Tag Archives: startups

The Death Spiral of Startup CMOs

I’ve met with a lot of CMO/VP Marketing types at startups. Generally speaking, they do not have a long shelf life. I was curious why so many startup CMOs leave the company after a short period of time, so I dug in a bit to figure out why. I found that it’s partially due to bad hiring practices for startup executives (which I have previously blogged about), but even more common was that big brand traditional marketers have a tough time “scaling down” into the scrappy, digital-first world of startups tactically.

The traditional marketing executive is more likely to be a brand marketer from a large company than an online marketing or growth person from a startup. These marketers have built careers deploying big budgets and leveraging a few channels that really matter to tell the brand story. The main one of course is still TV. TV is great for brand building, but startups cannot wait for multiple years of investment in brand advertising to start to pay off. They typically will run out of cash before they start seeing a return on the investment in TV. (This problem is particularly acute if you’re a startup that is live only in a few cities and gradually expanding across the country — i.e. most marketplaces.)

How does a marketing executive get a quicker read on effectiveness, especially if their startup doesn’t cover the entire country? One way I’ve seen marketers try to solve this problem quickly is to buy local TV brand advertising in a test market and compare to a control test market where they have not bought TV advertising. They measure if brand metrics improve in the paid market during a three month test. If brand metrics improve, it should be leading indicators for sales to improve.

On paper, this makes sense, but in practice, it is the first trigger of the CMO death spiral. The problem is twofold. First, the cost of local TV is 10x more expensive than national advertising. That premium is hard to make up. Second, TV brand advertising is generally not very effective and even harder to drive quick results. With a typical brand-based buy, ads will run at many different times during many different, popular shows. So even though many people will be watching and thus exposed to the brand, the viewers’ focus is on the show and not on a call to action.

In short, big brand marketing executives have used large budgets for a test and gained small bits of exposure for the company. That exposure usually doesn’t transit into brand lift or sales, and the founders quickly lose confidence in the CMO. From there, I’ve seen it is a matter of months before they either get fired or lose their budget, which will make them leave.

But its not all fire and brimstone! There are savvy ways for marketers with big brand backgrounds to be successful at startups. For one thing, start by testing national, direct response TV. This type of ad buy targets people who are watching TV because they are bored — not because they are super engaged with the show. In this scenario, ads run at off-peak times or on less popular networks and only cover a small percentage of the country. You get the data on the exact times and areas where the ads run. Then, you can watch for an immediate response in website/app traffic right after the ad, and track those users to see what they end up doing. Agencies that sell direct response typically offer data science services to measure the impact, and you can augment their reports with tools like Convertro or C3 Metrics that do it.

Grubhub and Seamless are both good on-demand startup case studies before they merged: Seamless bought national, direct response TV ads while the service only covered 20% of the U.S. population. Grubhub did it with only 40% coverage. Seamless then used all the website traffic data outside their coverage areas to determine where to launch next. At Grubhub, we saw a response immediately during some of these ads airing, and could actually calculate not just brand lift surveys, but CPAs due to transactions.

A broader lesson here is that traditional target marketing can be expensive as the people selling advertising have learned that traditional marketers will pay a premium for it. And while that math still works for a CMO of a CPG brand, it won’t typically work for a startup.

What is the the best way for a CMO to be successful and stick with your company for the long term? First, they need to find ways to reach the target market they want to reach in a cost effective way. (I go deeper into this topic on my post on remnant inventory.) Seamless reached an insane amount of people in New York with DRTV ads, so it didn’t matter that some people in Boise saw them, too. Secondly, CMOs need to work on other ways to add value to the business besides telling big brand stories. Depending on the business, that can include understanding customers better, building communities, performance marketing, or product driven growth.

Currently listening to Children of Alice by Children of Alice.

The Real Value of PR for Startups

Many startups get obsessed with press. Not just CEOs, but also employees, friends of employees, and investors. Press, like paid acquisition, can be a drug though. I’ll talk about some of the ways the startup ecosystem perceives press is bad, what it is actually good for, and some outliers.

Press Abuse
First, let’s talk about some of the abuse of press by startups in the past. Business insider has a great recap of some of the issues Evernote faced in the couple years before their CEO stepped down. I’ll highlight this quote in particular.

“There was a feeling that we were working on the wrong priorities,” a former employee said. “It was clear the motive was to just continually drum up press. They had no idea how to optimize and improve growth.”

From The inside story of how $1 billion Evernote went from Silicon Valley darling to deep trouble

Startups frequently correlate press with growth, and think that they need to create new stories to drive growth. Then they start changing their entire product roadmap to drive press instead of value to customers. Startups should not look for PR to drive growth. PR doesn’t usually drive growth. And when it does, it’s a bump, not an engine. In startups, you want to build growth engines.

