Tag Archives: retention

What Is Good Retention: An Exhaustive Benchmark Study with Lenny Rachitsky

At the end of 2019, I presented Eventbrite’s product plans to the board for 2020. These plans included a lot of the goals you likely have in your company: improvements in acquisition, activation, and retention. One of our board members asked: “I understand these goals for the year. But long term, how high could we push this retention number? What would great retention be for Eventbrite?”

I actually didn’t have a great answer. Soon after, I was chatting with Lenny Rachitsky, and we decided to embark on a holistic study across the industry to ask “what is great retention?” across business models, customer types, etc. Lenny surveyed a lot of the top practitioners in the industry across a variety of companies, and we’re happy to share the results here. You can see the raw data below, but I recommend reading Lenny’s analysis here. Done? Good.

Why is retention so damn important?
Why are Lenny and I spending so much time researching retention? Because it is the single most important factor in product success. Retention is not only the primary measure of product value and product/market fit for most businesses; it is also the biggest driver of monetization and acquisition as well.

We typically think of monetization as the lifetime value formula, which is how long a user is active along with revenue per active user. Retention has the most impact on how many users are active and lengthens the amount of time they are active. For acquisition, retention is the enabler of the best acquisition strategies. For virality or word of mouth, for example, one of the key factors in any virality formula is how many people can talk about or share your product. The more retained users, the more potential sharers. For content, the more retained users, the more content, the more that content be shared or discovered to attract more users. For paid acquisition or sales, the more retained users, the higher lifetime value, the more you can spend on paid acquisition or sales and still have a comfortable payback period. Retention really is growth’s triple word score.

What are effective ways to increase retention?
Okay, so you understand retention is important and want to improve it. What do you do? Well, at a high level, there are three types of efforts you can pursue to increase retention:

  1. Make the product more valuable: Every product is a bundle of features, and your product may be missing features that get more marginal users to retain better. This is a journey for feature/product fit.
  2. Connect users better to the value of the product that already exists: This is the purpose of a growth team leveraging tactics like onboarding, emails and notifications, and reducing friction in the product where it’s too complex and adding friction when it’s required to connect people to the value.
  3. Create a new product: Struggling to retain users at all? You likely don’t have product/market fit and may need to pivot to a new product.

We discuss these strategies in a lot more depth in the upcoming Product Strategy program coming soon from Reforge, and if you really want a deep dive on retention, we build the Retention & Engagement deep dive.

Why does retention differ so much across categories?
One question you might be asking yourself is why does retention differ so much by different categories? This was the impetus for the initial research, and why I couldn’t give a great answer to our board. Every company has a bunch of different factors that impact retention:

  • Customer type: For example, small businesses fail at a much higher rate than enterprise businesses, so businesses that target small businesses will almost always have lower retention.* This does not make them inferior businesses! They also have many more customers they can acquire.
  • Customer variability: Products that have many different types of customers will typically have lower retention than products that hone in on one type of customer very well.
  • Revenue model: How much money you ask from customers and how can play a big role in retention. For example, a customer may be more likely to retain for a product they marginally like if it costs $30 vs. $300,000. A product that expands revenue per user over time can have lower retention than ones that have a fixed price.
  • Natural frequency: Many products have different natural frequencies. For example, you may only look for a place to live once every few years (like my time at Apartments.com), but you look for something to eat multiple times of day (like my time at Grubhub).
  • Acquisition strategy: The way a company acquires users affects its retention. A wide spread approach to new users may retain worse than carefully targeting users to bring to your product.
  • Network effects: Network effects may drive retention rates up more over time vs. businesses that do not have these effects. For example, all of your friends on Facebook or all of your co-workers on Slack makes it hard to churn from either product whereas churning from Calm or Grammarly is entirely up to you.

* In those businesses, the business failing and churning as a result is called “involuntary churn”, though that can also mean a payment method not working for someone who wants to retain in other models.

BONUS: Why are Casey’s benchmarks for consumer transactional businesses lower than others?

For the demand side of transactional businesses, where the retention graph flattens is more important to me than the six month retention rate. And unlike other models, these businesses can take longer than six months to have their graphs flatten. Also, for marketplaces, one of the two common models along with ecommerce in this category, a healthy demand side retention rate is very dependent on what supply side retention looks like and acquisition costs. For example, since Uber and Lyft have to spend so much time and money acquiring drivers due to a low retention rate, in order for their model to work, demand side retention either has to be high or demand side acquisition has to be low cost. For a business where supply side retention is high and acquisition costs are low, demand side retention can be lower, and the company can still be very successful. Etsy and Wag I imagine fit more into this model.

Currently listening to We All Have An Impact by Boreal Massif.

Announcing the next Retention Deep Dive, Growth Series, and something new

Over the last few years, I’ve worked with Brian Balfour (CEO Reforge, formerly VP Growth @ HubSpot) as a Growth mentor and contributor to the Reforge programs. These are part-time programs (no need to take time off work) specifically designed for experienced Product Managers, Marketers, Engineers, and UX/Designers in both B2B and B2C companies.

Today, Reforge announced their three upcoming programs this fall:

1) The Retention + Engagement Deep Dive program. I worked closely with Brian developing this program, which looks at every aspect of retention including activation, engagement, resurrection, and churn.

2) The Growth Models Deep Dive program. This is a new, detailed examination of a key growth topic Brian and I developed this year with Kevin Kwok.

