solve. Acquisition is very hard, retention is even harder, and the market seems incredibly small and difficult compared to growing the core business.

#3 The team has veered off track to a new destination.

This problem occurs when the team investing in building new products is too focused on the lean startup path of developing products and fails to remember they are not their own startup, but a team attempting to accomplish a business objective for a larger company. In this scenario, the company has to ask how attractive the new destination that seems to have more traction than the original destination is. I was working with a company recently working on a new product that would address a new market that was higher frequency. All of sudden, the team working on it veered back toward the market for the original target market, for which this product would have too small a TAM and not address any of the new market or frequency objectives we originally had for starting the project in the first place. We decided to course-correct.

#4 We cannot seem to get closer to the destination over time.

This can much of the time be one of the other failure modes, but it’s important to call out on its own. If the only thing the team learns over time is “well, that didn’t work either”, it may be time to shut down the project. New activity on the problem needs to be generating new learning that makes the map to the destination clearer. If all we are learning is we are still going in the wrong direction over time, the chances of finding the right direction trend to zero.

Development of new products with new value props is difficult, and the frameworks for building startups or building other products inside the company often fail to apply to these situations. This makes it super important that teams map their destination, map the territory they expect to go through and map the process they will use, and stay on track for all of those to ultimately make the best decisions to maximize an outcome that meaningfully impacts the company.

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Currently listening to my Footwork playlist.

When and How to Build Second Products

This is part three in a series of posts related to some presentations I did for the TCV Engage Summit. The Summit gathered ~40 CPOs and product leaders to chat through topics centered around product development and product-led growth. This year, topics ranged broadly from incorporating AI to deliver world-class consumer experiences to defining and measuring different forms of community-powered growth. You can read parts 1 and 2 here and here.

In a previous post, I talked about how product work post-product/market fit shifts from zero to one innovation to features, growth, and scaling work. But a question founders and teams often ask is when do we start layering in innovation work again that creates new value props. In Reforge terms, we call this new product expansion. I recently did a talk for TCV’s Engage Summit where I explained the different types of product expansion, when to start building that second product with a new value prop, and how to know if it’s successful.

Why Second Products Matter So Much

Why do we even care about second products? Don’t some of the best companies in the world win with one dominant product? Well, increasingly that’s not the case. Companies can rarely ride one product into the IPO sunset anymore. Yes, the headlines are filled with many of these examples, such as Google in the 2000s or Zoom in the 2010s, but these examples reflect an environment that is becoming increasingly rare. The tech IPO narrative used to reflect stories that would include much of the below:

  • Large markets
  • Low or stagnant competition
  • Rapidly growing markets
  • Strong network effects or economies of scale
  • Scarce talent pools

A lot of those bullets can be explained by just the growth of the internet, and there being no entrenched internet-first competition. The maturity of the internet means most of these are no longer the case. Almost every recent tech IPO is multi-product at time of IPO, and the dynamics of their markets appear much different:

  • International competition
  • Multiple startups in the same space
  • Incumbents are tech native, no longer asleep, and copy what works from startups quickly
  • There is talent across a wide range of companies and skills
  • Network effects are no longer impenetrable

Uber, Instacart, Doordash, Unity, Klaviyo, Nubank, Toast and many other recent IPOs all reflect this new reality.

The Types of New Product Expansion

There are many ways for a company to expand its product offering, with different levels of difficulty. The main vectors on which product expansion should be evaluated is whether the expansion changes the product, changes the target market, or changes the core competencies required to deliver the product’s value. I highlight six different types of product expansion, in increasing levels of difficulty based on these vectors.

Expansion Type Product Market Core Competency Examples
Geography Existing New Existing Grubhub LA, Pinterest Brazil
Category Existing New Existing Whatnot Sneakers, Thumbtack Home
Format Existing Existing New Netflix Streaming, Snap Spectacles
New Value Prop New Existing Existing Uber Eats, Hubspot Sales
Platform Existing Mixed New Shopify App Store, Salesforce App Exchange
Strategic Diversification New New Existing AWS, Cash App

Geographic and category expansion skills are fairly well developed in software businesses. Companies build a deep understanding of how they achieved product/market fit in the first market, and make as few tweaks as possible to adapt the product/market fit to these adjacent audiences. Most marketplaces and social networks have executed these playbooks fairly well.

Format changes are usually only required around platform shifts already occurring or platform shifts a larger company is trying to drive. The last large one was mobile, and most internet companies were able to replicate their success in mobile.  Netflix and Snap have worked on more interesting format shifts, building on entirely new technologies to deliver their value props on new forms of media.

New value propositions are what we traditionally think of for second products, and will be the focus of the rest of this post. This is creating a new value proposition for your existing audience so that you can acquire, retain, and/or monetize them better. In extremely horizontal products, it may be wiser to launch a platform than build a lot of this new product value yourself, but this requires massive scale to attract external developers, and is very difficult to execute. I have written more about platforms here. Strategic diversification is a much rarer phenomenon where a core competency you have built internally is marketable for an entirely new value prop and audience, like Amazon leveraging its core ecommerce infrastructure to sell to other developers, or Square leveraging its financial expertise in SMBs to launch a consumer fintech product with Cash App.

New Product Value and S-Curve Sequencing

In my previous post, I talked about S-Curves. In that post, I mentioned that sequencing from an original S-Curve to a next S-Curve is the key to long term sustainable growth. That sequence can come from finding a new growth loop, but eventually that next S-Curve will require new product value to be created. This is what I realized when I joined Eventbrite. Eventbrite initially found this success with a content loop around event creation.

To continue its growth, instead of investing in new product value, Eventbrite kept grafting new growth loops onto this core loop to acquire more event creators and drive more ticket sales per event, creating a much more complicated growth model that looks like the below.


What became clear after building this model of how Eventbrite grows is that all of this effort would no longer drive the kind of growth Eventbrite needed to be successful on the public markets. We could no longer acquire event creators and ticket buyers fast enough, and we didn’t make enough money from them when we did. If we wanted to grow sustainably, we needed new products. And we probably needed them yesterday. It’s not that the company hadn’t ever tried to invest in new value props, but those that could create significant new growth had eluded them.

When To Invest in New Products

If you want to be proactive in thinking about when you should invest in building new products vs. diagnosing a growth problem and determining new product development requiring years of effort to be the solution, how do you do that? Well, the first step is tracking what impacts the need for a second product inside your company. Besides building a growth model and forecasting your growth from it, which I absolutely recommend you should do, what are the factors that contribute to how quickly you need to be investing in that second product after the first product finds product/market fit?

Historically, the factor that most people use as a heuristic is the business model. Traditionally consumer businesses have longer S-curves, so there is less of a need for a new product to drive growth. B2B requires suite expansion. Why does B2B require suite expansion? Well, they usually do not have network effects which makes marginal growth harder, of course, but the main reason is competition from bundled competitors. Acquisition, retention, and monetization potential of your first product is another reason B2B tends to expand earlier. Second products influence sales efficiency and profitability dramatically. Next is the size and growth of the market. If the market is large, your product can grow inside it for a long time. And if the market is growing fast, market growth can frequently drive enough company growth on its own, like, say, Shopify with ecommerce. The smaller the market is, the faster you need to expand the addressable market to grow. The last factor is how natural product adjacencies are for your first product. Generally, in B2B, product adjacencies are more obvious and less of a gamble to invest in. Launching successful consumer products is very hard with a very high failure rate.

But I’m going to show you why if you pay close attention to these other factors, the business model can be a red herring.

New Product Expansion by Business Model

Let’s break some examples down by business model and start with pure consumer businesses. Pinterest and Snapchat were compared a lot because we started scaling at around the same time. And even though they are both the same business model, you can see some of their attributes look a lot different. 

First, no one actively competed with Pinterest during its rise to be the primary way people discovered new content related to their interests. Snap, meanwhile faced an aggressive competitive response from Instagram as they grew to be a place where friends interacted around pictures. From a customer acquisition perspective, the companies grew in very different ways too. Pinterest grew by capturing users searching for things related to their interests on Google while Snapchat grew virally. Their retention strategies were also different. Pinterest primarily increased engagement by learning more about what you liked and recommending content better and better matched to your interests over time. This is usually a strong retention loop. Snapchat built out your friend graph, but didn’t really get much stronger after that. In fact, too many friends might be off putting. The most important difference was the monetization potential. Pinterest’s feed of content related to your interests is a perfect model for integrating advertising and commerce, the two best consumer business models. Disappearing photos however was not a good fit for either of those models, and likely best lent itself to subscriptions and virtual goods, both largely unproven at consumer internet scale. Lastly, Pinterest grew adjacencies by making the product work better with interests in different local geos and in different categories e.g. travel vs. fashion. Snap had similar geographic growth, but had some additional format and product adjacencies.

