Author Archives: Casey Winters

Addressing Common Misconceptions about Food Delivery Marketplaces

I spent five and a half years working at Grubhub, from series A to right before IPO. This allowed me to learn about many of the intricacies of the restaurant market and food delivery in general. More people have started to take notice of the market because of a slew of market entrants and Grubhub ($9.1B), Just Eat ($4.8B), and Delivery Hero ($7.2B) being successful on the public markets. With that, more articles in the press. Articles… that are wrong. I am reminded of the Murray Gell-Mann Amnesia Effect, invented by Michael Crichton, when I read these articles. It says:

Briefly stated, the Gell-Mann Amnesia effect is as follows. You open the newspaper to an article on some subject you know well. In Murray’s case, physics. In mine, show business. You read the article and see the journalist has absolutely no understanding of either the facts or the issues. Often, the article is so wrong it actually presents the story backward—reversing cause and effect. I call these the “wet streets cause rain” stories. Paper’s full of them.

In any case, you read with exasperation or amusement the multiple errors in a story, and then turn the page to national or international affairs, and read as if the rest of the newspaper was somehow more accurate about Palestine than the baloney you just read. You turn the page, and forget what you know.

As someone who does know, I want to explain some of these misconceptions, so people don’t think wet streets cause rain (even though rain does cause delivery orders).

Misconception #1: Restaurant Margins
One major gripe journalists cite about food delivery marketplaces (more so UberEats than Grubhub due to its higher fees) is that restaurants operate on slim margins. Therefore, if food delivery marketplaces are charging 15-30% for delivery orders, restaurants are not making any money. The issue is understanding the difference between restaurant margins and delivery margins, which are very different.

Most successful marketplaces are built on top of an under-utilized fixed asset. For food delivery marketplaces, this under-utilized fixed asset is not the restaurant, but the kitchen. Restaurants have a fixed capacity they can seat at a restaurant. The kitchen, however, is usually capable of producing much more food on a daily basis than is needed by the patrons that dine in the restaurants. Restaurants are paying for that kitchen capacity regardless of how much they use because one of the highest costs for most restaurants in cities is the cost of rent. That’s why I thought it was so silly when all of these delivery service startups started making their own food. You’re spending a lot of money to build what the incumbent gets for free: excess kitchen capacity to make food. This is why restaurants love catering orders so much. They get big orders that can better leverage their kitchen capacity. After catering, their next favorite is delivery.

Why delivery? It allows them to serve many more customers at a time with their fixed asset, spreading their fixed costs across many more customers. Catering and delivery are pretty much pure margin for restaurants because their only extra cost is a delivery driver (or not, in many cases) who is subsidized by the people ordering the food via tips.

Misconception #2: Paying for Repeat Orders
The second major gripe I hear about food delivery marketplaces is that they charge the same amount for a customer’s first order to a restaurant and repeat orders. Now, a lot of this is drummed up by a competitor who does not drive demand, so it is biased, but I’ll endear it. The misconception here is that all a restaurant has to do is pay an advertising fee to induce trial, and if the food is good, the customer will order again based on that experience. That is now how food delivery works. Food delivery is very fragmented, and while there is differentiation by way of restaurant quality, there are usually quite a few worthy substitutes. Also, the way delivery orderers usually make decisions is method first, restaurant last. The way the average person decides to use Grubhub operates something like this (flowchart):

Restaurant loyalty is one of the least important and last steps in the process of the person ordering food. This means people ordering food do not have loyalty, and you need to compete for every order as if it’s the first. Google Adwords does not charge Apartments.com less if someone who clicked their ad a year ago when searching “apartments” does so again the following year. The reason they don’t is because if Apartments.com wasn’t showing up there, that user would have gladly clicked the ad for ApartmentGuide. Restaurants should absolutely be working to build loyalty, and some do. But expecting that acquisition was the hard work, and restaurants should only pay a SAAS-level fee for retention does not align to the value these marketplaces actually create for restaurants.

