Starting the Lawnmower

Have you ever tried to start an old lawnmower?

If you haven’t, I bet you’ve at least watched someone struggle with it.

You yank on the starter cord with all your might and…nothing happens. You try it again…still nothing. After a few more tries, you’re sweaty and frustrated. But the grass still needs cutting.

You keep trying.

Three more pulls. Still nothing. You curse and kick the mower.

What’s wrong? Are you not pulling hard enough? Are you not strong enough? Or, is something else wrong?

Next time, you pull the cord and the motor fires. But, just briefly. It quickly stutters out again. Progress of a kind.

Finally, you realize that there’s not enough gas in the tank. Once you top it off, a few more pulls and you’re mowing grass!

This is what starting a company is like. Except slower.

With a startup, each pull of the starter cord is equally tiring. But, each pull can take weeks.

You don’t know how many pulls are going to be needed. You don’t know if it’s ever going to start working.

You just have to keep pulling, and keep tweaking things. Just like the mower, sometimes it will start but then, quickly stop again. It’s just as frustrating but for much longer.

This is why founders need grit to keep pulling. They also need smarts to work out why it’s not working.

But, there’s a better way than struggling alone. What if you had an experienced team to help? A team of lawnmower experts who were there to check there’s enough gas in the tank, to top it off if there isn’t, to make sure the spark plug is not fouled, to make sure the blade is adjusted correctly, or just grab the cord and pull with you.

That’s what a Venture Studio does. A Venture Studio helps you start the lawnmower so you can start mowing.

At Platform Venture Studio, we’re helping the next generation of entrepreneurs start their lawnmowers. If you’ve ever thought about being a startup founder yourself, or just want to checkout the companies we’re building, please consider joining Platform.

Everything You Always Wanted to Know about Unit Economics but were Afraid to Ask – Part 2

This is Part 2 of “Everything You Always Wanted to Know about Unit Economics but were Afraid to Ask”.

This part covers the Unit Economics of Two-sided Marketplaces. If you need an introduction (or refresher) on Unit Economics, please read Part 1 first.

Definition

Firstly, let’s define two-sided marketplaces: these are businesses that make money by connecting Supply with Demand. Examples are Wonolo, Uber, eBay, and Airbnb.

Sometimes, which side is “Supply” versus “Demand” can be counterintuitive. To make it simple, Demand is normally the side that pays the money. The marketplace business collects that money, takes out its slice, and passes the rest of the money to the Supply side.

At Wonolo, our “Supply” is our Wonoloers (the workers who do the job) and our “Demand” is our customers (companies that pay money to have the work done).

For Uber’s core business, riders are the “Demand”, and “Supply” are the drivers and their cars. For Airbnb, “Demand” are guests and “Supply” are hosts with properties.

Unit Economics

Unit Economics get tricky in two-sided marketplaces because you have to consider both Demand and Supply.

To start with, you have two acquisition funnels, two CACs, two LTVs, two Break-even Points, etc. You also have marketplace effects to consider.

This provides for a number of possible approaches to understanding Unit Economics:

model Supply and Demand in isolation,
model the Demand side as your “unit”, and consider Supply-side costs as variable costs,
model your Supply side as your “unit”, and consider Demand-side costs as variable costs, or
combine Supply and Demand and model at the individual interaction level – e.g. your unit is a job (Wonolo), ride (Uber), or stay (Airbnb).

In practice, you will often end up doing all of the above to provide multiple perspectives to investors.

However, for simplicity, my strong recommendation is that you start with modeling your Demand side as your “unit”. I find that this is the most intuitive for most cases. It most closely resembles the modeling of Unit Economics for simpler SaaS businesses, which are what most investors are familiar with.

Extra Layers of Complexity

In addition to considering how to model Supply and Demand, many marketplace businesses have additional complexities which make their Unit Economics even harder to model.

These complexities are not unique to marketplaces but are seen in many of them:

Variable spend: unlike a simple SaaS business, where customers sign-up and pay a fixed amount each month (MRR), marketplace users can have highly variable spend month-to-month. This has large impacts on LTV and break-even, and can make averages misleading.
Variable pricing: often the fee that customers pay is negotiated and/or variable, meaning margin is variable.
Seasonality: if a business is seasonal, it means the Break-even Point depends on when in the year a customer signs-up. This makes it hard to compare cohorts.
Ambiguous churn: for a simple SaaS business, customers sign up and then pay every month until they explicitly cancel their service. In contrast, many marketplaces only make money when supply and demand transact. Either or both sides can go dormant at any time and then come back at any time. This makes modeling churn hard and churn is a big determinant for LTV.

There are various ways to cut through these complexities, including comparing cohorts of like customers, and seasonal adjustments, but I’ll save those for a potential Part 3.

Marketplace Example: Wonolo

Since Wonolo is a business I’m intimately familiar with, I’m going to use it as my example. However, the concepts here will be very similar for any two-sided marketplace.

Note: the numbers I’m using here are for illustrative purposes only, and to make for simple math. Several details that have a smaller impact on the numbers are omitted for simplicity.

Demand-side

Per the above, we’re going to model our Demand-side first.

Demand-side CAC

At Wonolo, our demand side is our customers. A typical customer is a logistics company with a warehouse needing Wonoloers (workers) to work on their production line.

The primary components of our Demand-side CAC are marketing and sales.

For illustrative purposes, let’s say the marketing component is $1,000 and the sales component is $4,000, making a total Demand-side CAC of $5,000. (Remember that these are averages and can vary widely.)

Jobs

Our unit of interaction in our marketplace is the job. e.g. an 8 hour shift in a warehouse.

Length of shift and the hourly pay rate vary but, on average, let’s say a shift pays $100 and the average fee to our customer is 50%.

So, for each job, we charge the customer $150. We pass $100 straight on to the Wonoloer who did the job, leaving us with $50.*

Combining this with the CAC above, we can see that it will take the customer using Wonolo for 100 jobs to pay back our Demand-side CAC [$5,000 / $50]. This gives us some sense, but not a full picture, since it doesn’t include the time dimension or the Supply side.

*Staffing companies typically lead with their Gross Revenue, which includes the wages to be paid to the worker. I think this is a misleading “vanity metric” because the wages are simply passed through. At Wonolo, we lead with Net Revenue.

Demand-side Break-even Point

So, we know that it takes 100 jobs to recoup our Demand-side CAC. But, we don’t know how long that takes.

To calculate that, we have to know how frequently our customers use our service – frequency of interaction (also referred to as “frequency of transaction”).

Let’s say that, on average, our customers post 2 jobs (shifts) per day. We therefore now know that we recoup our Demand Side CAC after 50 days usage [100 / 2 = 50].

We also know that we hit our CAC-Doubling Point at twice this – 100 days.

Customer Lifetime & LTV

The next question we need to ask is whether we keep customers for at least 50 days. If we don’t, then we lose them before we pay back our Demand-side CAC, and our Unit Economics are negative.

Good news: we keep an average customer for 500 days. Easily long enough to recoup our Demand-side CAC.

We can also now calculate our Customer Lifetime Value (LTV). 2 shifts per day, at $50 per shift, for 500 days, is an average LTV of $50,000 [2 x $50 x 500].

Supply Side

So far, we haven’t considered the Supply side – in Wonolo’s case, our Wonoloers (workers).

In this approach, we’re going to model the Supply-side costs as Variable Costs incurred in delivering our service to our Demand side (customers).

How much Supply is needed?

The first question to answer is, how much Supply do we need to satisfy our Demand?

In Wonolo’s case – and since the “unit” we’re modeling is our Customer – the question is, how many Wonoloers do we need to do the work one Customer needs done?

The first answer might be to say we need one Wonoloer per shift. However, Wonoloers work multiple times for the same customer, so we don’t need nearly that many.

It turns out that, on average, a Wonoloer works for a given customer 10 times. So we only need 1 Wonoloer for every 10 jobs that our Customer needs done.

Supply-side CAC

Now we need to consider how much it costs us to acquire a Wonoloer.

There are two primary elements here: marketing cost (to get them to download the Wonolo app) and onboarding costs (to get them ready to work).

These costs add up to around $50 to find a Wonoloer and get them ready to do their first job. i.e. our Supply-side CAC is $50.

Impact of Supply-side CAC on Demand Side

Now we know how much Supply we need to satisfy a customer’s demand, and we know how much that Supply costs, we can put it all together.

For convenience, let’s look at a year.

Over the course of a year, a customer will use us for about 730 jobs on average [2 jobs per day for ~365 days].