Okay, so let’s look at how Pinterest and Snapchat grew their product offering over time. We’ll focus on the consumer, not advertiser side of the equation for this example, though obviously both companies built advertising products. The companies launched around the same time, and launched their second products around the same time. Pinterest significantly evolved how its core product worked, changing both the acquisition and retention loops over time. Acquisition shifted from a content loop built on top of Facebook’s open graph to a content loop built on top of Google with SEO. The way Pinterest retained users also changed, from seeing what your friends were saving to getting recommended the best content related to your interests regardless of who saved it. Snapchat did not evolve their core product nearly as much. But Snapchat’s second product was a lot more successful. Snapchat Stories was a huge hit. Pinterest around the same time released Place Pins, a map based product that did not find product/market fit and was deprecated. Both companies also launched additional new products in the coming years. Snapchat found new product success again with Discover, and Pinterest again failed building a Q&A product around saved content.

So wait a minute? You’re telling me Snap succeeded multiple times in product expansion where Pinterest failed, yet the companies are valued at about the same. What gives? Well, it turns out Pinterest didn’t need to expand into new products because its initial product had great acquisition, retention, and monetization potential, albeit with some evolution on how they worked. Iterating on its initial value prop was the unlock, not creating new value props. In fact, the new product expansion work outside of expanding countries and categories was a distraction that probably prevented the core product from growing faster. The company might be worth double if it had not spent so much time trying to develop new products. Snap, however, probably would not have survived without product innovation because its first product had low monetization potential. They needed those new products to work, and they did.

Let’s look at an example in SaaS. I had the pleasure of working with both Figma and Canva as they were developing. I was an advisor to Canva starting in 2017, and got to work with Figma while I was a growth advisor at Greylock, which led the series A investment. It’s a fascinating example of two design tools targeting entirely different audiences, basically designers and non-designers. 

At the time of their launch, Figma was in a competitive space with legacy products from Adobe, and many tech companies were using Sketch. Theoretically, Adobe’s Photoshop was the competitor for Canva, but it was much too complicated for laypeople to use, and much of Canva’s pitch was that it was Photoshop “for the rest of us”. Both could acquire users by having creators share their designs. Figma looked like it would be a much higher retention product as it was multi-player from the start, and applicable to larger businesses. Canva was more of a single player and SMB tool. As a result of this, it looked like Figma would monetize a lot better, with a classic per seat model selling to enterprises, with Canva having a lot of single user subscriptions. 

At the time, investors didn’t think the design market was one of the larger markets out there (they were wrong), but everyone did think the category was high growth. Both companies had some nice theoretical adjacencies in terms of formats they could work in, new products they could create, and platform potential.

Both companies evolved how they acquired users over time, layering in sales, and Canva got a huge boost from SEO. Both companies also evolved their retention strategies. Figma became a tool not just designers to collaborate, but for those designers to collaborate with their peers in engineering and product. Canva created lots of ways for users to not start from scratch with community-provided templates and stock photos to leverage. 

Figma launched its first new product in 2019, called Figma Community. It intended to create  a Github-like product for designers, or perhaps a Dribbble competitor. It has not reached the company’s expectations. Canva launched Presentations in 2021, and it has become a heavily used product. Both companies have continued to invest in delivering new value props. Figma launched Figjam, a Miro competitor in 2021. It has not become the Miro killer the company imagined as of yet. Canva launched its video product also in 2021, and it continues to gain traction, along with a suite of other enterprise bundle features more recently, like a document editor.

So on paper, it looks like Figma’s in a competitive space. Canva is not in a competitive space. But while Figma has had mediocre product expansion and is still being sold for potentially $20 billion, Canva is grinding on building out a suite to succeed. Why? Because Figma’s product/market fit so quickly surpassed what was on the market that products ceased to become competitive over time. And while Canva didn’t have competitors, it became a substitute to the big incumbents Adobe and Microsoft, forcing them to build copycats and respond. Canva as a point solution likely loses out to their bundles if they don’t expand their suite successfully.

Time and time again, we see two things. One, companies in the same space may need to think about new value prop development much earlier than other seemingly similar companies. Two, we see new products not inflect company growth when they are a random bet on innovation. New products tend to work when they have to work for the company to succeed. If you can still grow your core product, they rarely get the focus they need to succeed, and furthermore, might be a less efficient use of resources than continuing to grow the core business. Figma will not live or die on the success or failure of Figjam. Canva might need these new products to be successful to stay competitive long-term. 

Portfolio approaches that recommend some percentage of development on innovation vs. features vs. growth vs. scaling tend not to be where massively successful second products come from. Understanding your growth model, and betting big on new product development when you sense the company needs it, tends to be the more successful approach.

Okay, let’s look at a marketplace example. Here is where you see how marketplace strategy has needed to evolve over time. Older marketplaces like Grubhub were extremely profitable because they did not facilitate the transaction beyond payments. More recent startups like Instacart have needed to manage a significant component of the delivery of the value prop, which means its monetization potential out of the gate is much worse. 

Similar to Snap vs. Pinterest, Grubhub’s initial market was so large and so profitable that all new product value expansion did was limit the potential of the core product. Pickup cannibalized search results and lowered activation rates, especially in some key markets like LA that allowed upstarts to gain traction, notably Postmates.

Instacart’s initial product however required so many complex operations that it found it could not eke out real profits while paying groceries and pickers. But it could expand its network to CPG advertisers, replicating grocery market slotting fees in a digital product. So these companies had very different paths to similar market caps despite both being labeled marketplaces.

Last, but not least, let’s look at a consumer subscription example. Duolingo and Calm launched around the same time as consumer subscription apps. Both are in competitive spaces that struggle with retention because building new habits is hard for consumers. The language market is however considerably larger than meditation.

Both companies evolved their acquisition strategy over time, but Duolingo got a lot more leverage out of virality, keeping their acquisition costs much lower. Duolingo’s core product experience also got stronger over time through both data and manual improvement in lessons from user engagement, and laying in gamification tactics. Calm moved from web to app, and built in some daily habits that helped retention. 

What made Calm a much more interesting business though was the launch of Sleep Stories. Not only does expanding into sleep expand the target audience dramatically, it makes it easier for Calm to become attached to a durable habit. People have to sleep; they don’t have to meditate. Calm also was able to expand into B2B by selling Calm as a mental health benefit. Duolingo did not have the same success in new product expansion. While the core product continued to get better at covering more languages, new product efforts failed to create value, such as TinyCards in 2017. Yet, even with this fact, Duolingo appears to be a lot more successful than Calm, likely primarily due to the acquisition strategy and larger initial target market.

In these scenarios, it is not good to assume you are one of these companies on the left side of the table where your initial product/market fit will have such a large addressable market and lack of competition that you can scale successfully without new product development. It is also dangerous to assume you will need a lot of new product innovation when your initial target market ends up being quite large. What I urge companies to do is dig deeper into the attributes in these tables for their company, and re-ask these questions every year as we have seen many of these market dynamics shift dramatically over time.

How to Know If New Products Are Successful

So when is a new product “successful”? Well, the answer, surprisingly, is not product/market fit. If you’ve read some of my work, you know I define product/market fit as satisfaction, normally measured by a healthy retention curve, that is through its own engagement or monetization able to create sustainable growth in new users for a significant period of time.

But second products don’t need to do all of that to matter. Whereas a new startup isn’t going anywhere unless it figures out acquisition and retention (and maybe even today monetization), new products may only need to influence one of the three to be successful. But the key is, they need to influence it for the overall company, not just the product itself. So if a second product has high retention and can effectively acquire new users, but can never inflect the growth of the overall business, it’s not successful. 

This is why developing a growth model above becomes so important. It can tell you if the new product is developing fast enough to inflect growth of the overall business, and when that might happen. And if it isn’t, you can understand what it will take for that to happen. This is something that confuses product teams that work on new products inside larger companies. By the frameworks they understand, the new product “is working.” It has product/market fit, it’s growing, etc., but it can never grow enough to really help the overall company.

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Most companies struggle to understand when they need to start investing in adding new product value vs. just continuing to grow off the traction of their initial product/market fit. But it is becoming necessary earlier and earlier in a company’s lifecycle due to a confluence of factors. In order for us to get better at building great, enduring businesses, we need to talk about the types of expansions that matter for companies, and assess at an individual company level what is required for the new phase of growth. Modeling your growth really is a helpful start, and digging deep into understanding the competitive landscape, the acquisition, retention, and monetization potential of your current business, the size and growth of your market, and what your natural adjacencies is becoming critical to make the right calls at the right time regarding new product investment. New products work when they have to. It’s time to ditch outdated portfolio practices and innovation teams, and build modern approaches around when to start building, investing hard in building new products when it is the right time, and evaluating their success or failure properly.