Changes That Do Make Sense
Now, there are some elements I would change in regard to repeat orders if I worked at these marketplaces. While Grubhub already charges a much cheaper rate if the order originates from a restaurant’s own website, there are other forms of orders that the restaurant seemingly drives itself without the marketplace’s marketing engine or aggregation. One is if the person ordering food directly types the restaurant’s name into the marketplace. Charging a lower rate for that makes sense as the restaurant is clearly driving the business. I know from the data that this is a small percentage of orders, but this would show good faith to restaurants that marketplaces want to align their revenue model to the value they create.

Another scenario is if the person lands directly on the restaurant’s page on the marketplace from somewhere else e.g. Google. If this happens organically (because the marketplace ranks high or because the restaurant does not have its own website), the marketplace should charge a lower rate. The reality is Yelp and Google Local take 90% of this traffic, but again, it would send the right message.

The other example of this is a little harder to parse. These marketplaces also bid on restaurant names on Google. If that drives an online order, what should be the charge? The restaurant drove the demand, but the marketplace spent the advertising dollars to close the order. In this case, I think these marketplaces should evolve to asking if restaurants want this type of marketing, and if so, charge for the advertising as a service. This was not possible for Grubhub when I worked there due to game theory issues with all the competitors, but with a shrinking field of credible players, it may be possible.

The Three Personas: How Marketing, Product, and Analytics Attempt to Define The Customer

In my career, I’ve worked in marketing, product, and analytics. While the American Marketing Association defines all of that as marketing, the reality is those are rarely under the scope of one functional team, and the people in those groups see things very differently much of the time. One of the key ways this manifests that creates confusion for the organization is in the creation of personas. All three groups have their own ways to define personas that don’t tell the same story. And in many cases, these are called marketing personas even though they are very different. I’ll walk through each of them to try to define them separately, and talk about how to use each of them best and avoid common pitfalls.

The Analytics Persona
The analytics persona is the most straightforward of the group. The analytics persona is created by looking at clusters of users based on their usage and defining them based on that usage. At Pinterest, these were defined as core, casual, marginal, and dormant users. Core people came every day, casual people came every week, marginal people came every month, and dormant users had stopped coming to Pinterest altogether. This segmentation can be useful to see if your product is becoming more or less engaging over time. Key projects can include migrating people from, say, marginal to casual, by understanding the statistical differences between the two groups. Then, a product team might take something the casual group does that the marginal group does not and try to make the marginal group try to do that thing as well. Some of these differences are just correlation (or represent the people more so than the action itself being important), but some may be causal, and experiments like incentives or education to marginal users may help them become casual users.

The Product Persona
The product persona, just like the analytics persona, focuses on understanding existing users. In contrast to the the analytics persona, it relies on qualitative research to define who these people are, not just what actions they take. Someone that uses the product monthly can be the same personas as someone who uses it every day. When we built our personas at Grubhub, we had to use a mix of qualitative and quantitative research to define them. After a back of forth of customer calls and surveys, we were able to define four personas that used Grubhub based on two specific criteria: whether they ordered spontaneously or planned ahead of time, and whether they ordered for themselves or with others. When we mapped these personas back to our data, we were able to find that one segment was detrimental to serve because of high customer service costs, and that one segment was high potential, but low value currently because we had not built the right product for them yet. This helped inform our product strategy for the future.

The Marketing Persona
The marketing persona, unlike the first two personas, is usually a forward-looking persona: about who the company is going to try to reach. These are customers you want rather than customers you have. Marketing personas are common for product launches and market expansions since there are no existing users or data to build analytics or product personas.

The marketing persona exists to define a target market to go after. Marketers rarely try to target all people. They attempt to define a niche with specific needs (physical and emotional) and make their product attractive to that group. Marketers have many tools to define this persona. They pore over demographic and psychographic data and map competitive landscapes to spot opportunities for new markets. Since this persona is about targeting people outside the product, one common tool created during this process is a mapping of the target customer’s typical day. What they listen to on the radio on the trip to work, where they get their morning coffee, what they watch on TV during dinner, etc. From that, marketers identify opportunities to advertise to the target during one of those times to introduce them to the product.