So, on average, we’ll need 73 Wonoloers to meet that demand [730 / 10], and it’ll cost us $3,650 to acquire them [73 x $50].

Let’s look at how that impacts our overall unit economics calculations.

One approach is to look at the individual job level. It costs $3,650 to get the supply for our customer’s 730 jobs per year. So, per job, it costs us $5 to acquire the needed supply (Wonoloers) [$3,650 / 730 = $5].

Remember that we receive about $50 per job as our fee; so this means we’re spending 10% of that on Supply Acquisition.

So, rather than receiving the full $50 per job to pay back our Demand-side CAC, we’re netting $45 per job. This pushes back our Break-even Point by a corresponding amount. It takes us 111 days to hit breakeven on our $5,000 Demand-side CAC, once we’ve taken account of our Supply side costs [$5,000 / $45 = 111].

Given that we’re viewing the Supply side acquisition costs as variable costs, we can also say that our Contribution Margin of our staffing business is $45 per job and our Contribution Margin Ratio is 1.11 [$50 / ($50 – $5)].

Marketplace Dynamics

So, good news: it looks as if our business has positive unit economics! We break-even on a per customer basis after 111 days on average, even taking into account the cost of finding the needed Supply (Wonoloers).

However, because we’re looking only at one customer in isolation, this entirely misses the fact that Wonolo is a marketplace. These marketplace dynamics actually make the unit economics significantly better. This is one way in which marketplaces can be extremely powerful.

Remember we said that, on average, a Wonoloer works for a given Customer 10 times? That’s true but Wonoloers work at more than one Customer! It turns out that Wonoloers work on average for 10 different Customers during their lifetime on Wonolo.

Therefore, we share our Supply-side acquisition costs across multiple Customers.

This means we can divide the $5 per job that we spend by 10, meaning we only really spend 50 cents on average acquiring the needed Supply for one job [$5 / 10].

This means our Contribution Margin is actually $49.50 per job.

These marketplace dynamics exist in most marketplaces – arguably, that’s the point of the marketplace. Uber drivers don’t just drive one person, eBay sellers don’t just sell to one buyer, Airbnb hosts don’t just have one guest. The ability to “sell” the same Supply to multiple Demand is what makes a marketplace powerful.

Ignored Components

As I said at the outset, I’ve ignored some components of Wonolo’s unit economics that would be included in any full accounting. These would include items such as:

customer account management,
customer and Wonoloer support, and
payment network and banking fees to collect payments from customers and pay Wonoloers.

Negative Unit Economics and The Big Gamble

Everything I’ve described above discusses Unit Economics in a sober and rational way; which is often not the way Silicon Valley works.

There are several high-profile cases where companies have continued to grow aggressively despite clearly having negative unit economics. The poster-child arguably being Uber. At the time of writing (July 2020), despite its huge scale (meaning you’d expect its contribution margins would start to cover its fixed costs), Uber has never made a profit. Will it ever? We’ll see.

To continue to grow despite having negative unit economics, businesses have to continue to raise bigger and bigger gobs of money. Remember: negative unit economics means the more money you spend, the more money you lose. Uber raised a total of $24.5B, including over $9B in their Series G alone.

Investors in Uber are continuing to make a big gamble that there will only be one or two winners, and the winners will be those that capture as much of the market as possible, even if it is done at a massive loss. The thesis is that, once they’ve “won”, they’ll be able to keep out competition and control pricing to the extent that they can become profitable in the long-term.*

Investors are also gambling that Uber will be able to continue to raise money to cover the growing losses. Fortunately for them, their market timing was good and they were able to continue to raise bigger and bigger rounds at higher and higher valuations before IPO. If they were trying to raise that Series G today, it would likely be a very different story.

*This thesis is itself based on a perhaps flawed understanding of the strength of Uber’s network effects, but that’s a topic for another day…

Everything You Always Wanted to Know about Unit Economics but were Afraid to Ask – Part 1

Unit economics are critical to successful business growth. Without positive unit economics, spending more money means you simply lose more money.

However, when it comes to two-sided marketplaces, It’s Complicated™. I’ve seen experienced analysts admit defeat when trying to get to grips with our unit economics at Wonolo.

Here I aim to walk you through the topic step-by-step, in two parts. No prior knowledge is assumed.

Part 1: Unit Economics Basics

I want to provide a gentle learning curve. Before we talk about two-sided marketplaces in Part 2, let’s first quickly cover some basics. If (like me) you don’t have a formal background in finance, you may struggle with some of the terms used.

Disclaimer: because I’m assuming no prior knowledge, some complexities are overlooked for simplicity.

If you already know all this, feel free to skip straight to Part 2: Unit Economics in Two-Sided Marketplaces.

What’s a “unit”?

Ok, we’re talking about “unit economics”; so, what’s a unit?

For me, the confusion starts right here. As the name implies, a “unit” might originally have referred to a widget made in a factory. For example, if we run a factory that makes paperclips, unit economics relates to understanding the economics of paperclip production and sales, down to the level of an individual paperclip.

However, in the context of startups, we typically have one product and we’re selling access to that product. The “unit” we’re interested in modeling is the act of selling access to a user or customer.

Therefore, a “unit” is typically equivalent to a customer or user. As we run through these basics, you can think of our “unit” as a customer.

Variable vs Fixed Costs

The first core concept to understand is the distinction between “Variable” and “Fixed” costs.

Simply put:

“Fixed Costs” remain the same, however much you sell.
“Variable Costs” vary depending on how much you sell. If you sell nothing, Variable Costs are zero.

When looking at Unit Economics, we’ll mostly be interested in Variable Costs.

Introducing our Lemonade Stand

I’m going to use a Lemonade Stand as an analogy. It’s a simple but sometimes flawed analogy. (If nothing else, it presents an opportunity for some heart-warming pictures.)

For our lemonade stand:

- “Fixed Costs” would be the costs of renting and operating our lemonade stand, and the pay for the person running the stand. They stay the same whether you sell 100 cups of lemonade or 0 cups. (Ok, I wouldn’t normally pay my kids to run a lemonade stand but this is just an analogy.)
- “Variable Costs” would be the costs of the lemons, sugar, and water to make the lemonade. They vary with how much lemonade you sell.

Customer Acquisition Cost (“CAC”)

The first of many TLAs (Three Letter Acronyms) is “CAC” or Customer Acquisition Cost. This is what it costs you to acquire one customer.

The most obvious and common costs included in CAC are marketing costs.

There are various accounting practices and standards that determine what else is or isn’t included in CAC. For example, in some cases, CAC may include all or some part of the compensation paid to people directly involved in selling to customers.

[Don’t confuse CAC (Customer Acquisition Cost) with COGS (Cost of Goods Sold) – they are related and may essentially be the same in a simple software business but CAC is the important consideration in our unit economics analysis.]

Our Lemonade Stand

Let’s say we print fliers to advertise our lemonade stand.

These cost $1 each to print and each one brings 10 customers to our stand.

Our Customer Acquisition Cost (CAC) is $0.10 [$1 / 10].

LTV

Our next TLA is “LTV” or Life-Time Value. This refers to how much money you’ll make from each customer, on average, during their entire time being your customer.

LTV is often difficult for startups – especially in the early stages – because you don’t have enough data. For example, perhaps customers will stay on average 2 years or 10 years but, if the startup is only 18 months old, you don’t know yet.

However, as the company ages, you’ll start to see the average customer life-time level off (or “asymptote”) to a certain value.

A complication here is that LTV can either be discussed as “Gross LTV” which is simply the money you get paid by the customer, or “Net LTV” where the variable costs have been subtracted.

Net LTV will typically be much lower than Gross LTV. If you want to look good, use Gross LTV. If you want to really understand your Unit Economics, use Net LTV.

Our Lemonade Stand

We’re going to charge 25 cents for a cup of lemonade. Our stand is only open for 1 month during the summer (and only this year).

On average, each customer buys 4 cups of lemonade. Therefore our customer LTV is $1 [4 x $0.25].

LTV:CAC Ratio

By dividing the average Lifetime Value (LTV) of a customer by the average cost to acquire a customer (CAC), we get the LTV:CAC ratio.

This is a simple measure of the efficiency of our business model – it measures how much more money you get back from each customer versus the cost to get the customer.

Our Lemonade Stand

So, our LTV is $1.00, and our CAC is $0.10, so our LTV:CAC ratio is 10:1. Pretty good.