Currently listening to my Early Dubstep playlist.

Finding the Next Wave of Growth: S-Curves and Product Sequencing

I’ve had the pleasure of speaking at TCV’s Engage Summit the past two years. The Summit gathered ~40 CPOs and product leaders to chat through topics centered around product development and product-led growth. This year, topics ranged broadly from incorporating AI to deliver world-class consumer experiences to defining and measuring different forms of community-powered growth. I never posted my talk from last year, so I’m adapting it into a blog post here, and will do the same for this year’s talk in the following weeks and as well as some follow up questions from the Summit I’ve had a chance to ruminate on.

Also, I publish on Substack now! So subscribe here.

After product/market fit, most companies’ obsession is not thinking about how to create their next amazing product. Their obsession is thinking about growth. Specifically, how do I get this product I know is valuable in the hands of everyone it can be valuable to. Most companies have a primary acquisition loop that drives this scalable growth, and unfortunately, there aren’t that many acquisition loops that really scale. Even when they scale, they eventually asymptote, and companies need to find new ways to grow. This can be new growth loops for the same product, or entirely new products. In this post, I’ll explain how to think about the timing of that, and show some of the successes and failures of my career.

As I have discussed in previous essays, product/market fit can be hard to interpret at the time. When you find product/market fit, problems don’t go away. Customers don’t stop complaining. In fact, they complain more, because they like the product enough to care. What they stop doing is leaving. And you start being able to acquire more of them in a scalable way i.e. an acquisition loop.

Because of this and other factors, when you find product/market fit, you can’t stop iterating. Product/market fit has a positive slope. If you find product/market fit and don’t continue to make the product better, a rising competitive landscape and customer expectations can have you fall out of product/market fit over time. But when you do achieve product/market fit, while you don’t stop iterating, your portfolio of what you work on needs to change.

At Reforge, we talk about the four types of product work. Once you find product/market fit, zero to one product/market fit work goes away entirely for a while as your portfolio shifts to different types of product work:

  • Features: improve current product/market fit
  • Growth: connecting more people to existing product/market fit
  • Scaling: being able to scale the product to more users and more teams internally
  • Product/Market Fit Expansion: new segments, markets, and eventually products


The growth work in particular becomes a major focus, strengthening and discovering new acquisition and engagement loops. Most companies when they find product/market fit with their first product only have one acquisition and engagement loop that is successful, and the job of most of the team is to refine and scale those loops. At Pinterest, I was originally in charge of building a new acquisition loop built on top of Google. It looked like this:

We eventually re-architected our engagement loops to be based around personalization instead of around friends. When you stitch these acquisition loops and engagement loops together, it creates a more complicated growth model that looks like this:

The acquisition loop now feeds new users into a personalization loop that increases engagement over time, and emails and notifications reinforce that loop by distributing relevant content to users outside the product to bring them back. The entirety of Pinterest for the first few years I was there was tuning these loops in one way or another. Eventually, the company needed to layer in new advertiser focused loops to monetize, but I’ll skip that detail for now.

When I arrived at Eventbrite, the company was a lot more mature than when I started at Pinterest. But similar to Pinterest, it started with one acquisition and engagement loop driving its growth.

Creators market their events to bring in new ticket buyers. Many of those ticket buyers, once introduced to Eventbrite, start creating events themselves. And when event creators are successful at selling tickets, they come back and create more events. But Eventbrite didn’t stop there. It kept investing in making its overall growth model stronger.

Why did they do this? Well, all growth loops eventually asymptote. If you get good at modeling your loops, which basically takes the diagrams above and turns them into spreadsheet based forecasts of the impact to your business, you can start to predict when they will stop driving the growth the company needs. Modeling both helps you predict when those asymptotes will happen and unconstrain those loops by finding their bottlenecks and alleviating them. At Pinterest, we 5x’d conversion rate into signup over time, and doubled the activation rate of signups to engaged users over time as a couple of examples.

Some constraints in your growth loops can’t be fundamentally unconstrained by optimization though. The company requires either new growth loops or new products to acquire, retain, or monetize better. Modeling your loops helps you start investing in building out those new growth loops or products well in advance of when you need them to sustain your growth, because of course developing them takes much more time than improving a current loop. We think of this as sequencing different S-curves of growth.

By my arrival as an advisor by 2017 and CPO by 2019 at Eventbrite, the company had layered on many more acquisition loops onto its original loop to continue to grow, creating a much more complicated growth model.

Now, I know this looks complicated, but all that is really going on here is Eventbrite took its monetization of ticket sales and re-invested all of that money into new acquisition loops to bring in more event creators (sales, paid acquisition, content marketing). Also, Eventbrite took the increasing scale of event inventory created on the platform and started distributing it themselves to drive more ticket sales per event to places like Google, Facebook, Spotify, and its existing base of millions of people who had bought tickets to previous events.

People don’t talk enough about how much S-curve sequencing work went on at all these successful companies, so I wanted to give you a taste of what it looked like across my experience at Grubhub, Pinterest, and Eventbrite because it’s a lot, and a lot of it didn’t work. Let’s start with Grubhub summarizing ten years of decisions that both helped and hurt Grubhub as it scaled to be a public company (+’s show up where I think the decision helped, and -’s where I think the decision hurt):

  • 2004: Grubhub co-founder collects menus of Chicago neighborhood restaurants, scans them, and puts them online (+)
  • 2005: Grubhub expands to cover all of Chicago (+)
  • 2006: Grubhub launches online ordering from restaurants (+)
  • 2007: Grubhub optimizes sales model and expands into second market (+)
  • 2008: Grubhub unlocks demand side channels and refines expansion playbook (+)
    • Grubhub launches Boston and New York (+)
    • Grubhub landing pages for restaurants that deliver to X start ranking well on Google (+)
    • Grubhub unlocks paid acquisition to drive demand (+)
  • 2009: Grubhub scales market launch playbook (+)
    • Grubhub switches from flat fee to percentage model (+)
  • 2010: Grubhub launches pickup (-)
    • It doesn’t find product/market fit and hurts delivery use cases (-)
    • Grubhub now launching at least one new market per month (+)
  • 2011: Grubhub acquires Campusfood and launches restaurant websites (+)
    • Grubhub acquires Campusfood to expand to many college markets (+)
    • Grubhub acquires Fango to build in-restaurant tech (+)
    • Grubhub launches restaurant websites to drive in-restaurant growth (+)
  • 2013: Grubhub acquires Seamless (+)
  • 2014: Grubhub goes public and starts building a delivery network to compete with Uber and Doordash (+)
    • It doesn’t matter as those companies raise billions of dollars to destroy Grubhub’s network effect (-)

What you can see here is despite a successful outcome of an IPO and $7.6 billion exit, Grubhub made a lot of mistakes. If you strip those mistakes out, the sequencing of S-curves looks like:


The main lessons that matter here to me are that Grubhub tried product expansion too early with pickup. But market expansion became a major strength and well oiled machine through sales and SEO expertise as well as strategic M&A. That strategic M&A failed them, however, in responding to the threat of delivery networks. Grubhub was integrating its largest acquisition when Doordash and Uber Eats rose to prominence, and while Grubhub acquired over a dozen companies, it never acquired the one that was truly disruptive (Doordash).