3) The Growth Series program. This is Reforge’s flagship program that provides an overview of the key topics in growth that’s been 100% revamped to reflect today’s growth challenges.

Apply to Reforge (Takes ~5 minutes)

Each Reforge program runs from September 24th through November 16th. Seats always fill up fast, and I’m excited to be involved. I’ll also be doing some speaking and Q&A during the events.

Besides Brian, Kevin and myself, other hosts include Andrew Chen (General Partner @ Andreessen Horowitz), Shaun Clowes (VP Growth @ Metromile, former Head of Growth @ Atlassian), Dan Hockenmaier (former Director of Growth @ Thumbtack), Heidi Gibson (Sr. Director of Product Management @ GoDaddy), and Yuriy Timen (Head of Growth @ Grammarly).

About the Reforge Programs
These are all invite-only, part-time programs that last 8 weeks. Each program requires a time commitment of 4 – 8 hours per week. They’re designed for Product Managers, Marketers, Engineers, and UX/Designers in both B2B and B2C companies looking to accelerate growth in their companies and in their careers by developing a systematic approach to thinking about, acting on, and solving growth problems.

In addition to the course material, we’ll also hear from leaders in the industry through interviews, live talks, and workshops, including:

Fareed Mosavat, Growth @ Slack
Ken Rudin, Head of Growth, Search @ Google
Brian Rothenberg, VP Growth and Marketing @ Eventbrite
Ravi Mehta, Product Director @ Facebook
Mike Duboe, Head of Growth @ Stitch Fix
Josh Lu, Sr. Director, PM @ Zynga
Guillaume Cabane, VP Growth @ Drift
Matt Plotke, Head of Growth @ Stripe
Joanna Lord, CMO @ ClassPass
Gina Gotthilf, ex-Growth Lead @ Duolingo
Elena Verna, SVP Growth @ MalwareBytes
Kieran Flanagan, VP Growth/Marketing @ HubSpot
Naomi Pilosof Ionita, Partner @ Menlo Ventures
Nick Soman, ex-Growth Product Lead @ Gusto
Nate Moch, VP Growth @ Zillow
Simon Tisminezky, Head of Growth @ Ipsy
Steve Dupree, Former VP Marketing @ SoFi
See the full list here

Here’s some more detail about each program below:

About the Retention + Engagement Deep Dive
Retention + Engagement Deep Dive zooms in on one of the most important sub-topics of growth.

Retention and engagement separates those companies in the top 1% of their category. Every improvement in retention improves acquisition, monetization, and virality. But moving the needle on retention is hard.

This program takes a microscope to every aspect of retention, including:

  • Properly define, measure, segment, and analyze your retention
  • Find and quantify the three moments every new user goes through to create a long-term retained user
  • Construct a high performing activation flow from the ground up using detailed strategies across product, notifications, incentives, and more
  • Layer your engagement strategies to build a compounding growth machine at your company
  • Articulating retention and engagement initiatives across teams, as well as influencing how leaders think about retention in your company
  • Walk step-by-step through of lessons applied to dozens of examples from companies like Instagram, Zoom, Spotify, Everlane, Airbnb, Turbotax, Jira, Credit Karma, Blue Bottle
  • And more…

The Retention + Engagement Deep Dive is designed for growth professionals who are looking to zoom in on retention, either because their job is focused on retention, or because they already have an advanced working understanding of the quant and qual fundamentals of growth and are looking to build additional competency in retention and engagement.

Apply for the Retention + Engagement Deep Dive

About the Growth Models Deep Dive
The new Growth Models Deep Dive addresses an essential new skill and topic that every growth practitioner needs to understand. Your growth model is the essential tool that drives alignment, prioritization, strategic investments, metrics, and ultimately, growth. Without it, your team ends up setting faulty goals, focusing on sub-optimal initiatives, and running in opposite directions.

This program goes deep into growth models across companies. You will:

  • Learn how the fastest growing products actually grow (hint: the answer isn’t funnels)
  • Dissect how the fastest growing products like Uber, Slack, Dropbox, Stripe, Airtable, Instagram, Fortnite, Tinder, and others grow using growth loops
  • Learn the detailed components of 20+ growth loops
  • Systematically construct growth loops your product can use after analyzing the three qualitative properties of every growth loop
  • Assess gaps and uncover opportunities for growth by identifying, measuring, and analyzing your products existing growth loops
  • Complete a step-by-step walkthrough to build your quantitative model for a single loop and your entire product
  • Communicate actionable insights from your growth model to obtain buy-in from leadership and across teams
  • And more…

The Growth Models Deep Dive is designed for growth professionals looking to focus on growth modeling, either because their job requires modeling their company or product’s growth or because they’re in a leadership role. It’s especially useful for growth leaders looking to influence leadership, set a team’s direction, and rally colleagues using growth models.

Apply for the Growth Models Deep Dive

About the Growth Series
The Growth Series is a comprehensive overview of the key topics in growth. The program is designed to help you accelerate growth of your product, company and your career by creating a prioritized list of retention strategies, building your quantitative growth model, and much more. Plus, the Reforge team spent +100 hours collecting feedback, investigating new growth concepts with experts, and analyzing the latest strategies coming out of top companies to completely overhaul the content with new topics, frameworks, and relevant examples.