MRR

Our third TLA is “MRR” or Monthly Recurring Revenue. This is the average amount of money you get each month, from each customer.

MRR is very commonly used for SaaS companies since they tend to have fixed and predictable income from each customer, based on monthly billing of a fixed amount. e.g. $9.99/month, paid every month until you cancel the service.

As we’ll see later, marketplaces can have very different dynamics and MRR can be a much less useful measure. I’m including it here because it is very commonly used in the context of unit economics and provides a simple way to explain some of the following concepts.

Break-even Point

As discussed above, the LTV:CAC ratio provides a simple way to understand the efficiency of a business model by comparing the money you make from each customer against how much money it costs to get them.

However, LTV:CAC ignores the time dimension: in many cases, you bear your CAC upfront but you only receive payment from the customer over time.

The Break-even Point refers to how long it takes a customer to “pay back” what it cost you to acquire them (the CAC).

This chart shows a simple example of a company with a $500 upfront CAC and a fixed $50/month MRR. (i.e. a customer costs you $500 to acquire and pays you $50 per month for your services.)

As you can see, it takes 10 months to break-even in this simple case [$500 / $50].

Break-even Point is important because it determines how fast you can grow your business. While you’re waiting for a customer to “pay back” their CAC, you can’t use that same money to do anything else, like acquire more customers. (In finance terms, it impacts your “working capital”.)

So, the shorter your Break-even, the better.

CAC-doubling Time

Once you break-even on a customer, you’ve got your money back, and you can rinse-and-repeat. However, while you’re waiting to get your money back, your speed of growth is limited – you can’t grow any faster without more working capital.

One way to get more working capital is to raise more money from investors. With this money, you can spend it on acquiring more customers while you’re still waiting for your earlier customers to pay back their CAC.

The downside of this from a founder’s point of view is that you have to sell part of your company to raise more money, of course. The downside from an investor’s perspective is that they will have to keep putting more money in to increase the rate that you grow.

Therefore, it’s good to understand the CAC-doubling Point. This is the point in time where you’ve not only recouped the original CAC, but also earned enough to acquire another customer – i.e. 2x the CAC. Once you’ve got 2x your CAC back, you can continue to accelerate your growth without having to raise any more money from investors.

Simply put: your CAC-doubling point tells you how quickly one customer pays you enough to acquire another customer.

Churn

One of the biggest limitations on customer life-time value (LTV) is typically churn – i.e. losing customers.

To understand the impact of churn on your Unit Economics, you need to understand how frequently you lose customers (churn rate) and how much money they’ve spent with you on average before you lose them.

If they churn before paying back what it cost to acquire them (CAC), then you’re going to lose money – your unit economics are negative and the more you spend to acquire customers, the more money you lose.

Marginal Operating Costs

Often there are ongoing costs associated with servicing a customer after you’ve paid your CAC to acquire them. Typical examples would be support and service/maintenance costs. Such costs are referred to as “Marginal Operating Costs”.

This is an area where finance conventions can differ but it’s easiest to think of Marginal Operating Costs as Variable Costs that occur over time.

From the perspective of Unit Economics, it’s important to understand the impact of Marginal Operating Costs on your Break-even Point.

The below chart shows the same $500 upfront CAC and $50 MRR but, this time, we’re adding a $20 marginal operating cost each month.

As you can see this pushes back the Break-even Point from 10 months to about 17 months. [$500 / ($50 – $20)]

Contribution Margin

Our final basic Unit Economics term is “Contribution Margin”. This refers to how much of the company’s fixed costs are covered by the revenue from customers, once the variable costs have been taken out.

Personally, I find the term confusing in the context of startups.

“Contribution Margin” is the contribution (hence the name) of a single product towards the company’s overall margin, whereas terms like “Gross Margin” refer to the company as a whole. i.e. if you sell only one product (like most startups), contribution margin and gross margin are essentially equivalent.

Contribution Margin is most often discussed as a ratio – “Contribution Margin Ratio”. It is simply the revenue divided by the revenue minus variable costs. Again, if you have one product only, “Contribution Margin Ratio” is equivalent to the % margin for the business as a whole.

Going Beyond Unit Economics

The ultimate objective is that, over time, your Contribution Margin (or margins, if you have more than one product) will be enough to cover all of your Fixed Costs.

At this point, you can think of your business as “profitable” as a whole, in colloquial terms – enough money is coming in to cover all of your costs (variable and fixed). In accounting terms, we’d also need to consider any costs related to taxes, interest on loans, etc before we can call it truly “profitable”.

Scooter unit economics: a Cautionary Tale

Now let’s take a look at a cautionary example in recent history.

In 2018, rental scooters suddenly appeared on our streets. Bird, for example, raised a $100M Series B and a $300M Series C – both in 2018.

So, scooter rental must be a great business, with great unit economics, right?

Let’s look at the unit economics of scooters:

Acquiring customers was not hard – people were literally tripping over them on the sidewalk (and complaining about it). So, for the purposes of this analysis, let’s say rider CAC was essentially $0.
It cost about $500 to buy one of those first-generation scooters.
Each scooter made about $500 per month in revenue.
The Marginal Operating Costs – charging, relocating scooters, etc – were about $200 per month.

So a Contribution of Margin of about $300 per month, per scooter. You can pay back that $500 purchase cost in less than 2 months. Pretty good business?

You may remember what happened: people didn’t treat the scooters kindly. The average scooter lasted about 28 days, so it wasn’t possible to recover the $500 upfront purchase cost before the scooter was destroyed, and the unit economics didn’t work.

The other thing that happened was injuries and subsequent lawsuits. A big proportion of scooter injuries were nasty head injuries, meaning big payouts for the scooter companies.

The huge amount of investment the scooter companies garnered was enough to maintain these losses for a while but ultimately a few things happened:

Scooter companies tried to source more resilient scooters, with higher longevity to get to breakeven on each scooter (compare how solid current [2020] scooters are with the first-generation 2017 Bird scooter).
Uber/Lyft acquired scooter companies – although the unit economics of scooters were poor, they provided a great way for ride sharing companies to acquire riders, and more cheaply. By becoming scooter chargers, it also allowed Uber and Lyft to offer additional income generation for their drivers.
Some scooter companies pivoted to electric bikes – much lower injury rate, much higher longevity, and much better unit economics.

The lesson of this story is that initial, quick takes on businesses can be misleading – you need to truly understand the unit economics.

Summary so Far

To recap, the core concepts we covered are:

Fixed vs Variable Costs
Customer Acquisition Cost (CAC)
Life Time Value (LTV)
LTV:CAC ratio
Break-even Point and CAC-doubling Time
Churn
Marginal Operating Costs
Contribution Margin

Next, on to Part II: Unit Economics in Two-Sided Marketplaces…

Product Operations: do you need it, or is it just a fad?

Recently, I’ve been hearing about more and more companies building “Product Operations” functions. This appears to be especially true of startups with high operational-complexity; particularly marketplaces.

So, what is Product Operations? Do you need it? Or, is it just another fad?

I spoke with several founders/leaders at a number of high-growth and operationally complex startups who have invested in Product Operations functions, including Uber, Thumbtack, and Zipline.

So, what is it?

The definition of “Product Operations” varies somewhat between companies but here are the main themes I hear:

can be part of the Product organization, but distinct from Product Managers
can also report into the Operations organization
works very closely with operational teams
very detail-oriented and data-driven, especially as it relates to process optimization
provides the bridge between the operations teams and Product Managers, solving problems and being supportive, and ensuring communication is effective

The simplistic distinction between Product Management and Product Operations seems to be as follows:

Product Management – “what should we build?”
Product Operations – “is what we’ve built working?”

So, is it a fad?

This is where my own cynicism started and where I got the first whiff of a possible fad.

To me, being very close to customers, even if internal customers, and having a great finger on the pulse of whether what you’ve built is working is a major aspect of any Product Manager’s job. If a Product Manager is not doing that, they’re not a good Product Manager, right?

Pragmatism Rules

Everyone I spoke to agrees with me…in theory.

However, the reality is that it’s very hard to find Product Managers who have a natural affinity for what’s involved in operating a complex business “at the coal face”. This makes sense because most Product Managers come up through the ranks of product and engineering organizations.

Although a Product Manager’s job is to understand and empathize with users, it seems it can be more pragmatic to hire people with an operational background who just more naturally “get it” and put them in a Product Operations role as the go between operation users and the product team.

Put simply, one person I spoke to said that Product Operations “makes sure operations are getting respected” by the Product organization.