Okay, let’s do Pinterest in the same format:

  • 2010-2011: Founder visits DIY/Craft Meetups and convinces Influencers to start “Pin It Forward” Campaign (+)
    • This gets people to learn how to use the “Pin It” functionality in their browser (+)
    • Pinterest uses Facebook Sign-In to bootstrap network of friends as more people join the platform (+)
  • 2011-2012: Pinterest leverages Facebook Open Graph to share every Pin into users’ Facebook feeds (+)
    • Pinterest starts to amass enough content to make discovery, not saving, primary value prop (+)
    • Retention and frequency of use improve (+)
  • 2013: Facebook turns off Open Graph and growth stops (-)
  • 2014: Pinterest fails to unlock growth with new products, but does unlock User Generated Content distributed through SEO (+)
    • Pinterest launched a maps product, a Q&A product, and a messaging product, and all fail to drive growth (-)
    • Pinterest finds another channel in Google to distribute its high quality content to after Open Graph turned off by Facebook (+)
    • Users come in with less match to existing network, so friend graph ceases to drive ongoing discovery. Retention decreases. (-)
  • 2015: Data network effects kick in (+)
    • While friend graph ceases to work, Pinterest now has the scale of content to recommend great content just based on users’ interests. Moves to interest, not friend based discovery. Retention improves again. (+)
    • Pinterest pauses all U.S. work to make sure we unlock international markets (+)
    • Pinterest tries to re-ignite user sharing and fails (-)
  • 2016: Pinterest crosses 50% international active users (+)
    • Focus shifts to building advertising business to make money (+)
    • Growth team starts seriously experimenting with paid acquisition as new channel (+)

Despite Pinterest being worth *check’s today’s stock price* $21.5 billion on the public markets today, you still see a lot of the mistakes we made. Too much new product development that didn’t pan out and too much trying to regain what we had lost vs. leaning into new areas that were working. Network effect products rely less on new product innovation unless it’s the only way to monetize. And Pinterest tried the harder expansion before the easier ones. Market and category expansion tend to be much easier than product value expansion. But, Pinterest did make a very successful pivot from direct network effects to data network effects and from Facebook to Google as the primary distribution channel. When you strip the failures out, our success looks like the following sequence:

Okay, for the last one, let’s do Eventbrite:

  • 2006: Eventbrite launches to allow event creators to accept payments online (+)
  • 2007: Event creators start putting $0 in the payment field to create free tickets, driving huge awareness (+)
  • 2008-2012: Eventbrite builds more features to help event creators run their business and includes them in ticket fee (-)
  • 2012: Eventbrite builds sales team to scale to more upmarket event creators (-)
    • Eventbrite launches new countries with sales-led strategy (-)
    • These countries never build the self-serve growth motion of the U.S.
  • 2016: Eventbrite launches consumer destination to help consumers find events (+)
    • SEO landing pages featuring events in different cities become large drivers of ticket sales (+)
    • Eventbrite begins scaling emails to consumers of events they might be interested in (+)
  • 2017: Eventbrite launches packages and acquires Ticketfly to move upmarket into the enterprise music segment (-)
    • Packages makes Eventbrite more money in the short turn, but drive churn and less acquisition over time (-)
    • Many Ticketfly customers are a poor fit for Eventbrite from a service / functionality perspective. Segment is low growth. (-)
  • 2018: Eventbrite acquires Picatic to build developer platform (-)
  • 2020: Pandemic hits, and Eventbrite rewrites strategy to focus on independent, frequent creators and help them grow
    • Focus on self-service and helping creators drive demand
    • Cancel separate music product and developer platform
  • 2021: Eventbrite launches Eventbrite Boost, a suite of tools to help creators improve their own marketing
  • 2022: Eventbrite launches Eventbrite Ads to help event creators reach more consumers searching for events on Eventbrite

Since this shift is happening in real time, I’ll describe the S-Curve sequencing Eventbrite was investing in as of the end of my full-time role. The value prop is shifting from payments and ticketing to helping event creators grow their ticket sales. Eventbrite has launched new pricing with tools like marketing tools that help event creators get better at their own email and performance marketing as well as let them get more distribution inside Eventbrite’s platform. The revenue from this will help drive more investment in the consumer product side of Eventbrite, which hopefully drives more consumers looking to Eventbrite to find things to do and buying more tickets from our creators.


Hopefully you see from these examples that sequencing S-Curves to drive growth of companies over the long term is not only quite difficult, but the craft of doing it is under-developed. All three of these companies made some critical successful moves as well as major mistakes that set them back years. I hope that by studying these and other examples startups can get smarter about how they sequence their S-Curves and drive long term success for their companies. In my next two posts, I’ll go deeper on how to think about how platform shifts like AI affect this and publish a lot more on when and how to invest in building your second product successfully.

Currently listening to my Rhythym & Bass playlist.

And don’t forget to get on my Substack list for future posts here.

New Podcast with Lenny Rachitsky

I recently joined Lenny Rachitsky on his podcast for the second time. We covered a lot of interesting topics, such as why Grubhub ultimately lost to Doordash, over-reliance on frameworks and research in product management, and a deep dive on network effects, SaaS to marketplace transitions, and why consumer subscription is so hard. Listen on your favorite platform below.

Podcast Links

Youtube:

Spotify:

Apple

Currently Listening to Raven by Kelela.

Finding Product/Market Fit with Network Effects

I have written a lot about product/market fit in the past. Whether you are a founder, product or growth leader, being able to recognize and measure product/market fit is a critical tool to make the best decisions on driving success of a company. In the post I linked to above, I make a case on the two metrics you really need to understand about product/market fit: retention and acquisition, and that product/market fit is really about finding a level of satisfaction (usually measured through retention) that will drive scalable acquisition. If you apply this model to a SaaS business, the application is usually pretty straight forward. Measure the retention of your customer, and does either the virality or monetization unlock scalable acquisition loops like sales, performance marketing, referrals, or content. Many founders struggle to apply this same framework for network effect businesses though, and it’s easy to see why. Frankly, it is just much harder. In this post, I’ll break down the nuances of product/market fit for network effects businesses to understand if you’re on the right track.

When a network is a key component of the value prop you are trying to create, and things aren’t going well, it’s hard to understand which is the real issue preventing the product from reaching product/market fit:

  • Is the product’s value prop not valuable enough?
  • Is there just not enough people or content to make it valuable yet?
  • Conversely, could there be too many people or too much content to retain the value?

I’ll break down some additional insights to product/market fit for different types of network effects to make the product/market fit mystery easier.

Cross-Side Network Effects (Marketplaces and Platforms)

Cross-side network effects can be painfully difficult to find product/market fit for because you are building out a product for two different types of customers that have to align. It can feel like building two companies at the same time. Fortunately, there are some easy ways to navigate this puzzle to make the journey to product/market fit less amorphous. The first is that for businesses like marketplaces, it tends to be easy to understand how to constrain the audience initially. The first thing to understand is that there a spectrum of network effects from global to local:

  • Global Network Effect: every seller makes the product better for every buyer and vice versa e.g. Ebay, Airbnb
  • Local Network Effect: every seller within certain area makes the product better for every buyer within certain area and vice versa e.g. Grubhub, Ritual

Based on whether you are pursuing a global or local network effect, you can usually either constrain the audience by category or geography to start: Grubhub started in one neighborhood in Chicago, for example. Amazon started with just books.

Assuming you pick the correct geographic or categorical limit, you can test the value prop and scaling of supply and demand to find liquidity, which is a synonymous term with product/market fit for marketplaces. There is almost always a quality and quantity element to liquidity. If you understand it, you can then measure product/market fit by just analyzing retention and acquisition for both sides of the market.

Platforms are more complicated in the beginning, but eventually follow the same logic as marketplaces; they are just less likely to have a geographical component at the core. Whereas marketplaces need supply to attract demand and are willing to do unscalable things to generate that initial supply like paying drivers to sit idle for Uber and Lyft or delivering from restaurants who are unaware they are on your platform like Postmates and Doordash, platforms tend to have to be the initial supply themselves. Let’s take a look at a couple of different examples. Nintendo has had some of the most successful hardware platforms in gaming across decades. One of their secret sauces is that they seed supply on their platform with games they build themselves that they guarantee are high quality. Now, they can afford to do that with games that are based on incredibly valued IP like Mario, Zelda, and Pokemon. This creates initial demand for a new hardware platform like, say, the Nintendo Switch, that then attracts third party developers to build games for the platform as well. Then, Nintendo can look at demand side retention through purchases of games, and supply side retention through creation of multiple games. This helped them understand that platforms such as the Wii and the Switch had strong product/market fit, whereas the Wii U and GameCube much less so. 

The success of the Nintendo Switch was partially due to the console launching with one of the best reviewed games of all time from a beloved franchise: The Legend of Zelda: Breath of the Wild.

On the software side, Shopify and WordPress have spent decades building their own SaaS features to allow the platform to become stronger and stronger with other features built by external developers they would never consider in their roadmap. Now, the majority of the value may be generated by third party developers, but a lot of first party development was required to get them far enough along in customer acquisition to make it attractive for so many developers to build on top of their platforms. You can read more about building successful platforms here.

Data Network Effects (Personalization and Recommendations)

For products that expect to create a better experience through data, the journey to product/market fit is usually about collecting enough data to provide a compelling experience. For some products, this takes a massive amount of time. Pinterest, for example, started as more of a tool for individual users to collect cool things from the internet before it could become a real recommendation engine for things related to your interests based on the wisdom of the crowd. Tinder can recommend great matches after just a few swipes. After I joined Pinterest, we had to make a hard pivot to onboard new users based on topics instead of their friend graph as we realized there was stronger product/market fit as a content recommendation engine vs. a social network. This creates an issue for geographical expansion. By default, we would not have enough data to understand local tastes. To rectify this, we had to reconfigure our feeds to emphasize the local content we had, in the local language, and in the local way of measuring (no more imperial measurements!). And in some markets, the focus needed to be gathering content so we could even have enough local content to display.