Issues with Different Personas
All of these personas have pros and cons, and I recommend using them in tandem rather than a one size fits all approach. The danger with the analytics persona is it looks at people solely based on their activity and not also their motivations, their personalities, etc. There are key insights you will never find out from data that you can learn within ten minutes by talking to a customer. The product and the analytics personas also assume the current customers are the most important customers to understand. This is not always the case. Many times, you need to expand your market. Other times, focusing on existing users makes the product worse for incoming users.

Marketing personas also have their flaws. Since they are based on secondary data, marketers can sometimes invent personas that don’t exist or are too small to be important. This is why so many marketing teams create personas that are just cooler versions of themselves. It’s why whenever a fashion designer is asked who they design for, they say something like “a gallery owner in New York City.” They are maybe 500 of those people in the world. Now, I should be clear in that last scenario there is a blurred line between the marketing persona and the aspirations of the true persona, but you get the idea.

Marketing personas also can be unhelpful to some organic growth strategies or for products that have infinitely large markets since they are based on niches and targeting. When we tried to build marketing personas to target at Pinterest at around 50 million users, I asked, “What exactly are we going to do with this? We don’t spend money on advertising. Our targeting is defined by whoever searches for something on the internet that Pinterest is relevant for.” Similarly, does Google search have a target market? Sure, if you can call every single person with an internet connection a target. I bet you the Google Home team had a very specific target in mind for its launch though.

Personas of all types are also mistakenly used in lieu of personalization for many internet products. Email is a common example. Many companies still spend a lot of time creating dedicated emails for specific personas, segmenting lists, writing custom copy, and adding custom content. This frequently doesn’t makes sense in a world of personalization and one to one messaging. Pinterest has automated, personalized one to one messaging across email and push to over 200 million users. It doesn’t need to know what analytics, product, or marketing persona you are to be effective. This is not proprietary tech either. Many third party companies can help build this same type of system for smaller companies with less data.


Personas are very useful tools to help you identify opportunities to grow your business and better serve your customers. You should be using them in almost all phases of a company. Understanding the different types of personas, how you could use them, and how to prevent making mistakes with them is key to making sure they are worth the effort to define them.

Currently listening to Big Loada by Squarepusher.

Product Visionary vs. Product Leader

Many people want to work in product management. One of the most common questions I receive is how to break into product management. It’s a hard question for me to answer, because 1) there is no default path (the same is true for trying to land a business development role), and 2) most of these people really don’t know what they’re asking for. My most common response is, “Are you sure? Product management can kind of suck.” The reason for the dichotomy of people who haven’t done product management finding it so alluring, and people who have done it cautioning people trying to get in is the difference between what I call a product visionary and the product leader.

Product visionaries are who we all hear about in the press. They are the people who come up with brilliant products that go viral or solve real needs in the market that no one else thought of. They appear to be masters of finding product/market fit. I’ve been lucky enough to work with a few of them in my previous jobs and a few in the portfolio at Greylock. These tend to be founder/CEOs, and they generate brilliant insights that create product opportunities others don’t see. Ev Williams is on his third breakout product in Medium after Blogger and Twitter. Ben Rubin created two products that hit product/market fit in two years with Meerkat and now Houseparty.

Anyone working in technology hears these stories, and they think the shortest path to that sort of glory is becoming a product manager. They are excited to get to a role where they can drive the vision of a product, even if it’s only one part of the company. This excitement is exacerbated by the commonly propagated myth that the product manager is the mini-CEO of their product. The reality is that in 90+% of cases, product management is not about being a visionary. It’s about being a leader.

What does a product leader do at a tech company? It’s actually very little of creating a vision and a strategy from scratch. It’s about helping everyone understand what the vision and strategy is. It’s about communicating to the entire team why the company is doing what it is doing. It’s about building a process that helps a team execute on that vision. It’s about when there are competing visions, aligning and motivating the team to focus on one, and getting people to disagree and commit (including sometimes yourself). It’s about looking at data to measure if product changes are having a positive impact on the customer and the company’s growth. It’s about talking to users to understand why they’re doing what they’re doing, and the problems they still face even though your product exists. It’s about mentoring more junior people on your team, across product as well as engineering, design, and analytics. And they don’t have to listen to you, so you have to use influence rather than authority to be successful.