This is arguably a better problem than the second way PR drives bad acting in the ecosystem: founders getting addicted to the attention of press. It feels great when there’s a picture of you in the Wall Street Journal and your mom sees it and your dad’s friends. And founders frequently want to create that feeling. It’s as if appearing successful is more important than being successful. This can be a dangerous pattern.

Another problem with press is that it helps competitors understand exactly what you’re doing. I’ve talked in the past of the advantage of being a silent killer and why seeking press attracts unneeded competitor attention. I won’t repeat that here.

What Press Is Good For
A common refrain I tell startups is that press is good for three things: investor interest, employee interest, and links for SEO. I’ll break those down a bit, and talk about why they matter. First, let’s talk about investors. Very few businesses make sense for venture capital, but when they do, they increasingly need larger amounts of it. The venture business has extreme cases of FOMO. One deal can make all the difference. So investors rely on a lot of signals, and the tech press is one important one they look at not only to find new business to research, but to also get comfort in their interest in certain businesses. When Grubhub was raising its Series C from Benchmark, we got written up about our iPhone app on Techcrunch. For Grubhub’s growth, it didn’t matter at all. But it looked great for our fundraising.

Likewise with investors, potential employees rely on press to hear about potential new places of employment and to feel comfortable they are making a good move. Recruiting outreach will have a lower conversion rate if the person has never heard of your company, or if the person’s network hasn’t heard of you when they ask around. This can be overcome, especially if you’re well connected or can tout amazing metrics privately (like WhatsApp), but my guess is WhatsApp always had a harder time recruiting than Snapchat.

Lastly, press is very helpful for SEO. SEO is about relevance and authority, and links from press are a great way to increase authority in the eyes of search engines. Not only can press drive a quantity of links, but the quality of links are probably the highest most startups will receive. So many people link to news publications that they have among the highest domain authority on the internet. Now, getting one of these links won’t make you rank #1, but it’s part of a healthy SEO strategy.

When Can Press Be an Engine of Growth?
Now that I’ve set an argument that press doesn’t usually drive growth directly, I want to talk about some outliers of when it does. Press can drive growth if a certain story about your startup is self-perpetuating. Then, the volume of articles written about you happens in such a regular cadence that readers eventually act on it. I have seen this happen in two major startups over the last ten years in Twitter and Uber. With Twitter, it received such a tremendous uptake from journalists that they kept writing stories about it. This drove their readers to try it, and it became a sustained vehicle for organic acquisition (unfortunately with a low activation rate, but that isn’t press’s fault, but the product). Uber’s growth via press was a little more sculpted. They were able to create a story about the business of Uber fighting against corrupt incumbents and governments that were preventing a better product from succeeding. It was almost a movement. For years this created a headline about Uber almost every week as they faced this resistance in almost every city in which they launched.

I want to highlight that these are anomalies, and it’s unlikely you will be able to create sustained growth directly from press in your company. But that doesn’t mean press is worthless. Just remember what it’s good for, and make it work for you for those interests.

Don’t Become a Victim of One Key Metric

The One Key Metric, North Star Metric, or One Metric That Matters has become standard operating procedure in startups as a way to manage a growing business. Pick a metric that correlates the most to success, and make sure it is an activity metric, not a vanity metric. In principle, this solves a lot of problems. It has people chasing problems that affect user engagement instead of top line metrics that look nice for the business. I have seen it abused multiple times though, and I’ll point to a few examples of how it can go wrong.

Let’s start with Pinterest. Pinterest is a complicated ecosystem. It involves content creators (the people who make the content we link to), content curators (the people who bring the content into Pinterest), and content consumers (the people who view and save that content). Similar to a marketplace, all of these have to work in concert to create a strong product. If no new content comes in, there are less new things to save or consume, leading to a less engaging experience. Pinterest has tried various times over the years to optimize this complex ecosystem using one key metric. At first, it was MAUs. Then it became clear that the company could optimize usage on the margin, instead of very engaged users. So, the company then thought about what metric really showed a person got value out of what Pinterest showed them. This led to the creation of a WARC, a weekly active repinner or clicker. A repin is a save of content already on Pinterest. A click is a clickthrough to the source of the content from Pinterest. Both indicate Pinterest showed you something interesting. A weekly event made it impossible to optimize for marginal activity.

There are two issues at play here. The first is the combination of two actions: a repin and a click. This creates what our head of product calls false rigor. You can do an experiment that increases WARCs that might actually trade off repins for clicks or vice versa and not even realize it because the combined metric increased. Take that to the extreme, and the algorithm optimizes clickbait images instead of really interesting content, and the metrics make it appear that engagement is increasing. It might be, but it is an empty calorie form that will affect engagement in a very negative way over the long term.