Direct Network Effects (Communication Tools and Social Networks)

For many network driven businesses, especially in the social realm, there is a Goldilocks zone of user experience. Too few people or content, and there isn’t enough content to consume or people to communicate with. Too many people or too much content, and it becomes overwhelming, or generates too many notifications. When Yogi Berra said, “”Nobody goes there anymore. It’s too crowded.”, it’s easy to imagine he was talking about Clubhouse, not a local restaurant at the time.

Clubhouse downloads per month. This graph is not up and to the right.

For a marketplace, there is usually a clear way to constrain the audience to generate appropriate retention metrics to evaluate product / market fit. Geography or category is the dominant mode for this in marketplaces. For SaaS, it’s a specific target audience. But social or content driven networks eventually want to scale to everyone, and a lot of times founders or leaders can’t tell which should be the first audience that will hook onto it. The better your thesis, the easier it is to evaluate e.g. Harvard only with Facebook, midwestern moms with Pinterest, USC with Snapchat. So the dominant strategy for building direct networks effect business is to have a strong thesis for the target market, prepare to be surprised both who the target market and right features are, and shift to another type of network effect over time. Outside of communication tools, it’s usually my belief that direct network effect products are actually cross-side network effects products where it’s just too early to understand who the different sides are. So, founders have to understand the user data or have a strong vision for what type of cross-side network effect or data network effect can be created to scale product/market fit over time. Do this, or risk becoming like a lot of other flash in the pan social network fads that failed to scale. 

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My previous post on product/market fit mentioned the Ries vs. Rabois model on finding product/market fit, which you can think of as testing vs. vision. While both concepts have value, attempts at finding product/market fit with network effects businesses tend to reward more of the Rabois model, or else the testing you do will be inconclusive on the reasons why something isn’t working between the core value prop vs. the size of the network. Finding product/market fit for these types of businesses is much harder, but can be much more rewarding in terms of scale if successful. 

Currently listening to my Hipster House / Lofi House playlist.

Five Ways to Address Complexity In Your Product

In Crafting The First Mile of Product, Scott Belsky talks about the product lifecycle. In it, Scott states:

  • Users flock to simple product
  • Product takes users for granted and adds more features for power users
  • Users flock to simple product

Now, myself and others have written before about different ways you should attempt to defy this second step. Let’s say you’ve listened. How do you then keep your product simple while trying to grow, capture new audiences, and add new value props? Is the solution to… not do any of that, like a Craigslist? Seems like that doesn’t work too well given all of the companies that have attacked Craigslist from different angles and built bigger businesses than it. Is the solution to ignore Scott’s warning and rely on network effects or some other deep switching costs to retain users? This isn’t a bad idea, and why companies like Facebook continued to grow despite adding more and more complexity over time. During this time though, Facebook users also flocked to Instagram, Snapchat, TikTok, et al. Is the solution to rely on humans to help customers navigate the complexity? Sounds expensive.

This is one of the key challenges we faced at Eventbrite when we radically shifted our strategy in 2020. Eventbrite was historically a 50% sales and 50% self-service business. The optimal outcome for a business like that is a “good enough” user experience with account managers that can make up its flaws. This is a very common strategy for enterprise companies with large margins. The problem for Eventbrite is we weren’t an enterprise company with enterprise margins. We work with small businesses and independent creators. Talking to humans is a bug, not a feature of what to them should be an intuitive user experience. And these SMB’s and independent creators don’t pay us millions of dollars individually to profitably employ armies of human help.

So, as we decided to take a bet on building an intuitive, self-service experience instead of masking user experience issues with human support, we really had to confront Belsky’s product lifecycle for the first time. Eventbrite over the course of the last decade built out a multitude of different features for all different types of event creators, of all shapes and sizes. We did not have a simple product for event creators at all. It had become quite complex.

When people think of simple products, they typically think of consumer products. That is usually where one looks to find the current peak in user experience. There has been a renaissance of user experience and design in B2B use cases over the last decade, but those typically revolve around single use case products, like:

  • Syncing files in Dropbox
  • Sending emails in Mailchimp
  • Setting up a website in Wix or Squarespace

Creating an event on Eventbrite should feel like that; the problem is how vague the definition of event is. Eventbrite has small meetups, large conferences, niche networking events, merchandise drops, music festivals and everything in between. If you can think of it, we’ve ticketed it. There are only a few types of files to sync, emails to send, or website use cases. Our cases were myriad.

So, how do you solve this problem? At Eventbrite we have surveyed a few different approaches I‘ll showcase below as well as what we think works best for our use case. Our initial approach to solve this problem was to just put the creator first. We designed something we called the adaptive creator experience that learned what type of creator you were, what features you valued, and automatically customized the experience for the features you needed front and center. This made for a great vision, but was practically untenable from a data or scale perspective. So what are the practical approaches to solve this problem? Let’s cover each below.

Approach #1: Validate and Unbundle (Temporary Complexity)

When Eventbrite acquired Ticketfly, we originally attempted to separate the experience into something we called Eventbrite Music. There, music specific features wouldn’t complicate the experience for, say, someone doing their first event for ten people. The more we learned about the Music space, the more we learned it wasn’t the features that needed to be radically different, though that sometimes was the case. It was more that the aggregate user experience that music clients, especially more traditional ones, wanted was incompatible with a self-serve user experience. They wanted very detailed interfaces, dedicated training for dedicated employees that only worked on that part of their business. The concept of creating not different features, but different interfaces, felt like a much larger complication to support. Eventbrite now caters toward more modern music creators that share the need for intuitive and self-service experiences. With Eventbrite’s new strategy, we didn’t really see an unbundling approach based on functionality given our two products on the creator side (ticketing and marketing) are already so intertwined in creators’ workflows, and we no longer differentiate creators by vertical as it didn’t map to product needs well.

Facebook was a different story. One thing Facebook did very successfully as it scaled functionality was to prove out the value of features in its core app and then unbundle them into separate apps later on. This keeps their user interfaces, especially on mobile, more focused and easier to navigate. Facebook has now done this with multiple features across Facebook and Instagram. It hasn’t always worked, but that is usually because the product/market fit of the product isn’t always strong enough to survive on its own e.g Facebook Local (failure) vs. Facebook Messenger (success). Uber did the same exact thing with Uber Eats. I have written about this strategy before here.

The pros of this strategy seem pretty obvious. Leverage the scale of the initial product experience to expose people to the new value prop, confirm product/market fit, then move that new product experience elsewhere so the new value prop’s added complexity doesn’t deteriorate product/market fit for the initial product. The issue with this mentality is that once a product is unbundled, it no longer receives as much new user acquisition from the initial product it was built inside of, and sustainable acquisition loops are a key part of product/market fit. Facebook has notably not spun out Facebook Marketplace or Facebook Watch, likely for this reason, and sunset Facebook Local after initially spinning it out. Many app developers tried to launch multiple apps as part of a trend called app constellations, and pretty much all of them failed because user acquisition is really difficult, or they failed to create product value (read more about this here). 

Approach #2: Progressively Disclose (Temporary Simplicity)

One of the key strategies we took at Pinterest to solve the first mile problem was to remove functionality from the initial experience to make sure new users could learn the core concepts. Advanced features like group boards and messaging were not available to new users until we saw that they understood how to save content and access their boards. Once we confirmed the user activated, we started to give them access to the entire product, confident they could handle the increased complexity. This is a form of progressive disclosure to prevent new users from being overwhelmed, but only delays the complexity problem to beyond the activation period. To be clear, this was a very successful strategy for Pinterest, and a dominant approach to new user activation, which is why so many growth teams have dedicated activation or onboarding teams that leverage techniques like this. But it only delays the inevitable complex product in the hope that users are better prepared for it. This is a particularly ineffective strategy when there are more permanent differences between the complexity needs of different users, more common in business use cases like ours at Eventbrite.

The inspiration we were able to take from this approach is progressive disclosure work typically calls into question whether certain features should exist at all. Eventbrite had accrued many features of questionable value because a creator here or there used them. We started aggressively deleting such features in 2020, which helped make the product and code base less complex. We had much success with deleting features entirely at Pinterest as well, and I have written about both feature deletion and successful onboarding in the past. The next phase of leveraging this concept for Eventbrite is radically simplifying our onboarding flow to help creators understand what value we offer before they have to switch their entire business over to it. This is a big investment that will take multiple quarters to get to a great spot, but it is worth it. Still, it doesn’t fully solve Eventbrite’s complexity problem.