What to look for

So, who makes a great Product Operations person? Here’s a summary of what I’ve heard:

have an operational background (vs a product/engineering background)
adaptive
empathetic
data-driven
strong personalities willing to fight for what they think operations needs

Side effects

Are there any undesirable side-effects of introducing a Product Operations function? The consensus seems to be that two problems can occur:

there is some duplication of effort/ownership between the Product Management and Product Operations and therefore some potential ambiguity and politics that needs to be carefully managed. To mitigate this, the distinction between the two functions summarized above must be made very clear to both sides.
the introduction of Product Operations risks Product Managers retreating further to their ivory towers, allowing them to get more divorced from the (internal) customers they serve.

Marketplaces: Scaling with Operations vs Engineering?

There are many things that make building and scaling marketplace businesses hard: for example, there’s the quintessential chicken-and-egg problem of building and balancing supply and demand, and there’s the need to build two or more products in parallel to serve the needs of the different participants in your marketplace.

There is also the question of how you scale your marketplace once you’ve got product-market fit established and some unit economics that seem to work.

Electrons vs Atoms

Most marketplaces have to deal with the tangible, real world: unlike pure software/SaaS companies, marketplaces have to deal with whole atoms, rather than just electrons.

Those atoms might make up people, or cars, or meals, or apartments but they are physical resources that have to be managed. This is why marketplaces tend to need significant operational headcount.

However, most marketplace companies aspire to be, and actively position themselves as, technology platform companies. This of course requires an ongoing investment in product/engineering.

Given finite resources, how do you choose between scaling a market place through operation headcount versus product/engineering investment? How do you strike the right balance?

The Comparables

I did some quick research to look at what other marketplace businesses are doing.

I took a basket of marketplace companies at varying funding stages and looked at their employee counts on LinkedIn by role.

Firstly, let’s set the scene by looking at the absolute number of engineers that various marketplaces have and compare that to their funding stage:

Perhaps no surprises here: as marketplaces develop, they hire more engineers. I am struck, though, by the widely varying number of engineers that the earlier stages marketplaces seem to have.

Now let’s look at the ratio between operational headcount and engineering headcount in these same companies:

This is also what you might expect. Although the data is noisy*, it seems that as marketplaces grow, they become less dependent on operational headcount. Presumably, their investments in product/engineering payoff in terms of automations and efficiencies.

Of course there’s also survivorship bias here – these are only the marketplaces that are still around. Perhaps the ones that didn’t make it had wildly different ratios.

What would be great is to get historical data on these ratios and see how that correlates with outcomes. Unfortunately, I don’t have that data (if you do, let me know!).

My bet would be that a higher ratio of operational to engineering headcount is hard for marketplaces to wean themselves off – i.e. it’s hard to change the ratio over time. If you organization gets accustomed to scaling and solving problems by hiring ops people rather than hiring engineers to automate, that just gets amplified over time.

* my methodology here was simply to search on LinkedIn for people with “engineer” and people with “operations” in their job title. This is obviously error prone for a number of reasons. For example, some “engineering” roles have “operations” in their job titles, and not all headcount are necessarily on LinkedIn, especially if a company outsources or off-shores some functions. However, given a sufficiently large sample set, one would hope that these effects blend out.

Brute Force Growth vs Long-term Value

Like any business, marketplaces have to continue to show top-line revenue growth in order to maintain the faith of investors and employees and be able to continue to raise money. The first, second, and third rules of business are “don’t run out of money“.

However, while it’s possible to “brute force” growth of many marketplaces through reliance on operational headcount in the short to medium-term, I believe this strategy has large associated dangers in the longer term.

In a perfect world, you could scale both operational and product/engineering headcount as needed but, in reality, you will be forced to choose between spending each $1 on one or the other. Here’s my quick take on the pros and cons:

In summary, the biggest danger with scaling by adding operational headcount is that it works…in the short-term. It’s also cheaper. But, the danger is that you win the battle but not the war.

Agree or disagree, please leave a comment.

Are you confusing Optimization with Growth?

In a startup, there are always many things that aren’t working as efficiently as they could be – acquisition funnel conversion, manual processes, customer acquisition costs, etc. This may be incredibly frustrating, especially for the team members who have to deal with it on a day-to-day basis.

It’s very tempting to direct precious money, time, and energy to resolving these frustrations, especially as its your team’s tired faces that you have to look at every day. It’s always tempting to give the squeaky wheel some oil.

However, it’s vital that you remain focused on growth and don’t confuse growth with optimization. Burning lots of time optimizing at the expense of growing is not a recipe for success for early- to mid-stage, venture-backed startups.

Of course, there is some nuance here: if things are so broken that your team starts to leave, you have to address that – no team; no company.

Also, the smart investors (i.e. the ones you want) realize that, if your unit economics fundamentally don’t work, you will simply lose more money as you grow.

However, conversely, it’s unlikely that a Tier 1 investor will invest in the also-ran, #3 player in any category in terms of growth rate and/or absolute revenue, however optimized and healthy the acquisition funnels, gross margins, etc. Investors are in the business of selecting for the biggest return on their capital, not the best run or most efficient business. The biggest return comes from the biggest exit and the biggest exit goes to the category winners.

As a venture-backed startup, the most important thing is to stay as one of the leaders in your category – this is what allows you to maintain team confidence and morale, attract the best talent and investors, and continue to raise money when you need it. Note: there are usually only 1 or 2 “leaders” in any category.

Let’s take two startups: to start with, Company A and Company B are neck-and-neck. Both have a $5M in gross revenue, with a average revenue of $5,000 per customer per year and a customer acquisition cost (CAC) of $2,000. Both have revenue that is doubling each year. Both are mid-stage startups – they’re not yet profitable and don’t expect to be any time soon.

Both companies also know that their CAC is too high and, by some optimizations, the CAC can be reduced significantly. The high CAC drives some members of the team crazy – so many opportunities lost, so many wasted marketing dollars.

So, the CEO of Company A directs the team to work on CAC. Over 6 months, they manage to effect a series of changes process and product changes in their customer-acquisition funnel, through A/B testing, cost reduction, etc. These compound and end up halving the CAC to $1,000 – that’s a huge improvement. Company A’s gross margin has significantly improved.

Meanwhile, the CEO of Company B ignores the CAC for now and instead directs the team to focus on increasing the size of the sales and marketing teams significantly and filling the top of the sales funnel with as many leads as possible.

One year later, Company A’s revenue has doubled again and they’re netting an average of $4,000 per customer per year – 33% more. Not bad.

However, by focusing on growth, one year later, Company B’s revenue has tripled rather than just doubling. They still net an average of $3,000 per customer per year but there are 3 times more customers.

Both Company A and Company B need to raise more money. So does a 3rd player in the category; Company C. Company C is going gang-busters, beating both Company A and Company B on growth rate and total revenue.

You know how this story ends: Company B and Company C are able to raise giant C-rounds from Tier 1 investors at great valuations. Meanwhile, Company A has fallen behind – its unit economics are better than Company B’s but it’s now an also-ran and struggles to raise money. Without that money, it cannot continue to grow and falls further and further behind Company B and Company C. Perhaps it’s acquired by Company C at a fire-sale valuation or perhaps it’s a giant smoking crater.

Of course, this is a contrived story. In reality, you can probably achieve growth and some optimization in parallel. But, the key is not to confuse one with the other.

So, grow and optimize as you go, as long as that optimization doesn’t slow your growth. Don’t optimize hoping that it will deliver meaningful growth.

tl;dr – in a startup, you can’t optimize your way to success – you must out-grow your competitors.

How Startups can work with Big Companies and not get Killed

(Note: this post was originally published on the Wonolo blog under the title “Collaborate. Innovate. Top Tips for How Large Enterprises and Startups Can Have a Winning Partnership”)

Recently, I was invited by Unum, one of our FORTUNE 500 customers, to participate in a panel session about corporate innovation at Maine Startup and Create Week. At the heart of our discussion was how large companies like Unum can be more innovative and how startups and large companies can work together toward this goal.

It’s a topic that’s close to my heart: I spent the first part of my career in the wireless industry, and back in 1998, I was a part of the founding team of Symbian, one of the first operating system platforms for smartphones (although they weren’t yet called “smartphones” at that point).

Symbian was a joint-venture between Nokia, Ericsson, Motorola, Psion, Panasonic and, later, several others. Their rationale for investing in Symbian was a desire to have a common software platform for smartphones. However, what these companies actually had in common was that they were large, bureaucratic, and they were arch-competitors.