During the Growth Series, you’ll learn:

  • Going from understanding one or two pieces of your growth model to understanding how the entire system works together
  • Evaluating the key components of growth (acquisition, retention, monetization) and how they feed one another
  • How to construct a holistic growth model, bringing together all the components of the funnel
  • How to understand and evaluate the user motivations behind the levers in your growth model
  • Running a continual, self-reinforcing experimentation process to execute against your growth model and user psychology
  • Learn how to properly call, dissect, and analyze an experiment, plus implement the results across your team
  • And more…

The Growth Series is designed for practitioners who already know the basics of growth and are figuring out how to take the next step. Participants are assumed to have knowledge about A/B testing, ad buying, and other fundamental tactics, and are ready to take on the bigger challenge of thinking about the entire picture of growth and forming a coherent and compelling strategy.

Apply to the Growth Series

Feature/Product Fit

Through various methods, Silicon Valley has drilled into the minds of entrepreneurs the concept of product/market fit. Marc Andreessen says it’s the only thing that matters, and Brian Balfour has an amazing series of posts that talk about how to find it. But what happens after you find product/market fit? Do you stop working on product? I think most people would argue definitely not. Post-product/market fit, companies have to balance work creating new product value, improving on the current product value, and growing the number of people who experience the current value of the product. While I have written a lot about how to do that third step, and even wrote a post about thinking about new product value, I haven’t written much about how do that second part.

What is Feature/Product Fit?
Every product team tries to make their core product better over time. But sadly, at most companies, the process for this is launching new features and hoping or assuming they are useful to your existing customers. Companies don’t have a great rubric for understanding if that feature is actually valuable for the existing product. This process should be similar to finding product/market fit, but there are some differences. I call this process feature/product fit, and I’ll explain how to find it.

In product/market fit, there are three major components you are searching for. I have written about my process for product/market fit, and Brian Balfour, Shaun Clowes, and I have built an entire course about the retention component. To give a quick recap from my post though, you need:

  • Retention: A portion of your users building a predictable habit around usage of your product
  • Monetization: The ability at some point in the future to monetize that usage
  • Acquisition: The combination of the product’s retention and monetization should create a scalable and profitable acquisition strategy

Feature/Product Fit has a similar process. We’ll call this the Feature/Product Fit Checklist:

  • The feature has to retain users for that specific feature
  • The feature has to have a scalable way to drive its own adoption

Feature/Product Fit has a third requirement that is a bit different: the feature has to improve retention, engagement, and/or monetization for the core product.

This last part can be a bit confusing for product teams to understand. Not only do the products they are building need to be used regularly and attract their own usage to be successful, they also need to make the whole of the product experience better. This is very difficult, which is why most feature launches inside companies are failures. What happens when a feature has retention and adoption, but does not increase retention, engagement, or monetization for the company? This means it is cannibalizing another part of the product. This might be okay. As long as those three components do not decrease, shipping the feature might be the right decision. The most famous example of this is Netflix introducing streaming so early in that technology’s lifecycle, which cannibalized the DVD by mail business, but was more strategic for them long term.

What is a Feature Team’s Job?
You would be surprised how many core product features are shipped when the new feature decreases one of those three areas. How does this happen? It’s very simple. The team working on the feature is motivated by feature usage instead of product usage, so they force everyone to try it. This makes the product experience more complicated and distracts from some of the core product areas that have feature/product fit.

If you own a feature (and I’m not saying it’s the right structure for teams to own features), your job is not to get people to use that feature. Your job is to find out if that feature has feature/product fit. You do this by checking the three components listed above related to feature retention, feature adoption, and core product retention, engagement and monetization. During this process, you also need to determine for which users the feature has feature/product fit (reminder: it’s almost never new users). Some features should only target a small percentage of users e.g. businesses on Facebook or content creators on Pinterest. Then and only then does your job shift to owning usage of that feature. And in many teams, it’s still not your job. The feature then becomes a tool that can be leveraged by the growth team to increase overall product retention, engagement, and monetization.

Mistakes Feature Teams make searching for feature/product fit
Feature teams commonly make mistakes that dissatisfy the third component of feature/product fit at the very beginning of their testing.

  • Mistake #1: Email your entire user base about your new feature
    Your users do not care about your features. They care about the value that you provide to them. You have not proven you provide value with the feature when you email them early on. Feature emails in my career always perform worse than core product emails. This inferior performance affects the value of the email channel for the entire product, which can decrease overall product retention.
  • Mistake #2: Put a banner at the top of the product for all users introducing the new feature
    New features usually target specific types of users, and are therefore not relevant to all users. They are especially irrelevant to new users who are trying to learn the basics about the core product. These banners distract from them, decreasing activation rates. It’s like asking your users if they’re interested in the Boston Marathon when they don’t know how to crawl yet.
  • Mistake #3: Launch with a lot of press about the new feature
    PR for your feature feels great, but it won’t help you find feature/product fit. PR can be a great tool to reach users after you have tested feature/product fit though. It should not happen before you have done experiments that prove feature/product fit. And it will not fix a feature that doesn’t have feature/product fit.