In the scaling phase of a startup, it’s product leadership that drives performance, not vision. Vision is needed early to find product/market fit and plot a course to scale, and then the less that vision wavers over time the better. This vision is usually done by the founder/CEO. The reason founders hire product managers and VPs of Product is not to set vision, but to help execute the vision. Don’t get me wrong; that will sometimes mean coming up with solutions to problems your customers face. If a company is scaling by having the founders solve all the customers’ problems instead of product teams, it will struggle. But much of the time, it will be wrangling the ideas of the individual engineers, designers, and analysts on your team and matching that to an overall vision set by the founder(s). It’s very rarely your ideas you’re executing on as a product leader, and it shouldn’t be.

I also don’t want to make it sounds like I am devaluing visionaries. They are, of course, critical in finding the initial idea(s) that create a growing company and maintaining a vision for that growing company. Having visionaries also becomes more important as you saturate your core market and need to tackle new value propositions to drive new growth opportunities. That is the ideal time for non-founder visionaries to enter a growing company. These are not going to be typical product managers or VP’s though. They are usually ex-founders. The best outcomes for these people entering an organization that is scaling is giving them a team and space to experiment with ideas until they find their own product/market fit, where a business unit is built out around that vision with product leaders to help them scale.

As you’re thinking about your business, think about whether you need a product visionary or a product leader. Most founder/CEOs are already visionaries, so they need a product leader to help execute. Some businesses minded founders need the opposite to be successful. If you’re thinking about product management, think about whether what you really want to be a is product visionary instead of a product leader. In that case, it might be a better idea to start a company than take a role expecting to execute on your vision and instead managing other people’s visions.

Currently listening to Ambivert Tools Vol. 3 by Lone.

How to Set Up and Hire an Analytics Team


Analytics has become a critical role at tech companies. A common question I receive is how to hire analysts and where they fit into an organizational structure. Below I share some tribal knowledge around common team structures, options I think work best and hiring tips that leverage this talent.

Functional vs. Embedded Teams
One of the first questions organizations face when hiring analysts is how they should structure the team. There are two common choices organizations pursue: a functional and an embedded model. The functional model is an analytics team reporting to a Head of Analytics. In the embedded model, every department (sales, marketing, product, customer service et al.) is in charge of solving for its analytics needs, hiring analysts for their teams when needed, and determining to whom they report.

Benefits of the functional model are having a senior seat at the table in major discussions for the company. The advantages this presents is getting analytics its own budget for tools and infrastructure and solving other analyst specific needs that may not be a top priority on any other specific department. The downside of a functional team is how analysts’ time are allocated. In a functional team, an analysts’ time is usually allocated on a project by project level, meaning they usually enter a project once that project is already well defined (whereas analytics could have helped by define the project, had it been involved earlier). And the analyst has not developed any specific expertise for that area. In my experience, analysts get frustrated in this model because they can’t go deep into any one area, and the other departments get frustrated because analysts provide a more cursory benefit than expected.

The embedded model solves a lot of these pain points, but introduces its own. With an embedded model, analysts are hired into one specific team, and therefore can develop expertise for that team very quickly. Teams are happy because they always have a teammate ready to help who understands their problems. While analysts seem to be happier in this model, the downsides are the reverse of the functional model. When there are cross-departmental analytics needs, they usually fall by the wayside. Investment in infrastructure and tooling is usually massively under-invested in, and it’s unclear where budget comes from to solve these needs.

At Apartments.com and at Grubhub, we implemented the embedded model. Marketing took control of analytics infrastructure initially, but we had trouble applying it cross-functionally. The analysts across all teams started meeting regularly to share learnings, but also limitations. When we added the Seamless team into Grubhub, they were used to the functional model. Analyzing the two together created awareness for me of a new approach.