Getting these large companies in the Symbian joint-venture to work together was somewhere between very hard and impossible. (For the full story, see David Wood’s excellent book.) The term of art at the time was term “coopetition,” and it didn’t work. None of the participants in Symbian really had any desire to share their product plans with their competitors.

So, the irony was that, while Symbian was arguably at the spearhead of technology innovation, it was frequently stymied from actually being innovative by the inertia and culture of its participants. This left the market open to more focused, agile and independent companies like Google and Apple to dominate the smartphone market of today. In contrast, Nokia had an ignominious end – broken up and sold off, with billions of dollars in market value destroyed.

So, fast-forward to 2016 and my panel discussion at Maine Startup and Create Week…How can big companies be more innovative, and how can startups and large companies work together to the benefit of both?

Designer, Builder or Maintainer?

First, let’s take a look at the kinds of people that tend to work at startups versus larger companies: I have a simplistic but hopefully powerful model that divides people into three groups – “designers,” “builders” and “maintainers.”

Let’s use an analogy: here in San Francisco, arguably our best-known symbol is the Golden Gate Bridge – just look at any tourist tchotchke.

If we think about the Golden Gate Bridge, first there were the designers. In our culture, the designers generally have the “sexy” job – they are the visionaries.

Next come the “builders” who actually constructed the Golden Gate Bridge.

Last come the “maintainers.” These are the workers who hang on ropes off the bridge, scraping off rust and continuously repainting it in International Orange.

This is the least sexy job in most people’s eyes: which would you rather be – the visionary designer of the Golden Gate Bridge or someone who hangs off it on a rope, scraping rust?

Now, in a startup, what you need for success are just a few designers – these are typically the founders. You can’t have too many because they tend to butt heads.

What you really need for a startup is a boat-load of builders: these are the doers – people that create and Get Shit Done (GSD). Builders are the backbone of any startup.

What you don’t need in a startup is maintainers: everything in a startup is being created anew so there isn’t anything to maintain. You’re also focused on growth rather than optimization.

Contrast that to a big, established company: there, most people are maintainers. Their job is to ensure that an already successful business continues to be more successful. They are there to grease the wheels and optimize.

So What?

What this means is that there is a cultural mismatch between a large company and a startup.

At the core of the startup mindset is a willingness to fail and an acceptance of it. In a startup, failure is the norm – as the cliché goes, you fail your way to success. Since “failure” has negative connotations, I think it better to simply reframe it as “learning.”

Another important aspect of building a startup is understanding the art of the “good enough.” Because you’re bandwidth-constrained, you are forced to be very selective and very efficient in how you do things. You have to get them done quickly. You have to not let the great be the enemy of the good. You have to focus on delivering 80% of perfection for 20% of the effort.

Naively, when large companies aspire to become more innovative, they trot out clichés like “we reward risk-takers.” This is a lie. The last thing you want when you have a large company generating billions in revenue it to have some cowboy risk-taker come in and break it. What you want are maintainers to keep it working and keep it generating billions of dollars.

To take it back to the Golden Gate Bridge example, would you want a maintenance worker who said, “Let’s see what happens if we take all the bolts out”?

I think it would be better to rephrase it as, “We reward people who make small, smart bets.” Making a series of small, smart bets to test various hypotheses is the basis of iteration, and iteration is the how you build great products and great companies.

Recognizing these problems, many large companies have started to take a different approach – they have created specific initiatives intended to foster and drive innovation. Wonolo itself was created through The Coca-Cola Company’s innovation program.

Creating a Great Corporate Innovation Program

So, how can a large company create an innovation group and/or program likely to succeed? These initiatives can take various forms, but I think these are the most important elements:

Set money aside – the budget for innovation can’t come out of the normal, operating budget for any existing business unit. If it does, it competes with the budget needed for maintenance of what’s already working.
The innovation group must report directly into the CEO – this demonstrates genuine commitment to innovation and also helps unblock bureaucracy.
Be clear with objectives – what specifically are you hoping that the innovation program does for your business? What are you looking to achieve? How does it positively impact your core business?
Build the right team – a good mix is designers and builders from outside of the organization, along with some inside players who can help navigate the existing organization, as long as they carry enough weight. You will also need to reassure your best maintainers that they should stick to what they do well rather than trying to join the innovation program because it’s sexy.
Accept failure – as discussed above, you must accept that failure is a vital part of the process. Not all initiatives you start or companies you fund will be successful, but you will learn something important from each.

How Can a Startup Engage with a Big Company and Win?

Big companies can kill startups. I’ve seen it happen.

Big companies can lead startups on and consume lots of their time and bandwidth with no pay-day. At the end of the process, the large company has perhaps lost a few hundred thousand dollars. Meanwhile, the startup has run out of funding and is dead.

Here’s what I’ve learned (the hard way) to avoid that outcome:

Find your champion. Ted Reed is our champion at Unum. Not only is he an all-round great guy, but he also understands the need to be completely transparent with us. A great champion is your guide to the large company – its structure, how it makes decisions and the key players you’ll need to win over.
Seek trust, honesty and transparency – any great relationship is built on mutual trust. Get feedback early and often (from your champion) on your likelihood to succeed.
Don’t over-invest until you have clear commitment – be prepared to scale back or end the relationship if it’s not clear you are on a path to success. The opportunity cost of your time in a startup is huge. Don’t do anything for free – free means there’s no value, and it won’t be taken seriously.
Ensure clarity in objectives, value and define success – if both sides are not clear on the business value that your product or service is providing to the large customer, be very cautious. Make sure both sides agree on what success means.
Start with a small, well-defined trial – rather than trying to boil the ocean, it’s wise to start with a trial that demonstrates the value your product or service provides to the large company. This has less risk, requires less investment and has a higher likelihood of success. For more tips on how to best go about setting up a pilot, check out our related blog post.

How Can a Big Company Engage with a Startup and Win?

On the other side, how can big companies successfully engage with startups and win? Here’s my personal recipe:

Be honest and transparent – don’t lead startups on. Be honest about chances of success and what it will take.
Be respectful of bandwidth and provide funding, if possible – realize that a startup’s most precious commodities are bandwidth and funding. Do everything you can to reduce the sales cycle. Structure the deal to provide the funding and/or revenue necessary for the startup to succeed.
Have realistic expectations in terms of maturity of a startup and its processes -don’t try to apply your pre-existing vendor onboarding process when engaging with a startup. For example, a 10-person startup won’t pass your 50-page IT security audit.
Respect the need for independence – you may be providing a startup with revenue, funding and a great customer reference. However, a startup needs to be in control of its own destiny and own its own product roadmap. Don’t treat a startup like a consulting company or development shop, unless that’s how the startup sees themselves.

Overall, the relationship between a large company and a startup can be a marriage made in heaven. I would marry Ted Reed if I could.

How to Hire Software People

At the time of writing (Dec 2013), hiring software people in Silicon Valley is as hard as its been since the dotcom boom of the late 90s (remember getting a Porsche as a signing bonus?). While the rest of the US and the world is still staggering through an uneasy recovery, we live in this weird bubble of 100% employment and inflated salaries.

I could debate whether this situation is sustainable or healthy (hint: no and no), but it is the current reality and, if you’re trying to build a software company today, you have to deal with this reality.

Your ability to find and retain software talent is currently one of the biggest, if not the biggest, barrier to the success of your business.

Know Who You are Looking For

Write a Job Spec

This sounds obvious but this step is often skipped, especially within smaller companies. Perhaps its perceived as too “big company” for a startup but this is a classic false economy.

It’s one of those activities where the process is more important than the output: by forcing yourself to write a job description, you force yourself to think about who you are looking for: you’ll create a lens through which to view candidates and you’ll give yourself clarity on where you are willing and where you’re not willing to compromise. You’ll also have a very useful start for recruiters (more on them later).

Make sure your job spec not only covers the skills and experiences you’re looking for but also the attitude that you are looking for.

Cultural Fit

“…culture isn’t just one aspect of the game, it is the game…” – Lou Gerstner

Everyone says that “cultural fit is important”, but what does that really mean?

Firstly, let’s define what “company culture” really is. There are many definitions out there (Google it) but the two that I find most useful are:

a. company culture is “the way things get done around here”, and

b. company culture defines who gets respected and rewarded in your company.

Your first task is to take a long, hard look at your organization and ask yourself what your company culture truly is. If you need help, there are various online tools and surveys to help you tease this apart.

Bottom-line: if you run a boiler-room full of fist-bumping, Izod-clad Brogrammers, own that reality.