The second issue is how it ignores the supply side of the network entirely. No team wants to spend time on increasing unique content or surfacing new content more often when there is tried and true content that we know drives clicks and repins. This will cause content recycling and stale content for a service that wants to provide new ideas. Obviously, Pinterest doesn’t use WARCs anymore as its one key metric, but the search for one key metric at all for a complex ecosystem like Pinterest over-simplifies how the ecosystem works and prevents anyone from focusing on understanding the different elements of that ecosystem. You want the opposite to be true. You want everyone focused on understanding how different elements work together in this ecosystem. The one key metric can make you think that is not important.

Another example is Grubhub vs. Seamless. These were very similar businesses with different key metrics. Grubhub never subscribed entirely to the one key metric philosophy, so we always looked at quite a few metrics to analyze the health of the business. But if we were forced to boil it down to one, it would be revenue. Seamless used gross merchandise volume. On the surface, these two appear to be the same. If you break the metrics down though, you’ll notice one difference, and it had a profound impact on how the businesses ran.

One way to think of it is that revenue is a subset of GMV, therefore GMV is a better metric to focus on. Another way to think of it is the reverse. Revenue equals GMV multiplied by a commission rate for the marketplace. So, what did they do differently because of this change? Well, Seamless optimized for orders and order size, as that increased GMV. Grubhub optimized for orders, order size, and average commission rate. So, while Seamless would show restaurants in alphabetical order in their search results, Grubhub sorted restaurants by the average commission we made from their orders. Later on, Grubhub had the opportunity to test average commission of a restaurant along with its conversion rate, to maximize that an order would happen and maximize its commission for the business. When GrubHub and Seamless became one company, this was one of the first changes that was made to the Seamless model as it would drastically increase revenue for the business even though it didn’t affect GMV.

This is not to say that revenue is a great one key metric. It may be better than GMV, but it’s not a good one. Homejoy, a service for cleaners, optimized for revenue. Their team found it was easier to optimize for revenue by driving first time use instead of repeat engagement. As a result, their retention rates were terrible, and they eventually shut down.

Startups are complicated businesses. Fooling anyone at the company that only one metric matters oversimplifies what is important to work on, and can create tradeoffs that companies don’t realize they are making. Figure out the portfolio of metrics that matter for a business and track them all religiously. You will always have to make tradeoffs between metrics in business, but they should be done explicitly and not hide opportunities.

Currently listening to A Mineral Love by Bibio.

If It Ain’t Fixed, Don’t Break It

Frequently, products achieve popularity out of nowhere. People don’t realize why or how a product got so popular, but it did. Now, much of the time, this is from years of hard work no one ever saw. As our co-founder at GrubHub put it, “we were an overnight success seven years in the making.” But sometimes, it really just does happen without people, inside or outside the company, knowing why. Especially with social products, sometimes things just take off. When you’re in one of these situations, you can do a couple of things to your product: not change it until you understand why it’s successful now, or try to harness what you understand into something better that fits your vision. This second approach can be a killer for startups, and I’ve seen it happen multiple times.

Let’s take two examples in the same space: Reddit and Digg. Both launched within six months of each other with missions to curate the best stories across the internet. Both became popular in sensational, but somewhat different ways, but Digg was clearly in breakout mode.

What happened after the end of that graph is a pretty interesting AB test. Digg kept changing things up, launching redesigns and changing policies. Some of these might have been experiments that showed positive metric increases even. Reddit kept the same design and the same features, allowing new “features” to come from the community via subreddits, like AMA. By the launch of Digg’s major redesign in August of 2010 (intended to take on elements from Twitter), Reddit exploded ahead of Digg.

This is what the long term result of these two strategies look like. Digg is a footnote of the internet, and Reddit is now a major force.

Now, neither of these companies are ideal scenarios. The best option in the situations these companies found themselves in is to deeply understand the value their product provides and to which customers, and to completely devote your team to increasing and expanding that value over time. But, if you can’t figure out exactly why something is working, it is better to do nothing then to start messing with your product in a way that may adversely affect the user experience. This has become one of my unintuitive laws of startups: if ain’t fixed, don’t break it. If you don’t know why something is working (meaning it’s fixed and not a variable), do nothing else but explore why the ecosystem works, and don’t change it until you do. If you can’t figure it out, it’s better to change nothing like Reddit and Craigslist than to take a shot in the dark like Digg.

Currently listening to Sisters by Odd Nosdam.