The Hybrid Model
At Grubhub, the value of a dedicated analytics team for infrastructure and tools became clear, but also the value of the embedded analyst. Once I started at Pinterest and dealt with the functional model, we began to work toward a hybrid approach. This is a dedicated analytics team with a Head of Analytics, but with the analysts dedicated to specific areas full-time. So the person reports to a Head of Analytics, but sits with the department they support (in my case, the growth team). As the growth team grew, we created a Growth Analytics Lead who reported to the Head of Analytics and managed other growth analysts dedicated to specific areas, like conversion optimization or on-boarding. This allowed Pinterest to have the analytics seat at the table for budgets and resourcing, but the expertise at the team level to make the most impact. It’s now what I recommend to all teams that are scaling.

Hiring Analysts
If you are scaling a company and need more analytics help, it can be hard to understand who to hire that will actually help your teams. Hiring from analysts at other companies, especially larger ones, proved not to be a great strategy for me. I found during the interview process that most analysts were actually what I call “reporters”, in that they ran well defined reports for people who needed them but didn’t actually analyze anything. If you read analyst job descriptions, they inadvertently screen for these types of people by saying the candidate needs experience with all of these special tools. I can’t tell you how many job requirements that list Omniture (or whatever it’s called this week), Google Analytics, SPSS, Tableau, etc.

Experience with tools is not actually what you care about (though SQL and Excel are a big help). The more tools someone has worked with, the less likely they are to analyze the output of those tools. What you actually want are people who are analytically curious. Our first successful analyst at Grubhub was a new graduate whose cover letter talked about how he tracked his sleep patterns and his diet to find ways to improve his health. He crushed dozens of analysts with multiple years’ experience in our interviews because he was using his brain to analyze results instead of just report. So I now screen for roles where analysis, not reporting, is the unit of value. Many analyst teams at other companies are structured that way, but the majority are not.

You also have to test analytical ability in these interviews. At Grubhub, I gave people a laptop with a bunch of data in Excel and some vague questions to answer from it. The question was based on a real question we gave an analyst intern, who returned it to me saying there was no trend in the data. I ran the analysis myself and found one of the most important correlations for our business (the impact of restaurants per search on the likelihood to order). So I said, you have to be better than our intern to get an offer. It turned out to be an incredible screener. Most people never got far with the data, or their answers were spectacularly wrong. The few good analysts cut right through to a direct way to solve the problem and could explain it easily.

I like this approach because it actually shows the analyst what the job is (and if they’ll like it), and I can walk the candidate through how I would solve the problem so if they did get it wrong, they could learn something from it.

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Analytics team are one of the hardest teams to scale. One of the keys is building a model of a team that will scale with the needs of a company, and the hybrid model is the best model I have found to maximize the important levers of effectiveness (and happiness!). Structure is not all that is important though. Hiring the right candidate is critical, and the market is doing a poor job of preparing people for what emerging companies actually need in an analytics organization. If you can hire correctly and structure correctly though, you will have a competitive advantage over those who do not.

Currently listening to Rezzett by Rezzett.

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.

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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.

Getting Smart About Growth Podcast with Andrew Chen

Andrew Chen recently wrote a blog post about how growth is getting harder. I invited Andrew to the Greymatter podcast to chat more about why growth is getting harder, and more importantly, what to do about it.

We talk about how viral growth is on the decline in consumer, but not in B2B, and how to leverage paid referrals effectively. We also walk through trends in paid acquisition, how to find your first channel of growth, and much more.

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

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.

Four Strategies to Win Big with Low Frequency Marketplaces


Frequency creates habit which creates loyalty which creates profit. Uber and Lyft are successful because consumers need to get from A to B multiple times a day, forming habits that lead to long (and high!) lifetime values. Grubhub similarly benefited from people eating more than once per day.

But there aren’t that many business opportunities that have daily — or even weekly — frequencies. And those spaces have become very competitive. For example, how many food delivery companies can you name? Now add in groceries or meal kit cooking companies. All that just for the “eating” use case.