Many features won’t find feature/product fit
Many of the features product teams work on will not find feature/product fit. When this happens, the features need to be deleted. Also, some older features will fall out of feature/product fit. If they cannot be redeemed, they also need to be deleted. If you didn’t measure feature/product fit for older features, go back and do so. If they don’t have it, delete them. Some of our most valuable work at Pinterest was deleting features and code. A couple examples:

  • The Like button (RIP 2016): People did not know how to use this vs. the Save button, leading to confusion and clutter in the product
  • Place Pins (RIP 2015): Pinterest tried to create a special Pin type and board for Pins that were real places. As we iterated on this feature, the UI drifted further and further away from core Pinterest Pins and boards, and never delivered Pinner value
  • Pinner/Board Attribution in the Grid (RIP 2016): Attributing Pins to users and their boards made less sense as the product pivoted from a social network to an interest network, and cluttered the UI and prevented us from showing more content on the screen at the same time

How do I help my feature find Feature/Product Fit?
All features should be launched as experiments that can test for feature/product fit. During this experiment, you want to expose the new feature to just enough people to determine if it can start passing your feature/product fit checklist. For smaller companies, this may mean testing with your entire audience. For a company like Pinterest, this might start with only 1% of users. The audience for these experiments is usually your current user base, but can be done through paid acquisition if you are testing features for a different type of user.

I’ll give you a few tactics that have helped the companies I’ve worked on find feature/product fit over the years. Most good product development starts with a combination of data analysis and user research. User research should be involved at the right times to add the most impact, prevent confirmation bias, and determine what components users are struggling to see the value of. For example, when we launched the Grubhub mobile app, we saw in the data when people used the current location feature, their conversation rate was lower than people who typed in their address. This turned out to be an accuracy problem, so we turned that component until off until we were able to improve its accuracy.

In research, we saw people were having trouble figuring out which restaurants to click on in search results. On the web, they might open up multiple tabs to solve this problem, but this was not possible in the app. So, we determined what information would help them decide which restaurants were right for them, and started adding that information into the search results page. That included cuisine, estimated delivery time, minimum, star rating, number of ratings, On the surface, the page now feels cluttered, but this improved conversion rates and retention.

Since Grubhub is a transactional product, we were able to leverage incentives as a strategy to help find feature/product fit. Our early data showed that people who started to use the mobile app had double the lifetime value of web users. So, we offered $10 off everyone’s first mobile order. This transitioned web users to mobile for the first time and acquired many people on mobile first. The strategy was very successful, and the lifetime value improvements remained the same despite the incentive.

Grubhub also uses people to help find feature/product fit. Since mobile apps were new at the time (and we were the first food delivery app), we monitored any issues on social media, and had our customer service team intervene immediately.


Not all complaints are this entertaining.

At Pinterest, we launched Related Pins in 2013. For Pinterest, we did not have a revenue model at the time, so tactics around incentives and people do not make as much sense. One thing we did instead was use notifications to drive feature/product fit. Once this algorithm was developed, after you Pinned something, we could now email you more Pins you might like that are related to that Pin. These emails were very successful.

Pinterest also used the product to drive feature/product fit. We launched the algorithm results underneath the Pin page to start, and interaction when people scrolled was great. But many people didn’t scroll below the Pin. So, we tried moving them to the right of the Pin, which increased engagement, and we started inserting related Pins of items you saved into the core home feed as well, which increased engagement.

The Feature/Product Fit Checklist
When helping a product find feature/product fit, you should run through this checklist to help your feature succeed:

  • What is the data telling me about usage of the feature?
  • What are users telling me about the feature?
  • How can I use the core product to help drive adoption of the feature?
  • How can I use notifications to help drive adoption of the feature?
  • How can I use incentives to help drive adoption of the feature?
  • How can I use people to help drive adoption of the feature?

When confirming feature/product fit, you need to ask:

  • Is the feature showing retention?
  • What type of user(s) is retaining usage of the feature?
  • How do I limit exposure to only those users?
  • What is the scalable adoption strategy for the feature for those users?
  • How is this feature driving retention, engagement, or monetization for the overall product?

Every company wants to improve its product over time. You need to start measuring if the features you’re building actually do that. You also need to measure if existing features are adding value, and if not, start deleting them. Asking these questions when you build new features and measure old features will make sure you are on the path to having features that find feature/product fit and add value to users and your business.

Thanks to Omar Seyal and Brian Balfour for reading early drafts of this.

Currently listening to Breaking by Evy Jane.

The Email Marketing and Notifications Evolution Inside Companies

“Stop sending emails like a marketer. Start sending email like a personal assistant.”

I’ve communicated this line in a lot of my presentations on growth, but I haven’t talked about in depth the evolution on how to get there. There is a clear evolution that companies follow in terms of evolving their emails and notifications, from not sending them at all, to sending one-off blasts to their entire audience, to creating a lifecycle, to having a holistic personalized messaging platform. Having worked on these systems at my last two companies, I thought it would be beneficial to outline these transitions for people earlier along in their process.

Phase 0: We Hate Email

“We hate email, so we don’t send it to our customers.”

Almost every company I have worked with has communicated this at some point in its early stages, and it’s always wrong. Except for very specific groups, people don’t dislike email; they dislike bad email. The path to figuring what email your customers would like to receive is largely to ask what kind of value do they see in my product, and can I deliver that value in email form.

Phase 1: Mass Promotional Email + Personalized Order Notifications
If you are a transactional service, you have to send personalized order notifications, so that is where most companies start. At some point later, companies start sending mass emails to their broader audience about certain things like new features, discounts, etc. This effort will show improvements in key metrics, but it is very unsophisticated.