Approach #3: Train the User (Hacked Complexity)

Every designer strives for an ultimately intuitive user experience. And I’m sure we’ve all seen that quote that say if a design needs an explanation, it’s a bad design. I often think, has anyone who’s said that quote tried to design software before? This stuff is hard! My preferred saying is a design with education is better than a design that doesn’t educate. Having this aspirational north star of intuitiveness is important for any design team, but it’s okay to admit you’ve fallen short of that lofty goal and leverage other tools to set up users to be successful. Using people and or prompts in the experience to ensure users are successful is not shameful; it’s smart. Eventbrite is in the early stages of leveraging proactive communication and still learning, but we have found that contextual prompts or offering to get on the phone with creators that have demonstrated they intend to use the product at scale can be pretty impactful. People-based strategies do not scale, but they can at least be profitable if they are gated on the value of a customer.

Approach #4: Segment User Experiences (Optional Complexity)

In business use cases, it is less likely that the average user matters, and instead, there are different levels of complexity required for different users. This can be admins vs. normal users or small vs. large accounts to give a couple examples. The more standard approach today to dealing with the very differing needs by user type is to proactively set up user types as part of a complex team-based onboarding, as is common with enterprise products. For products that achieve bottoms-up adoption, this is more likely to be achieved by different packages that segment different types of users. For example, a base package may only have a few features and a low price, and a professional package may have more complex features that would only confuse base users, but are valued by professional users so much that they are willing to pay more than the base package for them. This can be pretty successful when segments are easily identifiable, but when segment needs diverge from clear product delineations, it can create issues. Also, managing separate user experiences by user segment can be hard for engineering and design teams to scale.

Eventbrite launched a more realized package framework in 2017, and found that it failed to map to the different types of creators as elegantly as they initially thought. It turns out features cannot be mapped that easily to different segments just by scale, and that package changes had implications on the entire Eventbrite growth model, not just monetization, since so many creators start initially as ticket buyers in the ecosystem. Segmentation is something that is mapping more neatly to Eventbrite’s marketing tools, which are frequently purchased in a subscription. They work less well for Eventbrite’s ticketing business that deals in transactional costs where features help drive extra sales.

Approach #5: Make Advanced Features Discoverable (Perceived Simplicity)

Segmenting user experiences addresses differing levels of complexity needs when there are easy to identify segments, but what if the same user needs the simple product most of the time, but more powerful features only occasionally? The package based approach will present the user the complicated product every time even though the base product would be a better experience for them most of the time. 

A solution many self-service products attempt is to preserve the simplicity of the core product, but make that additional complexity immediately available on the rare occasion it’s needed. WhatsApp is a great example of this in consumer products. The main interface of WhatsApp is optimized around text-based messaging. It is simple to view chats and reply, and to most users, they need no education to figure out how to do this for the first time. However, WhatsApp actually has a lot more powerful features than this. You can record messages, call people directly via audio or video, leverage emojis, attach images, and take pictures. When you need one of these advanced features, I bet it takes most users less than a second to find them in the interface, but these features don’t crowd the interface for the baseline use case of text messages.

It is very difficult to preserve this level of complexity while preserving a simple interface, and WhatsApp may be the best I’ve seen at it. But it’s important that designers strive for this level of intuitiveness in the face of product evolution, and not retreat to lazier methods that denigrate both the user experience and business performance. Square at one point redesigned their interface to make it a lot simpler, hiding most advanced features behind various settings. The new interface was simple, but users could no longer find a lot of the features they wanted to use, and business metrics suffered. That is not what success looks like. Britelings are probably tired of me using the WhatsApp example, but it is our north star for how we tried to build creator products. Simple for the 99%, surprising and intuitive power for the 1% use cases.

The higher the ARPU, the more you can use direct contact to train users. The lower the ARPU, the more scalable your solution to complexity needs to be.

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There is no easy way out of the product lifecycle. Like scaling a culture, it requires a lot of intentionality to scale a product without losing the simplicity that drove so many people to it in the first place. At Eventbrite, we continually strive to make our user experience powerful, yet simple, and we frequently fail to achieve our own expectations. Hopefully, the approaches above help give you some options to manage complexity in the user experiences you own to improve the value for your customers.

Currently listening to my Uptempo Instrumental Hip-Hop playlist.

Podcast with Lenny Rachitsky

Lenny Rachitsky recently launched Lenny’s Podcast, and I was happy to be a guest. We talk about how to communicate upward, different product design strategies for complex products, what it means to be a product leader, and much more. I’ll expand on some of these in upcoming posts. You can listen to the podcast here or on Spotify below.

Currently listening to my Downtempo House playlist.

How to Justify “Non-Sexy” Product Investments

A common issue leaders in product management, design, or engineering face is justifying investment in the “non-sexy” stuff. What is not sexy can differ by company, but usually the sexy things are new products and few features. Non-sexy things include general user experience improvements, performance, developer velocity, infrastructure, technical debt, and, fortunately less than it used to be, growth. I’ll walk through some frameworks and examples from my career on how to drive excitement and investment in these critical areas that may not be properly valued or staffed currently in your organization. But I urge everyone I can in product to develop the intuition to support these initiatives without making teams jump through hoops to justify these investments.

User Experience

The most common path product teams are on today is that they go from feature to feature trying to add new functionality, never confirming their feature actually adds value, and never improving features over time or updating experiences to be more modern as the world evolves. Designers complain about how stale certain experiences get over time, but improvements never make the roadmap. Product managers think designers are whining about things that aren’t important versus their current OKRs. 

Why are the designers right in this instance? Well, they aren’t always. It is possible to over-design and do things that feel good and look excellent, but don’t materially help your customers or the business. Polishing too often can be just as bad polishing too little as you don’t deliver enough new value for customers. While over-polish does happen, why designers are mostly right is they intuit something about product/market fit that is hard to measure on a metrics dashboard: that expectations of customers increase over time. Another way to say that is product/market fit has a positive slope. If you do not consistently improve your product or feature, and customer expectations continue to increase, your product or feature can fall out of product/market fit over time. Many business strategists talk about companies being in a Red Queen effect with their competitors. This means they have to run really hard to stay in the same place competitively over time. But what many product teams misunderstand is that they are in a Red Queen effect with their customers to maintain product/market fit as well. Consistently improving the user experience helps products stay above that positive sloping curve of product/market fit. Let’s visualize this by borrowing a graphic from my product/market fit essay.

 

In the above graphic, the customer expectations line is the point at which customers stop complaining about elements of a product. That is not the target for product/market fit. The target for product/market fit is the purple line where customers stop leaving a product. Teams invest in products and features to get them above the purple line, but failing to continue to invest in them beyond that point means expectations for product/market fit will eventually exceed what has been built without continued investment. 

The dotted line is a worst case scenario as it happens in a way that is not measurable, but once those hard to define lines cross, every metric gets worse. So, in prioritizing user experience improvements that scale with customer expectations, the net effect you see is no impact in business metrics. But the effect of not doing these investments means business metrics will decrease over time. This practically means that teams that make investments feel like the investment didn’t “pay off”, but in reality it prevents the possibility of dramatic issues for the product down the line.

On the growth team at Pinterest, Kaisha Hom and Lindsay Norman on the growth design team intuited this, but had trouble convincing a very metric-oriented team on the value of this investment. Eventually, we decided that one of our key results would be a quarterly audit (and refresh if needed) of our top five user flows. The expectation was no material impact on growth, but instead prevented potential growth issues down the road. 

At Eventbrite, we have gotten a little more sophisticated in how we manage this. Adele Maynes, who leads our research team, helped craft a survey that measured different components of our product/market fit, including:

  • Ease of discovery
  • Ease of use
  • Ability to self-serve
  • Product fit
  • Likelihood to recommend

We also created this survey for some of our key features inside the product so we can understand their feature/product fit better. Our new strategy is to be a fantastic self-service experience that rivals the best SMB tools on the market, but we know we have a long way to go to get there. Investing in user experience is a key driver of this strategy, and these scores help us know if our overall product and specific features are on the right track. CRPX is now one of the top level key results for the product team.

Sample analysis of Eventbrite’s Creator Product Experience Score (CRPX)

Performance

Performance, roughly meaning how long it takes for software products to become usable to customers who load them, tends to become a problem at scale without concrete investment. Products become bloated, the number of different types of users and use cases multiply across countries and categories, and the number of frameworks engineers are leveraging to deliver experiences rises exponentially. We actually have pretty good data externally on the impact of performance. There are many studies that show additional milliseconds of load time impact things like conversion to purchase and engagement on many websites and apps. 