What if the natural frequency of use for a transactional business is low, like buying a house, selling a car, or booking a trip? How do you create a successful business if ideal frequency is quarterly or yearly or even once every few years? You would be unlikely to create a habit or loyalty, much less get the customer to remember your brand name. That is usually the case. If you don’t create loyalty, then you usually have re-acquire consumers when the need eventually arises again. This hurts customer acquisition costs and lifetime value. This fact makes building a successful business with low frequency extremely difficult.

With a low frequency business, you usually need to have a high average selling price to make up for the lack of frequency. While an order on Grubhub may cost you only $25, the average transaction size on Airbnb is hundreds of dollars. But a high average selling price alone is not enough to become a massively successful business. I’ve seen four distinct strategies for how to thrive in low frequency marketplaces. They all revolve around being top of mind when the transactional need occurs, no matter how infrequent that need is. I’ll start by talking about the most common approach, and then lead into some that are actually more valuable and defensible.

The Expedia Model (AKA SEO)
Companies that pursue this model: Thumbtack, Expedia, Apartments.com, WebMD

My first job was at Apartments.com. We were a classic low frequency marketplace. People search for apartments at most once a year, and there isn’t a whole lot of value you can provide in between apartment searches. So what did we do at Apartments.com? If you do not create habits or loyalty with initial use, users go back to the original way they solved the problem last time. Where do people go where they are searching for help renting an apartment? Usually, Google. So, the Apartments.com strategy was to rank well organically on Google so when people did search again for an apartment, they’d be likely to see us and use us for their search again.

SEO can be a very successful strategy, but the entire company has to be geared around success on Google. This strategy is also susceptible to platform shifts, like Google algorithm changes or Google deciding to compete with you. It also tends to shift companies toward portfolio models at scale. This is why Expedia owns Hotels.com, Orbitz, Hotwire, Travelocity, and Trivago, and why Priceline owns Booking.com and Kayak. When you rank #1 for your main keywords, the only way to grow is to own the #2 and #3 spots as well.

The Airbnb Model (AKA Better, Cheaper)
Companies that pursue this model: Airbnb, Rent The Runway, Poshmark

Sarah Tavel wrote a post about products that are 10x better and cheaper than their alternatives. You can definitely pursue this strategy even if you have low frequency. Airbnb was significantly cheaper than hotels, and many people, once they experienced Airbnb, found it a better experience as well. It was a more unique listing, in a “more real” part of the city, and they had a connection to a local. So, even though people only travel once or twice a year on average, when they do, they remember the Airbnb experience and start there directly instead of on Google, competing with the SEO behemoths of Expedia and Priceline.

Finding this level of differentiation in different industries is not easy, but worth contemplating. Airbnb is not the only startup that has entered a crowded space and grown rapidly by figuring out how to be 10x better and cheaper. RentTheRunway allows you to access high quality fashion without the high price, and without storing it, because dressing up is increasingly a low frequency occurrence.

The HotelTonight Model (AKA Insurance)
Companies that pursue this model: HotelTonight, One Medical, Lifelock, 1Password

There are certain businesses that are needed infrequently, but when they are needed, they are needed with great urgency. Example spaces include urgent care, being stuck in a random city unexpectedly, and fraud alerts. The key here is that someone keeps the app or account live despite a lack of usage because the fear of when it might be needed is so great. This is a hard strategy to pursue, but once the value prop is established, these companies remain sticky despite their lack of frequency.

The Houzz Model (AKA Engagement)
Companies that pursue this model: Houzz, Zillow, CreditKarma

Contrary to what many might think, keeping users engaged in a low frequency business is indeed possible: the key is a non-transactional experience. Many of these approaches have a “set and forget” component to them where they reach out with pertinent information in a more frequent way. Zillow is the first example I can remember that utilized this strategy. Even when not actively looking for houses to buy, Zillow kept users engaged by valuing their existing homes via the zestimates. CreditKarma reaches out with alerts and monthly credit check updates.