Promotions train users to wait for promotions to order, decreasing profitability. Also, with this approach, marketing is assuming a cadence for the customer instead of adapting to the customer’s cadence (and ultimately improving customer cadence to increase lifetime value).

If you are engineering constrained, there are some simple optimizations to this approach that will improve your performance:

  • Develop additional emails intended to drive habit formation (instead of just timely purchase). Examples include trending items, item sales, new merchants added, recommended items
  • If emails are successful, test them as push notifications as well
  • Take existing confirmation emails, and add marketing messages to them (other things to buy, set up a re-order, most popular items, etc.)
  • Send every non-transactional email and tweak to confirmation emails as an experiment with an enabled and control group to prove impact on lifetime value vs. unsubscribes/push permissions/app deletions

Phase II: Moving to Lifecycle Messaging
In Phase II, these additional emails and notifications that have been successful in Phase I start to form an automated program that consistently drives additional engagement from customers. In order to address messaging fatigue, these email and notification templates are managed to a frequency per month based on the team’s expected value of a good customer. This frequency is not based on data, but if everyone used the product properly, what would ideal frequency look like. The goal is to use messaging to remind people of the service and reinforce the habit. They are paired with personalized discount emails intended to drive new use cases and increase frequency.

These emails and notification templates are also managed against each other, so messaging does not get stale. For example, if you have three templates outside of confirmation emails, and you sent template 1 last week, you would attempt to send templates 2 and 3 before sending template 1 again. Also, each week, these emails have new subject lines to present them from looking like the same email as the previous weeks.

Phase III: Holistic, Personalized Messaging
As the Phase II approach flattens in terms of the additional impact it can drive, companies shift toward a more holistic, personalized system. This is a considerable investment, which we made at Pinterest. Essentially, product and engineering determine for each customer:

  • The right content
  • The right time to send it (day of week and time or day)
  • The right amount (how many emails and pushes to send)
  • The right channel (email, push, or both)

This requires a team to develop a log of every email/push sent to a subscriber, when it was sent, when it was opened, when it was clicked, what downstream engagement occurred from clicks, and which template it was. All emails and notifications are run through the same experiment dashboard as product changes to understand the impact on all key metrics. From this, it needs to determine:

  • The best day(s) of week and time(s) to send messages to each user
  • A prioritization of the templates to send based on historical click through rates and/or purchase rates
  • How many messages per a generic time frame maximizes lifetime value of each user

This usually starts via a rules based approach, and eventually becomes powered by machine learning. If you lack enough historical data on a user to do this, for example new users, you group people who used to look like those users as a segment i.e. previous new users and look at the best performing approach for them. Email can no longer be considered marketing at this point. It is considered an extension of the core product.

The team also starts optimizing deliverability through choosing better message transfer agent partners and segmenting IP addresses for different templates to isolate issues. The team may also start investing in more advanced security measures like DMARC.

This is a considerable investment, which is why most companies only start building this once there are sending millions of emails a day with a lot of history from operating in the first two phases originally. At this point, companies know the value of email, and can justify the investment.

In my opinion, every company should end up at phase III at some point. The question is how long it takes to get there. This varies based on engineering constraints, scale, and how long it takes emails and notifications to flatten off in terms of additional engagement by the previous phases. Outsourcing this to a marketing technology company is also very problematic as it requires access to all of your user data, and any migration of data from system to system slows down performance. At a certain scale (like Pinterest), it is not even possible.

If you’re not at Pinterest’s level of sophistication, don’t dismay. Very few companies are. Just start to think about the long term evolution, and when is the right time to push for a step change in email and notification performance vs. continued optimization. It’s a big investment to shift from phase to phase, but the returns are usually worth it, and the impact of these emails and notifications in the current phase, and the struggle to improve their performance, should be what drives that decision to make additional investment to get to the next phase.

Currently listening to XTLP by μ-Ziq.

Align Revenue to the Value You Create

“We want to create more value than we capture.”*

Tim Kendall, the former President of Pinterest, repeated those words at an all hands to describe our strategy for monetization a few years ago. My role as an advisor to Greylock’s portfolio companies allows me to work with many different types of businesses: consumer social, marketplaces, SaaS, etc. I’ve come to realize this saying describes an optimal strategy for a lot more than just an ad-supported revenue model. It should actually be the guiding light for most subscription software businesses.

Align Revenue To The Value You Create
One of the most common questions I receive from subscription businesses is when to ask for a signup and when to start charging customers. In freemium businesses, the slightly different question is how aggressively you upsell the paid product, and how good you make the free product. If you talk to entrepreneurs, you will get definitive answers from them, but they are frequently the opposite of each other. “You should never give away your product for free!” “You’ll never succeed without a free trial!” “Ask for credit card upfront! People won’t take the product seriously.” “Never ask for a credit card upfront! You’ll shoo too many people away.” The default answer I gave to entrepreneurs after hearing all of this feedback is that it depends on the business and needs to be tested.

As I researched more into the problem, these questions actually seemed to be the wrong questions to be asking. Harkening back to Tim Kendall’s advice, I started asking entrepreneurs, “What is the path to actually creating value from your service for your customers? How long does it take, and what actions need to be accomplished?” In other words, very similar advice to what is a successful onboarding? Once you learn that, you can determine how to capture some of the value you create.