A big problem is actually addressing performance issues at the start tends to be measuring it well, across different pages, apps, countries, use cases, etc. Obviously, this is normally the place to start. But sometimes even shockingly high metrics in certain countries or at the edges can’t motivate teams to scrap their current OKRs for performance work. 

On the growth team at Pinterest, we were struggling with some performance issues of our home grown frontend framework. After trying to rally the company around this work and failing, we decided to leverage our skills to prove out the value of this work. A small team of engineers led by Sam Meder decided to work part-time on a performance initiative just for our logged out experiences, migrating to React, server side rendering, lazy loading, spriting – all the usual suspects from a frontend performance perspective. They ran these changes as AB tests to show the impact on user engagement and key business metrics. The result was a 30% decrease in user-perceived wait time, which resulted in double digit= increases in traffic from Google and conversion rate to signup. The impact was enough to get our CEO to push this as an organization-wide initiative the following quarter.

Developer Velocity

Shortly after I joined Eventbrite, I ran into Omar Seyal on the street. Omar was the Head of Core Product at Pinterest at the time. As I said hello and asked him how things were going, Omar, always to the point, remarked, “Pinterest doesn’t understand leverage!”. He then went on to say how he was struggling to get Pinterest to invest in its infrastructure so that engineers could move faster. In my head, I thought, he doesn’t know how good he has it compared to Eventbrite. Startups, or companies that emerge from startups, tend to prioritize new customer value and growth at all costs. This not only can create a lot of technical and design debt that will slow companies down for years to come, but it also prevents them from seeing “what got you here won’t get you there.” At a certain point in a startup’s lifecycle, it has to shift from growth at all costs to balancing growth and long-term scalability. Yes, you could spend 90% of your time building new things when you were small, but that won’t work when you’re big and have dozens of things to maintain. 

A belief Omar and I share is that developer velocity is the purest form of leverage in a software company. So, it follows, investments in things that make developers more productive are the highest leverage investments a company can make. Sure, those investments don’t translate into customer value directly, but they enable each developer to build more customer value. That can mean more features, more experiments for a growth team, whatever the company needs to maximize long term growth. The key question I think non-developers fear is that these are just quality of life investments and don’t actually meaningfully improve the amount of value to customers. After all, you’re spending less resources on value to customers in the short-term whenever you look inward at internal tools.

What we did at Eventbrite to confront this narrative is we built a measurement plan and a goal. First, we measured the amount of downtime our developers experience on a quarterly basis for various issues. We then stated that with investment we could decrease that downtime, freeing up more capacity to build value for customers. We then set a goal. By making these investments, James Reichardt and Dan Peterson, our leaders in platform engineering and product, argued we could free up the equivalent of 15 new engineers’ worth of capacity at the company. In the end, those investments freed up 18 engineers’ worth of capacity. We confirmed this with “end of sprint” reporting on different teams on the amount of what they were able to deliver. If those numbers aren’t improving over time, you’re probably under-investing in projects related to developer velocity.

Developer Downtime

Engineering downtime was actually trending upward, but by working on our tooling we were able to save hours per engineer per week.

Growth

As much as I’ve written about the rise of growth teams and how growth teams work, the concept of investing in things that help connect customers to value instead of building new value is still pretty nascent in the software world. I speak to product managers and leaders all the time that are struggling to get investment in areas that could help inflect their growth. We definitely faced some of these same issues when I started at Pinterest. While we had a dedicated growth team, many parts of our growth model felt under-optimized, but also hard to measure or justify investment for.

One of these areas was search engine optimization. A few months before I got to Pinterest, Pinterest had “no indexed” the entire site, leading Google to email us to confirm that was what we wanted (it wasn’t). Anna Majkowska jumped onto the problem, but was only able to secure a few part-time engineers to help her. I joined shortly after as the PM, and we worked together on a plan to improve SEO for Pinterest as we believed that to be a large growth opportunity. The problem was we were on a growth team that ran every change as an AB test to show the improvement in growth. With SEO, you can’t run AB tests because it’s one Googlebot instead of millions of separate users. Julie Trier, a part-time engineer on the team at the time, said we had to develop an SEO experiment framework like we use for other parts of the growth team, and set out to build it. With this initial version, instead of showing different users different experiences, we changed parts of the experiences on some pages and not on others and measured the traffic change from SEO. The framework worked, and helped us justify SEO investments by showing how much extra traffic we received from making changes. 

More traffic was great, but the issue was that users from Google would just look at all our cool content and leave. Conversion rates were very low. Conversion was managed by another team. So I went to them and explained the opportunity. They said they were busy working on a home page overhaul and couldn’t look at it. So I said we’ll take on the work ourselves. By then Jean Yang had joined the SEO team and ran an experiment that increased traffic, but decreased sign ups. How that was possible was by making a new page available to Google, we removed a signup modal blocking logged out users from accessing it. It turns out people signed up when they saw that modal, so we hypothesized maybe we could trigger that modal when you clicked on an image when you didn’t have an account. Also, we thought the other thing that indicates you like what you’re seeing and should sign up besides a click on an image is scrolling down and viewing more images. We already restricted Google from seeing more than 25 images on a page, so we decided to make the same change with users, with a modal coming up from the bottom saying to sign up to see more. 

It took Jean two days to implement the experiment, and the result was a 50% increase in conversion rate to sign up. Every graph at the company kinked up as a result. I got a message from Tim Kendall, our Head of Product, asking “What did you do??”. I thought he might fire me, but instead he used the data to go raise more money at a higher valuation showing investors we could inflect our growth. Don’t under-estimate the power of proving it by going rogue or making the measurement investment others think isn’t worth it. It can turn subjective conversations into objective ones very quickly. The team grew dramatically after this with Julie eventually leading a platform team for growth tools.

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These are just a few examples of how teams were able to make the investment case and prove the value of “non-sexy” projects to make a big impact. Of course, what tactics work for you will depend a lot on your company’s culture, but one thing that will likely mimic these stories is teams working together to make both the case and execute on the investment. Building products is a team sport, and the more cross-functional the support you achieve, the more likely you are to succeed. 

As I relay these types of stories, it’s easy for people to say something to the effect of “sure, that worked at Pinterest or Eventbrite, but it could never work here” without realizing the point of the story is that almost all companies have these types of issues. The question is whether you are willing to put in the work to try to change the narrative to help your company grow. Those that do typically are rewarded and reward their customers in the process.

Currently listening to my Trip-hop playlist on Spotify.

What Type of Job is This: My First Year as Chief Product Officer

I have written about the Chief Product Officer role in the past, and why the job is so hard. I also wrote about being a product leader during a crisis. But not much is written about starting as a new product leader. So, I thought I’d write a post about my first year as CPO, and share some general lessons. First, a reminder of the situation I started in. I had been an advisor for Eventbrite for about two years, so I had a lot of comfort with the CEO and many of the executives before ever starting the role. I believe this is an underrated way to start new roles for senior people because you can de-risk the culture fit and alignment issues that plague many new executives. When I started advising Eventbrite, the company had a business unit structure, so it didn’t even have a CPO role, but product leaders embedded into different business units. The company reorganized functionally, and created this role and asked me to consider it.

What Type of Job is This?
I believe the most important question a product leader needs to ask when they get started is what type of job it is they have to do. I wrote in the past that there is frequently a misalignment on vision vs. execution roles. There may also be a misconception of what type of product work is needed to help the company i.e what the product strategy should actually be. In the Reforge Product Strategy course, we teach that there are four different types of product work:

  • Feature development: adding new things to the product that improve value proposition e.g. Uber’s Split Fare
  • Product/market fit expansion: adding totally new products that create new value propositions e.g. Uber Eats
  • Growth: tuning the product experience so more people can connect to the current product’s value prop e.g. Uber improving driver onboarding
  • Scaling work: tuning the underlying technologies or process to help the product and team continue to be effective e.g. Uber rearchitecting its data pipelines

Old school product leaders would just do their preferred type of product work even if it wasn’t what the company needed, or adopt a primitive portfolio approach to the four types of work even if part of the portfolio was wasted work e.g. building a ton of features for a network effects business, or doing a lot of growth work for a pre-product/market fit product. As a modern product leader, it’s important to understand based on the company and its lifecycle, what type of product work has leverage, and these crude approaches are usually not the best approach.

