What I want to do today
is I'll start really high-level
and then narrow in on the specific piece
of building a growth machine
and go through it step by step, tactic by tactic.
A little bit more background on myself:
I've worked both on the B2C and B2B side, everything from building a user base of millions
of daily active users to, obviously, Hubspot
which is more in the B2B-SAS space.
Building Growth Machines
There's really one mission, one goal
that I think we're kind of all in this startup space,
And that's really about authentic growth.
Growing something that is authentic
that really provides values
that's long lasting.
Not the type of growth that kind of explodes out of nowhere and then disappears
the next day.
But there's a few things that we need to understand
at a very high level that growing in a world of software
has massively changed.
The first big change that we've seen is that the lines
between marketing and product are completely blurry.
Really the question is where does marketing end
and product begin in software?
Because certainly, from a user perspective,
they really see it as one cohesive experience.
They don't care how your organization is structured,
whether you have marketing separate from engineering.
They view it as one fluid experience, and that better be
a great experience.
The second big thing is that what we've called marketing
in the past has become way more technical and quantitative
in the world of software.
What I would call "Mad Men skills,"
that qualitative soft side of branding skills
are becoming less important in
the day and age of software.
They're still important, but the quantitative and technical
piece has become a much larger piece of the pie.
The third big, high-level change is that data
is just becoming more accessible,
both qualitative and quantitative.
If we look back five or six years ago, tools that were
commonplace now like mix panel, and the dozens of analytics companies out there,
were either just getting started or even didn't really exist. This is becoming easier and easier for us
as we move forward.
Last but not least, scale and speed of growth
has really accelerated.
So the time from zero to a million users in the B2C world
and the time from 0 to one million ARR,
in the SAS space, is accelerating.
And so because of a lot of these changes,
we need to realize a few things.
How to Approach Growth
Number one is that growth does not equal acquisition.
This is the biggest mistake I see people make,
is that they think they can grow just by driving
at the top of the funnel.
But I can increase activation rate, retention rate,
our revenue and referral,
and our overall growth as a company will increase.
We need to look at it as one cohesive place
and one cohesive funnel.
The second big thing is
growth efforts actually require mixing the skill sets
of product marketing, engineering, and data all together,
to basically achieve and work on these initiatives.
You can't really think about them in silos
because you end up with a bunch of gaps in between
all those layers of the funnel.
And last but not least,
growth and how we've technically thought about
product in the past is actually pretty different.
The methodologies, the way that
we approach these problems, is different.
And so at the fundamental layer, there's really three things
we need to achieve: we need to build core value,
we need to give the largest percentage
of our target audience to experience that core value
as quickly as possible, and then we need to get those users to experience
that core value as often as possible.
Doing the first bucket, solving the first bucket,
is very different than solving the second and third bucket
in the way that we approach those problems,
evaluate those problems
and solve those problems, eventually.
And so one high-level way that we can think about growth
as we move forward is that
any good product, basically,
if you build really deep core value,
something that really strikes home with people,
you should have a natural adoption curve. Customers will refer
other customers, and you'll grow naturally.
But that doesn't necessarily represent
the true growth potential of the product or company.
Growth is really about how we take that
from the gray line to the blue line and basically optimize all of those different things
and all of those layers of the funnel
to make sure that we're reaching our true growth potential.
At the end of the day, when I talk about growth,
It's not necessarily the way everybody
talks about growth.
Growth is more about a change into
how we think about our team structures, people,
methodologies and processes.
It's not so much about tactics or hacks,
where I think most of the people
spend their time consuming.
The one piece that I really want to talk about
is not with tactics.
Learning how to grow authentically does not
start with tactics.
The one that I really want to dive into is process.
Process Versus Tactics
When I say this to people, "This is where
you should be starting,"
a lot of people kind of look at me wide-eyed
and say, "Why the hell should I start with process?
I'm a small startup. I don't need to think about process. I just need to move forward, just give me
the latest tactics that work."
But there's really four reasons you need to focus on
process first and tactics second when it comes to growth.
The first is that what works for others is not going
to work for you.
At the end of the day, your audience is different.
Your product is different.
Your business model is different.
Your customer journey is different.
Your business is different, plain and simple,
from one business to another.
You can draw some analogies, but at the end of the day,
what makes a really successful business is combining
a unique set of variables together.
You need a process that's going to uncover those
unique set of variables.
To figure out the ones that work for you,
figure out the combination that works for you
and not necessarily always rely on looking at others.