Hiring Startup Executives

I was meeting with a startup founder last week, and he started chatting about some advice he got after his latest round of investment about bringing in a senior management team. He then said he spent the last year doing that. I stopped him right there and asked “Are you batting .500?”. Only about half of those executives were still at the company, and the company promoted from within generally to fill those roles after the executives left. The reason I was able to ask about that batting average is that I have see this happen at many startups before. The new investor asks them to beef up their management team, so the founders recruit talent from bigger companies, and the company experiences, as this founder put it, “organ rejection” way too often.

This advice from investors to scaling companies is very common, but I wish those investors would provide more advice on who actually is a good fit for startup executive roles. Startups are very special animals, and they have different stages. Many founders look for executives at companies they want to emulate someday, but don’t test for if that executive can scale down to their smaller environment. There are many executives that are great for public companies, but terrible for startups, and many executives that are great at one stage of a startup, but terrible for others. What founders need to screen for, I might argue about all else, is adaptability and pragmatism.

Why is adaptability important? Because it will be something that is tested every day starting the first day. The startup will have less process, less infrastructure, and a different way of accomplishing things than the executive is used to. Executives that are poor fits for startup will try to copy and paste the approach from their (usually much bigger) former company without adapting it to stage, talent, or business model. It’s easy for founders to be fooled by this early on because they think “this is why I hired this person – to bring in best practices”. That is wrong. Great startup executives spend all their time starting out learning about how an organization works so they can create new processes and ways of accomplishing things that will enhance what the startup is already doing. When we brought on a VP of Marketing at GrubHub, she spent all her time soaking up what was going on and not making any personnel changes. It turns out she didn’t need to make many to be successful. We were growing faster, had a new brand and better coverage of our marketing initiatives by adding only two people and one consultant in the first year.

Why is pragmatism important? As many startups forgot over the last couple of years, startups are on a timer. The timer is the amount of runway you have, and what the startups needs to do is find a sustainable model before that timer gets to zero. Poor startup executives have their way of doing things, and that is usually correlated with needing to create a very big team. They will want to do this as soon as possible, with accelerates burn, shortening the runway before doing anything that will speed up the ability to find a sustainable model. I remember meeting with a new startup exec, and had her run me through her plan for building a team. She was in maybe her second week, and at the end of our conversation I counted at least 15 hires she needed to make. I thought, “this isn’t going work.” She lasted about six months. A good startup executive learns before hiring, and tries things before committing to them fully. Once they know something works, they try to build scale and infrastructure around it. A good startup executive thinks in terms of costs: opportunity costs, capital costs, and payroll. Good executives will trade on opportunity costs and capital costs before payroll because salaries are generally the most expensive and the hardest to change without serious morale implications (layoffs, salary reductions, et al.).

Startup founders shouldn’t feel like batting .500 is good in executive hiring. Let’s all strive to improve that average by searching for the right people from the start by testing for adaptability and pragmatism. You’ll hire a better team, cause less churn on your team, and be more productive.

Product-Market Fit Requires Arbitrage

One of the most discussed topics for startup is product-market fit. Popularized by Marc Andreessen, product-market fit is defined as:

Product/market fit means being in a good market with a product that can satisfy that market.

Various growth people have attempted to quantify if you have reached product-market fit. Sean Ellis uses a survey model. Brian Balfour uses a cohort model. I prefer Brian’s approach here, but it’s missing an element that’s crucial to growing a business that I want to talk about.

First, let’s talk about what’s key about Brian’s model, a flattened retention curve. This is crucial as it shows a segment of people finding long term value in a product. So, let’s look at what the retention curve shows us. It shows us the usage rates of the aggregation of users during a period of time, say, one month. If you need help building a retention curve, read this. A retention curve that is a candidate for product-market fit looks like this:

The y axis is the percent of users doing the core action of a product. The x axis in this case is months, but it can be any time unit that makes sense for the business. What makes this usage pattern a candidate for product-market fit is that the curve flattens, and does fairly quickly i.e. less than one year. What else do you need to know if you are at product-market fit? Well, how much revenue that curve represents per user, and can I acquire more people at a price less than that revenue.

If you are a revenue generating business, a cohort analysis can determine a lifetime value. If the core action is revenue generating, you can do one cohort for number of people who did at least one action, and another cohort for actions per user during the period, and another cohort for average transaction size for those who did the core action. All of this together signifies a lifetime value (active users x times active x revenue per transaction).