Capture Value For The Business After Value Has Been Created For The Customer
When your product is subscription based, the prime time to ask for a subscription is after a successful onboarding occurs. It frequently is based on usage, not time. Dropbox is a famous example. The product is free up to a certain amount of storage. Once a user hits that amount of storage, they cannot add more files to Dropbox without paying. This storage amount also happens to be around the point where Dropbox becomes a habit, and represents real switching costs to find another way to share files across devices. So their conversion rates to paid are very high without any sort of time-based trial period. They don’t have a free product and a paid product; they have a free introduction to their paid product, and it becomes paid as soon as value has been created for the customer.

Your company may not have a long time to demonstrate value though, which may force your product to change to display (and capture) value more quickly. For startups based on search engine traffic, people reach your page with intent at that moment, and you frequently learn that this initial session is your only chance to convert them. So you push for a signup during that session after showing a preview of the value you can provide.

That is what we implemented at Pinterest, and it worked well, but it definitely created backlash from users for whom we had not yet created enough value. Once Pinterest was relevant on search engines for multiple topics, we saw people come back multiple times, and pulled back the signup walls on first visit. At that point, Pinterest was confident users would come back and thus focused on demonstrating more value before asking for signup.

Don’t Try To Capture Value In A Way That Reduces Value Created
It’s interesting to map the revenue growth of Dropbox to Evernote over the same time period. Evernote allows you to store an unlimited number of files and only makes you pay for advanced features like offline storage, storing large files, and (later) sharing on more than two devices. These features would have actually increased value created and switching costs if they were free, because Evernote’s value prop is about being able to access notes everywhere. If Evernote had instead mined their data and seen that people stick around after, say 50 notes, that would probably have had more effective monetization.

You only want to hide features from free users if they do not create habits or virality. Hiding sharing functionality before payment never makes sense because it introduces more people to the product for free. Hiding functionality that helps create retention also doesn’t make sense because you can always upsell retained users, but you can never upsell users who did not see the value and therefore don’t come back.

Decreasing Churn Is Long Term More Important Than Maximizing Conversion
Many people will decry that this strategy actually reduces revenue. In the short term, this sentiment is likely to be true. Decreasing churn might have a lower conversion rate upfront, but it aligns to long term successful retention. Churn rate is usually one of the biggest barriers to long term growth, so it’s worth thinking about this type of strategy even if it has a short-term decrease in revenue. It can be much harder to re-acquire someone after they have canceled, than charge someone for the first time who has been receiving regular value because you charged them for value you didn’t create.

What usually happens when a company captures more value than they create is they will have high revenue growth for a period of time (with a lot of investor enthusiasm), followed by a flattening of growth and then a steep revenue decline. This happens because revenue growth is a lagging indicator. Usage growth is the leading indicator. When usage lags revenue, this predicts churn. As you churn more and more users, it becomes harder and harder (and eventually impossible) to replace those churned users with new users to keep revenue metrics flat. Look at Blue Apron’s valuation to see this playing out currently as subscribers start to decrease for the first time year over year.

You Want Your Revenue Model To Align As Closely As Possible To The Value You Create
Lastly, as you start charging customers to capture value you create, you want your business model to align to the value that is being created. Email marketing tools have mastered this. Email marketing tools’ value is based on reaching customers with messages. Most email marketing tools charge on a CPM (i.e. a price for every thousand emails you send via their platform). As your email volume increases, they continue to drop the CPM. This make these companies more money because customers are sending a lot more email over time. But it actually becomes more valuable to the customer as well, because email is now cheaper on a per unit basis to send.

Compare this to Mixpanel, a product analytics tool. Mixpanel charges per event, and their value is delivering insights based on data from events being logged on your website or mobile app. The more events that are tracked in Mixpanel, the more insights the customer can receive, and the stickier the product. Since Mixpanel is charging per event though, a weird calculus emerges for the customer. The customer has to ask if tracking this event is worth the cost because not all events are created equal. Meaning the customer has to decide which data is important before they use the product. So, Mixpanel’s revenue model actually hurts its product value.

— 

It’s easy for subscription businesses to get attracted to the allure of short term revenue. The goal of your business is first to create value. The creation of that value and the understanding of how it’s created allow for more optimal and sustainable revenue generation opportunities. Don’t pursue short term revenue opportunities that prevent the customer from understanding the value your company creates. When you are generating revenue, you want to align that revenue model to how value is created for your customer. If you’re not sure, err on the side of creating more value than you capture rather than the opposite. This leads to long term retention and the maximization of revenue.

Naomi Ionita, General Partner at Menlo Ventures and former growth leader at Invoice2go and Evernote, and I talk more about this and other subscription growth problems in the Greymatter podcast.

*This quote I believe originally stems from Brian Erwin.

Currently listening to Shape the Future by Nightmares on Wax.

What Are Growth Teams For, and What Do They Work On?

This blog post was adapted from a presentation I did recently. Hence, slides. Don’t say I didn’t warn you.

I receive a lot of questions about growth teams. Naturally, there is a lot of confusion. Is this marketing being re-branded? Who does this team report to? What is the goal of it? What do they actually work on? When do I start a growth team for my business?

The purpose of growth is to scale the usage of a product that has product-market fit. You do this by building a playbook on how to scale the usage of a product. A playbook can also be called a growth model or a loop.

The first question you should ask before asking about growth is if you have product-market fit?

The traditional definition above is qualitative, and if you’re like me, you like to have data to answer questions. The best way to get that data for most businesses is to measure retention.