If your company culture is not what you’d like, what do you want it to be? Armed with this insight, you can select people that fit that culture and have a chance of nudging it in the direction you want it to go.

But, don’t fool yourself that your company culture is what you wish it was, rather than what it is. Hire someone that is a misfit for your culture and they’ll pretty quickly suffer “organ rejection” and at best you’ll be back to where you started, having lost some credibility. It takes a very strong personality to come in from the outside and change the culture in a significant way so be realistic about the degree of change any individual hire can make.

3 Kinds of People

There are many tools and models to categorize people. One model I use to help understand the kinds of people you need in a startup is to divide people up into just 3 groups:

people who are good at designing the machine
people who are good at building the machine
people who are good at operating the machine

By “the machine”, I mean your business and your product, whatever that may be.

Very rarely you’ll find someone who is good at 2 out of 3. I have never met anyone that is truly good at all 3.

With a startup, you need a few designers and lots of builders. Only later, when you’ve probably pivoted multiple times and worked out the business you are actually in, do you need operators. At that point, you’re probably not a startup any more.

The Danger of Big Company People

The history of Silicon Valley is littered with smart and accomplished people with established careers in big companies who have tried and failed in the startup business. The question of why this is true is probably a whole post in itself but here are my quick views:

specialists versus generalists – as organizations grow, people’s responsibility tends to become narrower and deeper; more specialized. In a startup, you need to wear many hats and will likely pivot multiple times. Therefore, generalists tend to fare much better.
discomfort with ambiguity – running a startup is all about making an endless series of low-data, high-risk decisions. Big companies have established markets and products, lots of data and a more predictable future.
inability to work with extremely limited resources – big company people are used to established infrastructure and an organization that has the scale to do multiple things in parallel. With a startup, attempting to do several things at once is often fatal and acute focus is required at all times.
managing not doing – in a larger company, people typically spend the majority of their time just managing the organization. In a startup, you need to be able to both do the work and hire and manage self-starter people who can help.
the romance versus the reality – last but not least, many big company people fall in love with the idea of a startup but have little understanding of the reality.

I advise extreme caution if considering hiring people who have no prior experience working at a startup, however accomplished they may be in their big company career.

Big company people are generally operators. You have to take into account the stage of your business – the “right kind of people” changes over time.

Finding Candidates

Find a Good Recruiter

In today’s hiring market in Silicon Valley, the hard reality is that the people you want to hire aren’t looking for jobs. This means that to hire the people you want you have to:

a. spend an inordinate amount of time networking, going to meetups, searching on LinkedIn, etc, or

b. get very lucky, or

c. hire a recruiter.

As I said in my post on Focus, I think that spending your time on the early stages of recruitment is not a good use of your time as a founder/exec of a company. There are people that do that for a living and there are better things for you to be spending your time on in terms of building value in your business.

So, although they are much reviled, my strong advice is to find yourself a good recruiter. For most non-executive level positions, you should only need a commission-only recruiter (you pay them a % of salary when they successfully hire a candidate, nothing upfront).

Whenever we’re in a boom/bubble, a huge crop of recruiters springs up and, like me, you are probably inundated with their unsolicited inbound messages. Here are a few things to watch out for in dealing with recruiters:

look for recruiters that have been through multiple boom/bust cycles. We’re in a boom/bubble now so it’s easy to be a recruiter. Only the good ones have the staying power through the tough times.
adding multiple recruiters (for a role) often doesn’t help much, due to the very limited supply of candidates. It actually makes more busy work for you since you’ll have to track which recruiter has introduced which candidate first; otherwise you’ll end up having to deal with an irate recruiter who wants his commission for introducing a candidate to you months ago who you just hired via another recruiter by accident.
beware of recruiters that try to hook you in with candidates that they don’t actually represent and have never actually spoken to. Some recruiters are not averse to copying resumes from LinkedIn and then presenting them to you as candidates they actually represent in order to get your business.
beware of recruiters who overly “coach” candidates ahead of interviews, based on feedback from prior candidates they’ve sent you. If a candidate’s answers to your questions sound too good to be true, they probably are.
fees are always negotiable, as are guarantee periods. The standard initial offer from a recruiter currently is usually 25% of first-year salary with a 3 month guarantee. Negotiating down to 20% and 6 months should be possible in most case, bubble or no bubble.

On your side, you should also make sure you have realistic expectations of any recruiter and understand what you will need to do to be successful:

provide the recruiter with a written job spec (see above)
recruiters are not software engineers. Although a good technical recruiter should be expected to have a basic understanding of different skill sets, they are not going to understand the nuances you do. They should be able to ask questions you provide them, though.
tell the recruiter which companies suitable candidates are likely to be currently working at or worked at recently.
be responsive and professional in the recruitment process.
a recruiter can bring a horse to water but it can’t make it drink – the onus is still on you to make joining your company the most attractive option of the many options that any good candidate will have.

Compromise

Given the ridiculously competitive hiring market and the level of entitlement (see below), you are highly unlikely to find the “perfect candidate” and will almost certainly have to compromise on at least one attribute.

The important thing is to be clear on what to compromise on and what not to.

Don’t compromise on cultural fit or talent – that’ll slowly erode the quality of your organization. Filling a position with someone who doesn’t cut the mustard or fit with your culture just to fill it is almost always a recipe for disaster – i.e. worse than hiring no one.

The first thing you might compromise on is specific experience. Unless you are hiring for a highly specialized skill-set or have a very time-critical need, I would always choose the more talented person over the person with the more specific skill-set or experience match. This is because the more talented person will almost always out-perform the less talented person in a matter of weeks.

To take a concrete and pertinent example for software engineering candidates, Ruby-on-Rails engineers are particularly hard to find and demand a premium in the current market. But, it’s not like a great (or “rockstar” as a recruiter would put it) engineer with no Ruby-on-Rails experience is suddenly going to become a terrible engineer just because they don’t know Rails. I will put money on the great engineer with web development experience but no Rails experience outperforming the less talented engineer with Rails experience within 4-6 weeks.

Another attribute you might compromise on is work location; whether its someone talented who lives a long way away or someone more local who just prefers to work from home. If you’re based in Silicon Valley, opening up your search to other locations massively increases the pool of available candidates.

Hiring someone who is not local and helping pay for their relocation is potentially a great deal versus having to pay a premium for someone already local.

Alternatively, while allowing remote working introduces its own challenges (such as instilling culture), it is a viable option, as long as you build your organization, culture and processes in a way that supports it.

Interviewing

Dealing with Entitlement

Don’t take it personally; the market has educated everyone to think that they’re great, even when they’re not.

I recently spoke to a candidate who received over 20 inbound calls from recruiters the day he quit his previous job. He wasn’t that great. In this kind of market, evenly distinctly mediocre people only see signs that they’re superstars.

Software people in the Valley are also currently ridiculously pampered as employers go to greater and greater lengths to attract people.

Bear in mind that, in the software business, the workforce leans young. If you’re 25, you haven’t even seen one economic cycle in your adult, working lifetime – as far as you’re concerned, it’s always been like this, and always will be.

Combine this with the Millenial generation’s trademark attitude (is that really true, or were we all just dicks when we were young?) and you have a heady mix of entitlement.

So, get used to candidates asking all the questions. Get used to entitlement. Accept that, currently at least, its an employees market and you are selling to every candidate. Take a deep breath.

One and Done

You are going to lose candidates if you don’t move quickly. The best candidates will be gone in less than a week. Promise.

With this in mind, I highly recommend a “one and done” approach to recruitment. Schedule candidates to come in for a block of 2 to 3 hours max, have everyone you want to interview scheduled back-to-back and be prepared to make an offer that same day.

Standardized Assessments

People are not easily categorized but, in order to make your interviewing process and more scientific, agree a standardized way for each interviewer to write up their responses.

Here is the format I have used for some time:

Score out of 5 in terms of suitability for the role (4.5s are extremely rare, I’ve never seen a 5)
HIRE / NO HIRE – force people off the fence by making each interviewer decide whether the candidate is a hire or not
Pros – max 5 bullet points
Cons – max 5 bullet points

To keep it truly objective, these assessments should be sent only to the person doing the hiring so that different interviewers do not influence each other.

What to Ask

Let’s be honest; an interview is a highly artificial situation. Working with someone day-to-day is nothing like an interview. What you’re trying to do is make an assessment in 30-60 minutes of whether someone is likely to perform well and integrate with the team.