Now, an important decision for every startup is how do you define lifetime. I prefer to simplify this question instead to what is your intended payback period. What that means is how long you are willing to wait for an amount spent on a new user to get paid back to the business via that user’s transactions. Obviously, every founder would like that to be on first purchase if possible, but that rarely is possible. The best way to answer this question is to look with your data how far out you can reasonably predict what users who come in today will do with some accuracy. For startups, this typically is not very far into the future, maybe three months. I typically advise startups to start at three months and increase it to six months over time. Later stage startups typically move to one year. I rarely would advise a company to have a payback period longer than one year as you need to start factoring in the time value of money, and predicting that far into the future is very hard for all but the most stable businesses.

So, if you have your retention curve and your payback period, to truly know if you are at product-market fit, you have to ask: can I acquire more customers at a price where I hit my payback period? If you can, you are at product-market fit, which means it’s time to focus on growth and scaling. If you can’t, you are not, and need to focus on improving your product. You either need to make more money per transaction or increase the amount of times users transact.

Some of you might be asking: what if you don’t have a business model yet? The answer is simple then. Have a retention curve that flattens, and be able to grow customers organically at that same curve. If you can’t do that and need to spend money on advertising to grow, you are not at product-market fit.

Other might also ask: what if you are a marketplace where acquisition can take place on both sides? If you acquire users on both sides at the wrong payback period, you’ll spend more than you’ll ever make. Well, most marketplaces use one side that they pay for to attract another side organically. Another strategy is to treat the supply side as a sunk cost because there are a finite amount of them. The last strategy here is to set very conservative payback periods on both supply and demand sides so that in addition they nowhere near add up to something more than the aggregate lifetime value for the company.

Currently listening to From Joy by Kyle Hall.

Scaling Up, The Three Stages of a Startup and Common Scaling Mistakes

One of the biggest mistakes I’ve seen management make at startups is mis-managing how their startups scale. There are distinct stages of a startup. Early on, you prioritize speed over precision. Later on, you will trade off speed for understanding exactly what makes the numbers move. Early on, you prioritize self-managers and weed out employees that need a lot of support to be successful. Later on, you have to expand into developing people and training as most employees do not thrive being thrown into the deep end right away. I’ll talk about these stages, the right way to think about how you manage processes, teams, and rigor during these times, and some mistakes to avoid.

The Early Stage
“What are lemons? Okay, I’ll go find some.”

When you are early in a startup, you need to have a bias toward getting stuff done. Strategic thinking doesn’t matter a whole lot. You need to try things and see what works. You do not have a lot of data, so when you try things, you are looking for huge, noticeable gains in aggregate data. You’re not doing any AB testing. Your analytics investment is small, and you pay attention to a small number of metrics. Every investor question requires you to get back to them because you’ve never done that analysis before, or you don’t have enough data yet. You’re also looking to hire entrepreneurial, self-starting jack of all trades. These are people that spot opportunities and just immediately go work on them even if they don’t have much experience. They don’t ask for permission; they just go try to figure it out. Whether it’s manning the phones, pulling data, optimizing an Adwords account, they’ll do it. You use the cheapest tools you can find to achieve your needs. In the early stage, you also want to do as few things as possible, especially from a product perspective. The CEO is deciding many things on a day to day basis and manages almost everyone. The early stage is defined by a few rules:

  • Speed > Precision
  • Jack of all trades > Specialist
  • Done > Perfect
  • Focus > Breadth
  • Execution > Strategy
  • Hungry > Seasoned
  • Cheap > Robust
  • Teamwork > Process
  • Doers > Managers

The Middle Stage
“Let’s make some lemonade.”

In the middle stage, the things that were easy for jacks of all trades to cover with a few thousand visits or a few customers become impossible to maintain with more customers and more visits. So, you start to hire more specialized people, but still relatively hungry and more junior, and people are still doing multiple jobs. You bring in a few or promote some people to managers so the CEO doesn’t have tons of direct reports. The CEO isn’t aware of all the decisions being made, but keeps the company still very focused. The manager’s job is mainly to clear roadblocks for the executors, and they generally still do individual contributor work themselves. The managers handle some of the what is now cross-functional communication gaps, and put in some lightweight process to organize what’s going on. Focus is still extremely important, but you start to think about some expansion opportunities. You typically have over a year’s worth of data at this stage, so you expend some effort understanding seasonality, maybe making some projections. You have more metrics and formalize things like LTV, CPA, runway, and can generally answer investor questions when they are asked. You still mostly rely on SQL and Excel for detailed analysis instead of full dashboards or analytics suites. You do start to invest in some better tools since you have scaled beyond some of your initial choices. The new rules:

  • Speed with some precision
  • Specialist = Jack of all trades
  • Doers with some doer-managers
  • Focus > Breadth
  • Execution > Strategy
  • Hungry > Seasoned
  • Cheap and robust are more closely traded off
  • Some process
  • Done > Perfect

The Late Stage
“Screw your lemons. We ain’t going anywhere until I get 5 apples, ten oranges, and some kiwi”.