The best way to identify the key action is to find a metric that means the user must have received value from your product. The best way to understand the frequency on which you should measure that metric is how often people solved this problem before your product existed. Let’s look at some examples from my career.

For Pinterest, a Pinner receives value if we showed them something cool related to their interests. The best way to determine if the Pinner thought something we showed them was cool is that they saved it.

For Grubhub, this was even easier to determine. People only receive value if they order food, and when we surveyed people, they ordered food once or twice a month (except for New York).

Once you have a key metric and a designated frequency, you can graph a retention curve or a cohort curve. If it flattens out, that means some people are finding continual value in the product. But that is not enough.

Brian Balfour has a great post on this, which he calls product-channel fit.

If you’ve been around startups for a while, you might remember this tweet from Paul Graham. It talked about the fastest growing startup Y Combinator has ever funded. It is a graph of revenue growth from $0 to $350,000 per month in just 12 months.

The startup was Homejoy, an on demand cleaning service. Investors liked this graph, so they gave the company $38 million to expand.

20 months later Homejoy shut down. From a post-mortem of the company, I highlight the following quote.

If you discounted to get to product-market fit, you didn’t get to product-market fit. Product-market fit is not revenue growth, it’s not growth in users, it’s not being #1 in the App Store. Product-market fit is retention that allows for sustained growth.

So, I though product teams were in charge of creating a product people loved, and marketing teams were in charge of getting people to try the product. What changed?

What changed is an acknowledgement of what actually drives startup growth. There are three main levers. Phase I is simultaneously the most important and the least understood. In Phase I, you change the product to increase its growth rate. Some changes include improving onboarding, helping the product acquire more customers through activities like virality or SEO, incresing the conversion rate, et al.

These initiatives are “free” in that they don’t require an advertising budget. Their cost is the opportunity cost of a product team’s time. They are measurable in that you can create an experiment and understand the exact impact of the change. They are also scalable in that if you make a change that, say, improves your conversion rate, and it has a certain amount of impact, it likely will have that same impact tomorrow, weeks from now, and potentially even years from now.

The other two phases are what we traditionally think of as marketing. Performance marketing initiatives, like buying ads on Facebook or Google, are also measurable and scalable, but scale with an advertising budget. Brand marketing usually requires an even larger ad budget, and is harder to measure or scale. The time frame over which brand marketing works takes years, and can be hard to confirm. If you do create a PR campaign or a TV ad that seems to work on a more immediate time frame, it can be hard to scale. that is because brand marketing always requires new stories to keep people’s attention.

This is why marketing can’t be in charge of all growth initiatives. They don’t have access or capability to the most important ones. They might know they need to improve the site’s conversion rate or get more traffic from referrals, but they don’t have access to the product roadmap to get them prioritized appropriately, and if they do get engineering and design help, they don’t have the expertise in working with them to build the best solutions.

Perhaps what’s more important to understand in the difference between marketing and growth is how the traditional marketing funnel changes with startups. Above is the traditional marketing. This model is based on the old school model of product development before the internet in spending a lot of money to make people want things.

Startups by definition should be making things people already want. When you do that, you can invert the funnel and focus on people that already want the product or people that are already using it. This is more effective on a small (or no) budget.

When you translate that into tactics, you see how product-driven growth initiatives dominate the top of the list of priorities. It does not mean you won’t work on performance marketing or brand marketing, but that they usually become important later on in a product lifecycle as an accelerant to an already sustainably growing company.

So I spent a lot of time explaining why growth is different from marketing. How is different from product?

Growth teams don’t create value. They make sure people experience the value that’s already been created.

The most common examples to start a growth team to address are:

  • improving the logged out experience (for conversion or SEO)
  • sending better emails and/or notifications
  • increasing referrals or virality
  • improving onboarding

SEO and onboarding are harder places to start because their iteration cycles are much longer than the other areas.

Growth teams don’t start by finding mythical VP’s of Growth to come in and solve all of your problems. They are usually started by existing employees at a company (or founders) that really understand the company and what’s preventing it from growing faster. They report to their dedicated functions, but sit together to focus on problem solving.

To find out which area you work on after you have the team, you have to analyze the data. For example, at Pinterest, they originally wanted me and my team to work on SEO. What we saw was that while there was a lot of opportunity to get more traffic via SEO, a bigger issue was the conversion rate from that traffic. So we decided to work on conversion instead.

Then we had to figure out what to work on. Jean, an engineer on the team, had recently run an experiment that gave us a key insight. So, we said, we could use this same modal when people clicked on Pins. Clicking on a Pin could show enough interest in Pinterest for you to want to sign up.

The other thing people did when they liked what they saw scroll to see more. So, we decided to try stopping them where we stopped the Google crawler, and asking them to sign up then.

It took Jean two days of work to launch this experiment, and it resulted in a much bigger impact than expected.

So that’s an example of finding a conversion issue in you data, and putting together a really scrappy experiment to try to improve it. What else can growth teams work on? Here are some examples from my time at Pinterest, and some best practices we’ve learned.

Usually, the biggest area a growth team focuses on improving is retention. That’s right; growth teams are not just about acquisition. Retention comes from a maniacal focus on improving the core product, which I define as core product, not growth, work. Where growth comes in is reducing friction to experience that core product. Simplifying how the current product works usually has much more impact than adding new features. New features complicate the product, making it harder for new people to understand.