That’s hard. It’s particularly hard because software people generally are highly introverted – hell, they got into this career to start with to avoid having to talk to people.

Personally, I rarely ask any specific “technical” questions. Partly, I have the luxury of a team of people who can do that better than I can but, more importantly, I believe that you can teach skills but you can’t teach attitude.

My experience is that hiring people who you can work with, trust, rely on and communicate “at the speed of thought” is more important than someone’s specific background and skills.

I also find resumes to be nearly useless since they tell me nothing about the person’s attitude and are generally so carefully word-smithed and full of generalities that they don’t really tell me much at all.

What I want to know most of all is:

is this person a relatively normal human being* who I can effectively communicate with?
is this person accountable? Will they do what they say? Can they be relied upon and trusted?
is this person likely to deliver results?
does this person learn from their mistakes?

(* this is the software industry – “normal” is a relative term.)

My main way of answering these questions is to ask people for their stories. I want to hear about projects they’ve worked on where they were pleased with the results and I want to understand why they think the project was successful and what their role was in the success.

More importantly, I want to hear about projects where they look back and kick themselves and wish it had turned out differently. I want to hear why they think it turned out badly and what the lesson is they distilled from that experience. I strongly believe that you learn a lot more from failure than from success.

All of these stories also present many opportunities to dig into the specifics to test the candidate on what they really know and what they really learned.

What Not to Ask

Firstly, familiarize yourself with what you are not legally allowed to ask candidates – you might be surprised.

Next, remember that an interview is already an artificial scenario bearing little resemblance to actually working with someone. So, avoid making it even more artificial with ridiculous riddles and brain-teasers. I think these kinds of questions are really about making the interviewer feel smart and smug than about eliciting any useful responses from the candidate. People claim that they are good at making people think “out of the box” and testing reasoning skills but how about asking them some questions that are relevant to your business that achieve the same thing?

Remember that an interview is not your opportunity to show off how smart you are (seriously, this is a widespread problem in the Valley). So, don’t ask highly specific questions about a particular subject unless it’s absolutely critical the person you hire knows about that on their first day at work. Those questions just tell you whether the candidate happens to have dealt with that particular subject, not how talented they are.

Lastly, avoid asking obvious questions that you’ll only ever get one answer to. “Are you trustworthy?” “Are you a hardworker?” “Are you a team player?” Would anyone ever say “no”?

People to Avoid

Lastly, here is my short list of People to Avoid ™:

Talented Assholes

People who rub other people the wrong way should be avoided, even if they are incredibly talented. The damage caused is not worth it and the aggregate results of the team will suffer as a result more than any individual talent will compromise.

Heroes Need Not Apply

A close cousin of the Talented Asshole is the Hero. The Hero will discover a big problem 2 weeks before a product is due to ship and work 18 hour days to save the day, fully confident of success and the imminent adulation of the team. The problem is that the hero will fail 90+% of the time. The Hero should have told everyone else and asked for help. The team beats any individual 99.9% of the time.

“I’m not technical”

Even if you’re hiring for a non-technical role, this is Silicon Valley and you need to have some base level of interest in technology and in the industry. There are people who seem to wear “I’m not technical” as a badge of honor. Particularly in a startup, you need people who are generalists and adaptable. I have little time for “I’m not technical”, just like I have little time for engineers who have no sense that we’re writing software to satisfy a customer need, not for the sake of it.

That’s it. Have fun out there. Oh, any one last thing; if a penguin walked through the door right now wearing a sombrero, what would he say and why is he here?

Focus – what it really means and why it’s important

When I started my first company in the late 1990s, many people advised me on the importance of focus.

“Focus”, I’d repeat and nod sagely, not really knowing what they meant.

“Focus” seemed like an inherently Good Thing™ on the face of it – like happiness and democracy – but a pretty vague concept.

Since then, personal experience has shown me the critical importance of understanding what focus means when building a company.

Focus is absolutely essential when building a startup but it’s also important for companies at any stage so I hope this is useful and relevant to all.

Focus – a definition

So, how do we define “focus” in the context of building a company? I would define it like this:

Focus is maximizing the time spent on the essential complexity of the problem your company is trying to solve and minimizing the time spent on incidental complexity.

Every company and every product is trying to create some value for its customers and users. Without that, the company and product by definition has no value. Typically, creating that value can be thought of in terms of solving some problem for your customer.

The more time you can spend on understanding the value you are creating for your customer – i.e. understanding the problem you are solving for them and solving it – the more you are increasing the value of your company.

Working out what the value is you are creating – the problem you are solving for your customer and how to solve it in the best possible way – is the essential complexity of your company.

In contrast, time spent on incidental complexity does not increase the value of your company and product. It simply takes away time and energy from working on the essential complexity.

In summary, you don’t build value by solving problems that others have already solved – you create value by solving problems not yet solved and, to a lesser extent, by solving problems in significantly better ways.

Incidental Complexities

So, what are incidental complexities?

Here are some examples of the most common ones I’ve seen (in no particular order). I bet many readers will have seen all or most of these:

infighting between colleagues
“busy work” that could be automated easily
reinventing the wheel
failure to make decisions / trying to keep too many options open
early stages of recruitment – finding candidates, reviewing resumes
meetings with ill-defined agendas
waiting because of colleagues’ poor time-management
attending conferences without pre-scheduled meetings and clear objectives
over-analysis of vendor/technology selection
doing your own IT support
obsessing over your competition
pitching investors
trying to make money from things that are not part of the problem you’re solving for your Customer
frequently switching between multiple tasks
rewriting things that are good enough (see my post on Why You Should (Almost) Never Rewrite here)

Let’s look at some of these examples in more detail and try to identify some general sources of incidental complexity.

Decisions

“We would rather suffer the visible costs of a few bad decisions than incur the many invisible costs that come from decisions made too slowly – or not at all – because of a stifling bureaucracy.” – Warren Buffett

“Commit to making decisions. Don’t wait for the perfect solution. Decide and move forward.” – 37signals

“A good decision is a made decision.” – Clayton Christensen

Many people have written about the art and importance of decision making. I will not attempt to cover this topic in depth.

My view is simply that you don’t learn anything from a decision you don’t make. Whereas, you learn from all decisions, especially bad ones. Fail fast, move on.

Startups, in particular, are all about making high-risk, low-data decisions. If you don’t have the stomach for those, you shouldn’t be running a startup. Deferring hard decisions just creates ambiguity and saps time, morale and energy.

Choice is Bad

When it comes to moving quickly and focusing as much time and energy as possible on the essential complexity of your business, choices are a Bad Thing™.

This seems counterintuitive to many people, especially in the West where our culture is built on the importance of individualism and choice, and denial of such freedom is tantamount to treason. However, choice in areas outside of your essential complexity just slows you down.

In software development, one of the reasons that Ruby-on-Rails is so popular, and one of the reasons why we currently use it, is that it is built on the principle of “Convention over Configuration“. This principle is about reducing the number of decisions a developer has to make – gaining simplicity without necessarily losing flexibility.

Think about every aspect of your business in these same terms. Everyone likes to think that their business is special and unique but the reality is that, outside of the essential complexity of your business, the other challenges you face are common to 90-100% of businesses of a similar size and stage of growth. They are solved problems and therefore textbook incidental complexity.

Analyzing choices that don’t really have impact on the value you’re building isn’t time well spent. In a typical Silicon Valley startup, these are decisions like which accounting system to use, which CRM system to use, which web development framework to use.

Of course, I’m not advocating making completely blind choices with no thought whatsoever. However, what I’ve seen is that people often err on the side of overanalysis. The return on this time diminishes very rapidly.

If it’s not core to your competitiveness or differentiation, why spend another hour or day or week analyzing to make a 5% better decision?

Outsource Everything You Can – Use Specialists and Don’t Reinvent the Wheel

Money spent taking away incidental complexity is money, time and energy saved to focus on essential complexity.

A classic case in point is recruitment – people hate recruiters. With a few notable exceptions, I hate them too. But, they are a necessary evil. At the time of writing, hiring in Silicon Valley is as hard as it ever has been in my memory.

To be clear; recruitment is one of the most important things that you can do as a founder, manager or executive – controlling the quality of the people that you let in is one of the best controls you have over the quality of your company and its output. Therefore, I would never advocate for outsourcing the process of choosing the best people.