In the late stage, you have a large team, and you need full-time managers and a senior leadership team that can filter communication up and down. The CEO is approving large, strategic decisions made below him rather than driving every decision. Every individual contributor has a specialized role, is much more seasoned than before, and you’re appropriately staffed for every job you’ve prioritized to get done. You create a good amount of process to streamline work between teams. Shipping changes in product and marketing are hard to measure for effectiveness, and could have significant negative effects, so you rely on experiments to measure impact of your work and prevent catastrophe. You invest in analytics tools to easily understand the high level and detailed metrics without having to do custom work. You start to work on expansion opportunities as you max out your initial product and market’s value. You invest in sophisticated forecasts so you can understand if you’re off track and what causes are for fluctuations. You buy or build enterprise level tools to help specialists do their jobs better, whether that’s advanced analytics packages, marketing software, sales CRM systems, etc. You also start to codify specific strategies before executing instead of just trying different things and seeing what works. This is valuable to make sure you’re going in the right direction, and to build organizational confidence in different teams who don’t work so closely together anymore. The new, new rules:

  • Precision > Speed
  • Specialist > Jack of All Trades
  • Managers + Specialists
  • Breadth traded off with focus
  • Strategy just as important as execution
  • Seasoned > Hungry
  • Robust > Cheap
  • Process first
  • Perfect vs. done more clearly traded off

The Common Mistakes: Not Scaling and Scaling Too Early
The biggest mistake I see startups make is staying in the early stage longer than they should, or adapting the policies of the late stage too early. The former is typically led by the CEO. In this case, the CEO loves being hands-on and can’t let go to help the company scale. What this actually does is keep the company in the early stage and prevent it from growing. I’ve seen it happen. The best way to help the CEO realize he is entering a new stage is to have a board shepherd that process or former CEOs as mentors who have gone through this transition. It isn’t easy.

Conversely, many CEOs see what the best companies do and assume their company should do that, not recognizing the difference in stage between them. These companies invest in robust analytics and testing solutions before they have enough data to use them, hire full-time people managers too early who need to justify their existence by hiring big teams, invest in too much process that inhibits growth, and have a team of too many strategists and too few doers. Their burn rates are high, and their growth rates are low, and they typically need to raise huge rounds to continue operating. The best way to prevent companies from doing this is to have them recognize the stage they’re in and hire people appropriate for that stage. A too late stage of hire in an early company will cause massive distraction, culture shock, and an increased burn rate. These CEOs need to let the problems of their business guide them to scale up in stage, not emulate other companies at later stages. It isn’t about where you want to be, but where you are today that should judge how you run the company.

Currently listening to Hallucinogen by Kelela.

Entering Marketing at a Startup

With software eating the world, many marketers are deciding to enter startup organizations for the first time. CEOs, being told they need a brand, or to spend on paid acquisition or insert X marketing activity here, are trying to find marketing talent in a industry that has a dearth of it, so they’re happy to accept people from other industries or larger technology companies. Sounds like a win-win, right? Not exactly. The culture shock as a marketer from switching to a startup from almost any other type of company is hard to over-estimate. The switch chews up and spits out as many, if not more, people than it accepts. I’ll talk a bit why that happens and what marketers can do about it to be more successful.

The first thing you need to accept is that marketing is not understood as a function at most startups, and therefore it is not respected. These are organizations that have gotten to where they are without marketing, have probably never read a definition of marketing, and whose connotation of marketing is the seediest of snake oil sellers you can imagine. Starting from a position of distrust in a new position in a new company is never a fantastic option, but it’s where almost all startup marketers start. And they are not prepared for it. I remember a meeting my first week at GrubHub where one of the co-founders suggested firing me (referring to me in the third person even though I was there) to the other co-founder and just growing organically. The following week, I proposed doing email marketing to retain users after their first purchase and was met with a flat out “no, that’s not a good use of time.”

No one tells you to expect these types of barriers before you join, and many marketers never get past it. Marketer’s first instinct typically is to rely on the best practices argument. “But, every other company does this.” I can say from myself and watching several other marketers try it that it’s pretty much a worthless argument. If it’s a best practice in marketing, but your company thinks marketing is bullshit, then your argument doesn’t hold water. So, if best practices won’t work, you need to find arguments that will.