So how do you simplify the core product? Well, you have to have data to understand what people do, and pair it with qualitative research to understand why they do it. We spent countless hours at Pinterest putting laptops in front of non-users watching them sign up for the product to figure out why people didn’t activate.

At Grubhub, data pointed out that Grubhub was an S curve when it came to both conversion and retention. This graph is a (now very old) graph of conversion rate in Boston based on how many restaurants Grubhub returned when you searched your address. After 55 results, conversion rate essentially doubled for new and returning users.

Qualitative research gave us different insights. When we asked users why they didn’t use Grubhub more often, they would say, “it’s expensive.” We thought that was weird, because Grubhub wasn’t charging them anything. What they meant is that delivery was expensive due to minimums and delivery fees. So, we went back to our restaurants, convinced a few to try lowering their minimums and fees to see if increased volume could make up for lower margin. When it did, we creates case studies to help convince other restaurants.

At Pinterest, we simplified the signup and onboarding flow. What used to be a flow that required five steps was now three with one of them pre-filled and the other two optional. What we did do was introduce friction that we knew made it more likely a Pinner would find content they care about. This was asking them which topics interested them before showing them a feed of content.

We also realized that the more content people see, the more likely they will find something they like, which will lead to retention. So, we removed content around Pins that was non-critical, like who Pinned it to what board and how they described it. All of these increased activation rates.

We also contextually educated people on what to do next when they were onboarding. There is a common saying that if you need to add education to your design, it’s a bad design. It’s pithy and sounds smart, but it’s actually dangerous. My response is that a design with education is better than a design that doesn’t educate.

Search engine optimization has been a really great lever for organic growth for every company I’ve worked on in my career. It’s not for every business though. People need to already be searching for what you do. That alone isn’t enough though. You need to be an authority on the subject, which Google determines by relevant external links to your domain and your content. You also need to be relevant to what was just searched.

We worked on improving both of these at Grubhub. When Grubhub launched new market, by definition we weren’t locally relevant yet. So we would go to local blogs and press outlets and tell them we were launching there, and that we wanted to give their readers $10 off their first order. All they had to do was link to a page where the discount would auto-apply. After a while, that page would have enough local links so that even though the promotional discount was over, it would still rank #1 for the local delivery terms e.g. “san francisco food delivery”.

For relevance, Grubhub knew which restaurants delivered where, their menu data, and reviews from real people. So we aggregated them into landing pages for every locale + cuisine combination e.g. ‘nob hill chinese delivery”.

We applied the same landing page strategy at Pinterest. While Pinners had created boards on their favorite topics, it was one person’s opinion on what was relevant for a topic. Pinterest has repin data globally for every topic, so we knew what the best Pins were across the Pinterest community. So we created topic pages with the best Pins, and they performed better than individual boards on search engines and with search engine users.

We also worked a lot on emails and notifications on the Pinterest growth team. Emails are a key driver of retention. They won’t solve your retention problem, but they will certainly help if you do them right. At every company I have been at, people hated email and didn’t want to send them to their customers. When they finally did, they saw lifts. You are not your customer. You get more email than they do. Emails help them if they’re connected to the core value of why they use your product. Emails are not helpful if they’re pushing a marketing message.

At Pinterest, I made this mistake. I set up campaigns with emails that explained all of the things Pinterest could do. People don’t care about what Pinterest can do. They care about seeing cool content related to their interests. We needed to stop sending email like a marketer, and start sending email like a personal assistant. So we replaced those emails with popular content in topics of interest for each Pinner, and our retention increased.

Then, we built a system around it. Each Pinner likes different content, at different times, and different amount of it. So we learned for each Pinner what content they liked, when they liked to receive emails and notifications (based on when they opened them), and how much they liked to receive based on testing different volumes and seeing open rate impacts.

If you’re testing emails and notifications, you can test manually first, then automate and personalize. What I have learned at Pinterest and Grubhub is what seems to be worth testing. At Pinterest, one engineer tested 4,500 different subject lines, resulting in hundreds of thousands of additional weekly active users. Around the same time, we spent three months redesigning all of our emails, and it had no impact on usage.

A common issue I see with growth and marketing teams is they think that emails and notifications can only have positive impact. This is not true. You have to measure the lift in usage vs. the unsubscribes (and the impact of an unsubscribe) and app deletions. Those will impact usage, and you need to know how.

Growth teams have a clear purpose, and that purpose makes sense only if you have first found product-market fit. Once you have that, you will find traditional product and marketing lacking in their ability to help scale usage of your product. That’s where growth teams come in. Growth teams use data and qualitative research to help understand the frictions that prevent more people from finding the value in your product. That can mean acquisition, but it can also mean reducing friction in the core product, working on conversion or onboarding, or finding ways to remind existing users about the value you’re creating. If you have questions about growth teams, don’t hesitate to reach out to cwinters@greylock.com.

This presentation was made in conjunction with @omarseyal, who is awesome.

Currently listening to Everybody Works by Jay Som.

B2B Growth Podcast with Naomi Ionita

Naomi Ionita, VP of Growth at Invoice2go and formerly Director of Growth at Evernote, joins me to discuss the growth B2B startups that grow more like consumer businesses. We discuss topics like how to monetize your product in general, converting new customers to paying customers, and preventing churn.

The iTunes link is here, and here is the Soundcloud link for email readers.