Houzz is a great example that is more recent. People remodel and redecorate their homes infrequently, but they are inspired more regularly. Houzz has a great product that shows home inspiration that can be saved and discussed, and when needed, but much more rarely, transacted.This is a product people engage directly with in instead of having to have content pushed to them

For this strategy to work, you essentially build a second product that enables frequent engagement — not a transactional product. Engagement strategies for low frequency marketplaces take advantage of an inherent human desire to stay up-to-date on things important to them. This won’t work for all industries. We actually tried this at Apartments.com, but were not successful because renters don’t care as much about investing in their living situation as homeowners.

A common confusion is that loyalty programs are an example of this. What loyalty programs usually do is increase frequency or target users that have high category frequency, like business travelers in the travel segment, rather than create loyalty from infrequent users. It is still a very valuable strategy, and I have blogged about loyalty programs if you want to learn more.

Of the four models I wrote about above, you will notice that not one of these is a brand model. Many of the sites listed in the SEO model have spent hundreds of millions of dollars building brands. Yet most travel searchers still start with Google. Brand is an extension of the Airbnb model, not its own strategy. If the product doesn’t deliver on a differentiated experience, brand building usually does not create loyalty.

So, if you’re building a low frequency business, do not dismay. There are many paths to still becoming a very large and differentiated business. These strategies are difficult but very rewarding if they are executed well.

Currently listening to Take Me Apart by Kelela.

Why Focus Is Critical to Growing Your Startup, Until It Isn’t

When I was a teenager, I told my dad about a friend and his dad and how they had seven businesses. He immediately replied, “And none of them make money.” I thought it was an extremely arrogant thing to say at the time, but later, I realized it might be the smartest piece of advice he ever gave me.

When I joined Grubhub, I quickly noticed the founders were incredibly good at staying focused. They said we were building a product for online ordering for food delivery — and only delivery — not pickup, not delivery of other items, not catering, and that’s all we would do for a long time. I remember thinking, “but there’s so much we could do in [XYZ]!” I was wrong. By staying focused on one thing, we were able to execute technically and operationally extremely well and grow the business both very successfully and efficiently. When we added pickup functionality four years later, it proved not to be a very valuable addition, and hurt our conversion rate on delivery.

If you have product/market fit in a large market, you should be disincentivized to work on anything outside of securing that market for a very long time. There is so much value in securing the market that any work on building new value propositions and new markets is destructive to securing the market you have already validated.

There is an interesting switch in the mindset of a startup that needs to occur when a startup hits product/market fit. This group of people that found product/market fit by creating something new now have to realize they should not work on any new value propositions for years. They now need to work on honing the current product value or getting more people to experience that value. Founders can easily hide from the issues of a startup by working on what they’re good at, and by definition, they’re usually good at creating new products. So that tends to be a founder’s solution to all problems. But it’s frequently destructive.

If a product team can work on innovation, iteration, or growth, they need to quickly shift on which of those they prioritize based on key milestones and value to the business. In this scenario, it’s important to define what innovation, iteration, and growth mean. In this context:

  • Innovation is defined as creating new value for customers or opening up value to new customers. This is Google creating Gmail.
  • Iteration is improving on the value proposition you already provide. This can range from small things like better filters for search results at Grubhub to large initiatives like UberPool. In both cases, they improve on the value proposition the company is already working on (making it easier to find food in the case of Grubhub, and being the most reliable and cheapest way to get from A to B in the case of Uber).
  • Growth is defined as anything that attempts to connect more people to the existing value of the service, like increasing a product’s virality or reducing its friction points.

I have graphed the rollercoaster of what that looks like below around the key milestone of product/market fit.

Market Saturation
The time to think about expanding into creating new value propositions or new markets is when you feel the pressure of market saturation. Depending on the size of the market, this may happen quickly or slowly over time. For Grubhub, expansion into new markets made sense after the company went public and had signed up most of the restaurants that performed delivery in the U.S. The only way the company could continue to grow was to expand more into cities that did not have a lot of delivery restaurants by doing the delivery themselves.

All markets are eventually saturated, and that means all growth will slow unless you create new products or open up new markets. But most entrepreneurs move to doing this too early because it’s how they created the initial value in the company. Timing when to work on iteration and growth and when to work on innovation are very important decisions for founders, and getting it right is the key difference to maximizing value and massively under-performing.