But, that doesn’t mean you have to burn lots of hours of your own time on the front-end of the recruitment process – finding people, contacting them, reviewing resumes, screening candidates, etc is not a good use of your time – outsource it. That’s what recruiters do. Their fees may seem outrageous but they’re not when you consider the real cost of the time you’d spend doing it yourself – not only the direct cost of your time, but the much bigger opportunity cost of not spending that time addressing your essential complexity.

Another big mistake I’ve seen is startups taking non-core parts of their customers’ business process and incorporating them into their own product – invoicing, online payments, backup, logging and monitoring, reporting, hosting, etc, etc, etc. These aspects are peripheral to your essential complexity so they should be peripheral to your product. They are solved problems.

Usually, what superficially seems like a simple problem to solve inevitably turns out to be more complex. By implementing the functionality in your own product, not only have you lost that time you could be spending on essential complexity but you’ve also taken on the burden of supporting and maintaining a feature that doesn’t return anything for you in value and differentiation. You’ve basically re-invented the wheel.

Don’t forget that the vendors of these systems understand their overall domain pretty well (it’s their essential complexity) and they are building products that cover 70%-100% of the most common requirements. At the same time, you likely understand your own problem domain better than almost anyone so, if you happen to have unusual or unique requirements on invoicing, data backup, etc, you’ll know about it. If there is some aspect of these systems that you are differentiating on, that validly falls into your essential complexity.

These days, with SaaS companies proliferating and APIs available for more and more business systems and processes, it’s very simple to incorporate all of these important but non-core aspects into your overall offering with minimal effort.

Today, most companies wouldn’t imagine building their own data centers – most companies just use the Cloud (Amazon EC2 or similar).

Take that to it’s natural conclusion – what other aspects of your business can you out-source?

Beware False Economies

In a startup, money is always tight, and managing your cashflow is critical to success. However, it’s easy to let this drive you into false economies.

In every business there is “busy work” that is no fun but just has to get done. In startups, this is a bigger problem because people have to wear many hats so this busy work has to be done by someone who has many other things to do. Some of those things are likely to be part of your essential complexity.

Every case is unique but always think through the true cost of having internal resources spend their time on busy work that could be easily automated and/or outsourced. Again, there is a direct cost but a bigger opportunity cost.

Don’t Sweat the “Competition”

Another mistake I have seen is for early stage startups to waste time and energy worrying about their “competition”.

The reality is that, early on, most startups are in an ill-defined space and still in the Customer Discovery phase. This means your “competition” is hard or impossible to define. Your true competition will emerge over time, once you’re out of Customer Discovery and better understand your positioning.

In the meantime, just focus on creating value. You’ll likely beat your “competition” simply by focusing more of your energy on the essential complexity of the problem you’re solving than others. The faster you understand and solve the problem for your customer, the faster you create value.

Death by Meeting

So many people have written about meetings, that I will only give it a cursory discussion.

In my view, meetings are another necessary evil but are overused. Here are my guidelines:

meetings should be schedule to 30 minutes by default, not 1 hour
only invite those people who need to be there. The productivity of a meeting decreases with the square of the number of people in the meeting. Those people that need to be in the loop can be updated by one of the people in the meeting afterwards.
every meeting has a clear objective – not a detailed agenda, necessarily – a clear objective of what is expected to have got done by the end of the meeting
every meeting starts on time

Beware the Cost of Context Switches

Another hidden cost of having insufficient focus, is the cost of continually having to switch between tasks. This switching cost is often underestimated or ignored.

Paul Graham wrote a great post on the big impact of context switching to “makers” (software engineers, etc) versus managers, who are generally more used to scheduling their time in blocks.

Actually, the cost of context switch is there for everyone, even the most adept executive with the most fastidiously managed calendar. Research shows that human brains are very poor at multi-tasking and, ironically, those that think they are best at multitasking are actually the worst.

Don’t Underestimate the Importance of Motivation and Energy

If you’re only looking at the time spent, you’re only seeing part of the picture. You need to understand the importance of motivation, energy and morale on your own output and the output of the company as a whole.

Incidental Complexity not only takes time away from Essential Complexity but it also demotivates and saps energy and goodwill, especially when the people doing the work know they are working on Incidental Complexity.

Essential Complexity – What Should You Be Spending Your Time On?

So, what should you actually be spending your time on? What is essential complexity?

It’s tough to give a simple, single answer to this question.

Firstly, it depends on the stage of your business. At this point, I will pause to plug Steve Blank’s book “Four Steps to the Epiphany“. I dont’ know Steve but if there is just one book you should read on how to build a startup, you should read this one.

Early on in a startup, the essential complexity is working out who your customer is, the problem you are solving for them, the value of that problem, what your product is and how you’re going to solve it.

Once you’ve solved that (and only once you’ve solved that), your essential complexity becomes understanding your customers’ problem in even more detail and making sure you are designing, building and deploying a product that best satisfies those customer needs.

Further down the line, the essential complexity will be to make sure you stay ahead of your competition, increase efficiency, etc.

Of course, you can’t also ignore the #1 rule of any business; don’t run out of money. So, raising money is also essential complexity for most startups.

It also depends on your individual role in the business but this is easy to overstate. In a startup, people always have to wear multiple hats. The right things for you to do are the right things to do, whether or not they are strictly speaking part of your “job description”. People who have rigid ideas about job descriptions probably shouldn’t be working in startups.

Some Focused Suggestions

In a smaller company (i.e. a typical startup), the first step is to make sure that everyone – from the CEO down to the intern – knows what the critical success factors of the business are – i.e. what are the things that the business needs to get done in order to be successful. This list will change over time but needs to be regularly communicated and repeated. Achieving these critical success factors is the essential complexity for your business.

With that list in mind, every person in the company needs to get into the habit of asking the following question:

Of the things I could be doing, am I currently doing that task which most contributes towards the business achieving its critical success factors?

Of course, people won’t always get the answer right. Also, very often, and particularly for those people lower in the organization, they may be several degrees of separation away from the overall success factor for the business.

However, don’t underestimate people too much – I think most people will answer this question correctly more often than not. The trick is to get and keep the list of critical success factors at the front of everyone’s minds and to get people into the habit of asking the question of themselves, and of others.

The next part is about communication. People need to communicate what they think their priorities are and why. This helps flush out people’s dependencies on each other.

In a very small organization this can be done verbally. Once you’re beyond about 10 people, it makes sense to write these down and communicate them – I’d recommend weekly, by email.

As the organization grows, you’ll have to get more formalized. In a bigger organization I’ve seen large SIPOC exercises run at whole-company off-sites work very effectively.

Whatever the size of the organization, the most important thing is that you and everyone else understand the difference between essential complexity and incidental complexity and what the specific critical success factors are for your business.

Focus, focus, focus.

Agree, disagree? Please leave a comment (link at top of article).

observations from the startup frontlines

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Definition

Unit Economics

Extra Layers of Complexity

Marketplace Example: Wonolo

Demand-side

Demand-side CAC

Jobs

Demand-side Break-even Point

Customer Lifetime & LTV

Supply Side

How much Supply is needed?

Supply-side CAC

Impact of Supply-side CAC on Demand Side

Marketplace Dynamics

Ignored Components

Negative Unit Economics and The Big Gamble

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Part 1: Unit Economics Basics

What’s a “unit”?

Variable vs Fixed Costs

Introducing our Lemonade Stand

Customer Acquisition Cost (“CAC”)

Our Lemonade Stand

LTV

Our Lemonade Stand

LTV:CAC Ratio

Our Lemonade Stand

MRR

Break-even Point

CAC-doubling Time

Churn

Marginal Operating Costs

Contribution Margin

Going Beyond Unit Economics

Scooter unit economics: a Cautionary Tale

Summary so Far

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So, what is it?

So, is it a fad?

Pragmatism Rules

What to look for

Side effects

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Electrons vs Atoms

The Comparables

Brute Force Growth vs Long-term Value

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Know Who You are Looking For

Write a Job Spec

Cultural Fit

3 Kinds of People

The Danger of Big Company People

Finding Candidates

Find a Good Recruiter

Compromise

Dealing with Entitlement

One and Done

Standardized Assessments

What to Ask

What Not to Ask

People to Avoid

Talented Assholes

Heroes Need Not Apply

“I’m not technical”

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Focus – a definition

Incidental Complexities

Decisions

Choice is Bad

Outsource Everything You Can – Use Specialists and Don’t Reinvent the Wheel

Beware False Economies

Don’t Sweat the “Competition”

Death by Meeting

Beware the Cost of Context Switches

Don’t Underestimate the Importance of Motivation and Energy

Essential Complexity – What Should You Be Spending Your Time On?