Usually, the best place to start is looking at what the company is actually working on right now. In Eventbrite’s case, the company was:

  • Integrating the acquisition of Ticketfly to move up-market in a specific vertical and build an enterprise sales motion
  • Building a consumer marketplace to drive incremental ticket sales to event creators
  • Paying down technical debt with duplicate versions of Checkout and Create
  • Launching a developer platform so external developers can add more features for Eventbrite’s broad base of event creators
  • Launching new SaaS products with its incubation arm

Julia, our CEO, had told me she wanted me to focus on growing the self-service business faster. So, first off, what you should notice is that there are too many things going on for a company of Eventbrite’s size (sub one thousand people). In other words, the product strategy lacked focus. So, I had to spend my first few months understanding these different strategies to understand which ones to focus on. So I gathered as much information as I could about these different strategic initiatives, as well as digging into the core self-service business.

What Was Going On With the Core Business?
The core self-service business was growing steadily at significant scale and was profitable. Most of the sales clients we brought in stressed our product/market fit, which we compensated for with manual services at no charge, straining margins. We didn’t have a good sense of who our self-serve customers were, how we acquired them, or what retention looked like. As we dug into these questions, we found that while Eventbrite’s product/market fit was strongest with making it really easy to host a single event, but the bulk of our growth and profit was coming from frequent creators hosting small events very often. So, while the product roadmap was scaling for size of event, the market was scaling with frequency of event. The product did not handle this frequency very well, causing these event creators to hack the product to get what they needed, and a higher churn rate over time as those hacks proved problematic to execute. The gaps in our product to strengthen product/market fit for these creators didn’t seem insurmountable, but none of them were actually on the roadmap.

We also were able to get a clear picture of the core competencies and competitive advantages of the Eventbrite product. The fact that Eventbrite supported events of all types and wasn’t focused on one vertical e.g. conferences meant the company had a scale of data no other company had. Secondly, the self-service acquisition model meant the product had very low acquisition costs overall. That model was also a good fit for many different types of creators. Lastly, the company had leveraged its scale of events to drive consumer demand through channels like SEO, emails to previous ticket buyers, and distribution partnerships with companies like Facebook and Spotify.

What Did the Team Say?
As I talked with the team about the state of the product and what they were actually working on, let’s just say the team had a lot to say. Breaking it down by project:

Upmarket Music Vertical Expansion
We tried to integrate music customers too quickly into the Eventbrite platform, and we were much further away from product/market fit with the more traditional enterprise approach those customers were used to than we thought. The space is low growth and low margin, and relies on enterprise sales, relationships, and high touch human service, which doesn’t match our self-service capabilities well.

Consumer Marketplace
Frequent creators drive most of the inventory consumers are interested in, and if frequent creators’ efficiency tools on Eventbrite don’t work for them, they will leave the platform even if they sell extra tickets because of the platform. This is an interesting strategy, but needs to be sequenced after we have a great product experience for frequent creators.

Technical Scaling
Internal developer productivity was incredibly low due to low level of investment in developer tools. Our infrastructure was rickety and frequently had stability problems during big “on sales”. Multiple versions of every feature made it hard to build new things quickly and at high quality. We never deleted features because some sales clients use them and would complain. Everything we build is an MVP, and we rarely iterate.

Developer Platform
While the strategy of leveraging external developers to build specific features for a large array of customers with different needs makes sense at Eventbrite’s scale, we internally lacked the capability to service our own engineers well, much less external developers.

New SaaS Products
Many of these products are very far away from product/market fit and do not have a path to scalability. There is one partnership related to creator marketing tools being run out of this program which is doing well though, and it has been easier to talk with creators about that than our marketplace demand.

Developing A New Product Strategy
Strategy is about making choices among many options that optimize across a few key dimensions like:

  • Company Focus
  • Business Model
  • Target Customer & Market
  • Competition
  • Core Competencies & Competitive Advantages
  • Consequences & Risk
  • Sequencing

Eventbrite failed to make a lot of hard choices with its product strategy when I arrived, so it was time to make some tough calls on what to focus on. There are no simple answers here, but in evaluating the initial strategy, it became clear we should do the following:

  • Upmarket Music Vertical Expansion: We are too far away from product/market fit trying to rebuild the Ticketfly model, and there is little margin or growth to be had once we get there. There is a lot of competition, and the go-to market approach leans out of our core competencies. It felt like we were trying to win the music industry’s last war instead of building a more technology-forward experience many up and coming music venues would appreciate. We need to focus our music creators towards a self-service experience like the rest of our product, and if that means that some of the less tech savvy customers won’t come with us on that journey, that’s okay.
  • Consumer Marketplace: The product needs to have a good experience for frequent creators before they will value our demand, and we should probably help them improve their efforts to drive their own demand first. Sequence to this strategy when frequent creators are in a good state.
  • Technical Scaling: Developer velocity is the purest form of leverage in a software company. We should be investing more in this area so we can increase our strategic appetite over time.
  • Developer Platform: If we are not providing a great experience for our own developers to build great features, we are even less likely to provide a great experience for external developers. Pause until our technical infrastructure is in a much better place.
  • New SaaS Products: Creators drive the majority of ticket sales through their own marketing efforts, and they are not expert marketers. Our knowledge can help them improve and automate their efforts. Cancel everything else in this area.
  • Core Self-Service Growth: Make the product experience great for frequent creators of small events as they drive most of the profit for the core business. We are not far away from strong product/market fit here.

The new product strategy is remarkably simpler and more sequenced over time:

2021

 2022

2023

Frequent Creators

 Marketing Tools

Consumer Marketplace
Technical Scaling Technical Scaling

Technical Scaling

Frequent creator investment will be measured by improved frequent creator retention. Technical scaling will be measured by internal developer velocity and our say/do ratio. Marketing tools and consumer marketplace will both be measured by revenue from those sources. So, going back to what type of job this is, my initial directive would have made this product leadership role to be primarily about growth. Instead, the focus is on scaling with some product/market fit expansion. 

Your Product Strategy Probably Isn’t That innovative
One dirty secret behind the work of many executives and product leaders is that our strategies aren’t that innovative. There are a few playbooks we generally run to improve performance in companies depending on the business situation after we’ve gathered the right insight. You can run through them and rule most of them out like the con men strategies in Ocean’s 12:

Yes, product leaders also rule out strategies because we don’t have enough people or can’t train a cat that quickly.

The new Eventbrite strategy was a combo of two common strategic playbooks. The first part of the strategy is what Chris Zook calls “profiting from the core”:

“The greatest strategic error stems from an inaccurate understanding of the core and its full potential.”
-Chris Zook, Author of Profit from the Core

However, if you’re an Arrested Development fan, you might call it the “there’s always money in the banana stand” strategy. The idea behind this strategy is that many companies as they scale pursue too many expansion strategies and leave behind growth that is closer to their initial core business, plays more to their core competencies, and requires less work and less risk to execute. Eventbrite was pursuing expansions in verticals (music), business model expansion (SaaS), and value props (driving demand) while ignoring improvements that could help the growth of the core product (features for small, frequent creators). At Pinterest, VP Product Jack Chou ran a version of this he called “make the basics great”.

The other component of the strategy is probably most known from a blog post (and soon to be book) by current Snowflake and former ServiceNow CEO Frank Slootman. In Amp It Up, Frank Slootman basically divides up his strategy into three elements:

  • Improving velocity
  • Raising standards
  • Narrowing the focus

Personally, I would flip the order and revise the language to be more software specific:

  • Improve focus
  • Raise quality bar
  • Reduce tech and design debt (usually the biggest hurdle for velocity inside software companies)

By the way, if you’re a public market private equity investor, and you aren’t running this strategy on every sub-rule of 40 tech company, I have a question for you.

So, in Ocean’s 12 language, Eventbrite is running a banana stand combined with a Slootman Special. We… may need to work on these code names. Recently, Etsy has run this same strategy combo to grow its market cap from $2 billion to $25 billion in four years after many years of no market cap growth at all.

There is one other element to Eventbrite’s strategy, and that is presented by the table above: sequencing vs. parallelizing. There is a reason Eventbrite started to pursue a lot of these adjacent opportunities in the first place: fear the core business could not grow itself fast enough. But in trying to pursue multiple adjacencies at the same time, it not only failed to make the progress it wanted on any of them, but many were not set up for success because they would gain from other strategic elements of the plan already having been completed.

The goal of this post is not to geek out on all the generic strategies, though I could do that all day, but to give a sense of the work new product leaders need to do to understand strategy and make it explicit to the organization. Frequently, there is a mismatch between what the customer or business needs and what the team is working on today. Usually, by talking to the team, your customers, and looking at the data, you can identify the mismatch and position the team toward a more likely to be successful product strategy. Then, product leaders can move to the meat of the role, which is building and optimizing the structure and processes of the team to execute against that strategy more effectively over time, or adjusting to changing market dynamics *cough* pandemic *cough*.

Currently listening to the Housewerk EPs by Tusken Raiders.