In the past, I’ve recommended AB testing with people, and I still do. In this scenario, there is one strategy that is almost guaranteed to work, and that is relying on data. Entrepreneurs live and die by metrics, and with most startups being founded by engineers, they trust data above all else. So, marketers first need to think about how they can generate data on their activities. AB testing is generally the best way, even if it’s pretty crude. The other is to write out a clear strategy. If something is a best practice, it’s because it’s logical. Breaking down the logic in detail can be the right way to help those not familiar with your craft why something is the right course of action. I prefer to write clear “because A causes B, and B causes C, and we want C, we should do A” type papers, but feel free to adopt your own style. In the email marketing example, I started sending emails manually and built the campaign up to drive thousands of orders before I proposed a technical solution again. It was much clearer the value then, and the project was accepted. In the Pinterest case, one of our marketers just started sending emails without telling people, and it’s now a significant re-engagement channel for us.

One caveat: don’t manipulate data for your own gain. This is a mistake I see many marketers make. In the absence of data, you need to work to generate reliable data, not appropriate available data to try to explain your impact in a way that is forced. For example, when you see a lift in metrics, I’ve seen many marketers jump in to grab credit e.g. “That’s because of our Mother’s Day campaign on Facebook!” when the campaign was only seen by a couple hundred people and only had a dozen likes. This further deteriorates credibility, as startup employees see through it. Only claim credit when you’re confident and have the data to back up your claim.

Currently listening to 6613 by DJ Rashad.

First to Product-Market Scale

I like to think of this blog as balancing between business school theory and startup execution. While there are many places they don’t add up, usually the combination of the two provides an insightful truth that is hard to see without the theory plus the experience of trying to implement it. One area where I struggled for a while between my experience and the theory was the first mover disadvantage as it relates to barriers to entry.

The first mover disadvantage states that, while being the first firm in a market to do something has its advantages in terms of brand recognition and speed to market, the firm bares an even greater cost of R&D, education, etc. that second movers do not. These second movers can fast follow without all of these additional costs the first mover had to deal with and quickly compete. See HBR for details. In my Chicago Booth studies, both Eric Lefkofsky (CEO of Groupon who taught Building Internet Startups) and up and coming economist Matthew Gentzkow (who taught Competitive Strategy) argued about how potent the first mover disadvantage would be for Groupon, and that now that everyone knew how profitable the Groupon model was, it would be copied as there was no competitive advantage.

In a case study about Groupon in Gentzkow’s class, I did a one man filibuster against this argument. I looked at the data. During the time of the class, Facebook and OpenTable were winding down their Groupon clones, Yelp called theirs “not a priority” six months after shifting almost their entire team to work on it. Living Social started having financial issues. Groupon was winning despite the first mover disadvantage. The question was not would Groupon win, it what the prize was going to be for being first. Why was that the case when economics would argue against it?

I saw this same phenomenon in my own work at GrubHub. Online ordering was not a hard technology to clone, and once we had educated restaurants on the value of online ordering and shown them the additional business we could bring them, a competitor would have a much easier time with their pitch. Yet, we were still winning in every market except New York and college towns, where competitors had entered well before us. After acquiring those competitors, we talked candidly about competing with each other. The folks at Seamless (the New York competitor) talked repeatedly about feeling boxed out due to GrubHub’s first mover advantage in the rest of the country, even though we weren’t first in many of those areas.

Having taken two classes emphasizing first mover disadvantage before hearing this, I knew something wasn’t right, but couldn’t quite nail the hidden truth. Last year, I read Andy Rachleff’s post on first to product market fit. Andy argued it’s not about first mover advantage, it’s about first to product-market fit. It felt warmer, but not quite right either. GrubHub was not first to product-market fit in many of the markets it entered and later dominated.

If we tweak Andy’s definition slightly from fit to scale, the model fits better. One thing about GrubHub is that everything we thought about we thought about at scale and with velocity. We would systematically try to grow every market we entered with the same focus and the same process. If we achieved this, we would overtake successful players that were already in the market. It also didn’t matter who entered the market and tried the same after that. We had already won. Product-market fit implies a product that works with a small product, and the next step in the company’s evolution should be scale. So, the target for startups or large firms entering new markets in order to be successful should not just be product-market fit, but product-market scale. If you achieve that, you dominate markets and cannot seem to be usurped no matter how few barriers to entry you have.

Value Trade Offs in Online Food Delivery

If you’ve been following the online food delivery space, now is a pretty exciting time. Multiple services are starting up, competing on different value propositions, and many corporations are theoretically launching businesses here as well. There is one clear giant, and it is unclear if any of the upstarts will challenge them. But what is so interesting is how large companies entering the space and new startups alike are confronting the different value trade offs in online food delivery. I’ll first describe the different types of services, their different components, and then their trade offs.

Types of Services

Marketplaces
Services: GrubHub, Seamless, Eat24
Marketplaces aggregates delivery restaurants and allow diners to search for restaurants that deliver to them. The restaurants do their own delivery.

Delivery Services
Services: Postmates, DoorDash, Caviar, Uber Eats
Delivery services offer delivery from restaurants that don’t do their own delivery and deliver the food themselves.

Delivery Only Restaurants
Services: Sprig, Spoonrocket, Maple
Delivery only restaurants have no storefront. They just make food that is available for delivery and deliver the food themselves.

Delivery Only Restaurants that Require Prep
Services: Munchery, Gobble
These restaurant services require some prep work ranging from microwave to stove or oven, but usually it’s only a few minutes of prep required.

Delivery of Ingredients/Recipe Only
Services: Blue Apron, Plated
These services deliver the ingredients and the recipe required to make a meal, but the diner has to cook it themselves.

Delivery of Groceries
Services: Instacart, Fresh Direct
These services deliver whatever items you want from a grocery store.

I won’t go into corporate focused services in this post.

Value Propositions

Variety
People rarely agree on what food they like, let alone on which food they want to eat at a specific time. While GrubHub is currently unmatched in its variety nationally with over 35,000 restaurants, different companies are tackling variety on both sides of the spectrum. Postmates will theoretically offer the most variety as it will pick up food from any establishment. Online food companies like Sprig, Munchery, and Spoonrocket limit options considerably each day. Doordash, Uber Eats and Caviar have the most confusing approach here, as their ability to use their own delivery network does not restrict them to restaurants who already offer delivery, but they curate the list to provide supposedly only great options. GrubHub works with every restaurant that does delivery already, and has expanded the market by convincing many restaurants to start delivery because they see how well other restaurants do by offering that option with GrubHub.

Prep
Convenience has two components: how much work you have to do to eat (prep), and how quickly the food arrives (time). Marketplaces, delivery only restaurants and delivery services deliver ready-to-eat food. Then, there are some that require a little prep, some that require full cooking, and some that require figuring out what to cook and cooking it.

Time
The other convenience layer is time. Delivery only restaurants target 10 minute delivery times by pre-pepping meals and loading them into the cars of their drivers, whereas GrubHub and Eat24 are closer to 45 minutes to an hour depending on the restaurant’s location and type of food. Delivery services tend to take over an hour as they require extra coordination with restaurants. I believe Uber Eats is attempting a hybrid of the delivery service model and the delivery only restaurant model, but I can’t confirm. None of the other services deliver food ready to eat, but they range on how much work is required. The some prep restaurants are more like 10 minutes to heat, and ingredient/recipe services require typically cook time of over 30 minutes to an hour.

Price
Price varies for all of these services. Delivery only restaurants target less than $15 everything included. While that is possible in some cities with marketplaces, it is not in others. Ingredient/recipe delivery services have plans that are under $10 per person. Delivery services tend to charge a fee for delivery or mark up restaurant prices, so they are typically more at $20 and above per person. This incentivizes group order to spread the delivery cost around to multiple people. This is why most delivery services end up focusing on corporate catering instead of consumers over time. Prep delivery only restaurants have different plans to entice regular ordering.

Quality
In marketplaces, the quality options are set by the market, and the diner chooses how good they want their food to be. Delivery services have the same option with perhaps a higher end than marketplaces as the very best restaurants tend not to deliver. The delivery only restaurants tend to be cheap and low quality so far. Whether you had a hand in making it yourself can also be considered a quality parameter, as some people to tend to prefer things they cook themselves.

Planning
With food delivery, one typically does not need to plan in advance to use it, but with new grocery delivery and ingredient/recipe prep services, diners need to plan ahead of time to use the service.

Trade Offs

As you start playing with these value propositions, you recognize some additional constraints. I don’t need to lecture you in price vs. quality. That’s pretty obvious. But what may not be obvious is the trade off between time and quality. Even if you are delivering food from an amazing restaurant, if it takes a long time to get to a diner, it’s typically not very amazing by the time it gets there due to the food being cold. The other interesting trade off is quality vs. variety. At GrubHub, our stance was akin to the saying “quantity is a quality all its own.” In that, if you organized all of the supply, even if you had many amazing restaurants and many not so good ones, the good ones quickly emerged to the top due to ratings and reviews and overall quality of the service improved. So, all GrubHub worried about was variety and convenience, with convenience mostly limited to the ordering and customer service experience. Price and quality were set by the market, but presumably, variety solved quality, with a cap on the high end.

What these new services are doing is taking constants in the marketplace equation and making them variables: prep, time, price, and quality. It is way too early to tell if changing the equation is valuable to the broader market as GrubHub does way more orders in a day than the rest of these services combined. But it will be interesting to watch.