The second big thing is that growth is assembled
from a lot of small parts.
And so we see growth curves like this, often in TechCrunch
or other press articles.
We tend to really want to focus on this point.
We're always like, "What is the one thing that they did
that basically caused this explosive growth?" But what we should really be asking is, "What are all the little things that they did to get there? And what are all the things they did
to keep it going afterwards?"
Because, at the end of the day,
silver bullets don't exist.
There are certainly things that you'll do
that will be outliers.
They will cause a magnitude order more of growth
than some of the other things.
But at the end of the day, it's never one thing that
gets you on that growth trajectory.
We need basically a growth machine that's going to
continually test all of these little inputs
and learn from them over time.
That will lead to the successful combination of things
that will work for our business.
The third is that the rate of change is accelerating.
This is the one that I think about most
and actually worry about the most.
If we just look at acquisition channels we can look at
retention loops and engagement loops as well.
This is already out of date, but
over the past year there have just been
fundamental changes in every single one.
Take Facebook for example.
What works on Facebook today, either on ads or platform,
is not what worked 90 days ago.
My team is completely operating and executing
a completely different set of tactics
than they were 90 days ago
because all of these channels, all of this world,
is accelerating at a massive rate.
When we look at this on a macro level,
this is a graph from James Currier
who ran Ooga Labs and has built
I don't know how many companies to the
tens of million users.
He put this together, and as you see
on a macro level, as time has kind of gone on
in the software and Internet world,
more and more channels basically appear over time.
More importantly, the cycle between
launching, peak effectiveness, and it depreciating
is shrinking, meaning it's accelerating.
What we need is basically a process that's going to
continually experiment and uncover
the things that are working,
and uncover the things that we thought worked
in the past that no longer work any more.
The fourth and last thing about process first
and then tactics second, and then we'll dive in deep,
is you need a machine.
A growth machine means three things:
it's scalable, predictable and repeatable.
We look for those three elements.
Because when you have those three elements,
that's when you know that there's a great foundation,
a great machine in place.
And that you know what your inputs are, and for those inputs,
what you're going to get on those outputs.
The analogy that I use is that it's the machine
that produces the tactics, but the process is
what makes the machine.
So that's why we start here first with the process. Diving into this process, this is exactly what we use
on my team at HubSpot and at previous companies.
It starts with the goals.
What are we optimizing for?
What to Optimize
We optimize for four different things.
The first is learning, first and foremost.
It's always about constant learning of your customer,
product and channels, and feeding that back
into the process to improve over time.
A failure to us is not a failed experiment or a failed initiative.
Failure to us is that we did something
and we didn't learn from it.
At the end of the day, if we don't learn from it,
it's pretty much useless.
The second thing is rhythm.
Momentum is a very powerful thing.
In the nature of a highly experimental process
and building these growth machines,
you're going to fail more than you're going to succeed.
So to fight through those failures, establishing that cadence to fight
through those failures to get to the successes,
is really, really important.
The third and fourth
are more from a team perspective.
We really optimize for autonomy.
Basically, individuals decide what they work on
within a given set of guard rails.
With autonomy, obviously, comes accountability.
You don't have to be right all the time
with this process and this team,
but there's an expectation to improve over time.
If you improve, that means you're learning
and you're applying those learnings back
into our process and our ideas.
At a high level, this is what,
step by step, the process looks like.
It looks very overwhelming at first,
but we're going to walk through it step by step
and it's actually very easy.
What you see here at the top level,
the first three stages,
is what we call our "zoom out" phase.
We do this about every 60-90 days
depending on what we're working on.
Then the bottom cycle
is what we run daily and weekly.
The first part of the zoom-out phase
is really about finding levers.
And the question that we're really trying to answer is,
basically, what is the highest impact area
that we can focus on right now
given the limited set of resources?
I used be very naive when I started my first company,
that it was going to be another three months
and then we would have everything that we would need
to execute all of our initiatives.
They're another six months;
we just have to get over this mark.
But I'm in a public company now
worth a billion and a half dollars
with over a thousand people.
I can tell you,
no matter how big you get,
you will always be limited.
Either by time, money or people.
You will always have limited resources,
and so you have to get really, really freaking good,
at day one, on how you answer this question.
Because order of operations does matter.
The way that we find
the answer to this question is we use
what we call our growth model.
It's basically just a giant Excel sheet.
And the growth model helps us evaluate
a few things that I'll talk about in a second.
But what I want to first talk about is how we generate
this on a business-by-business basis.
The growth model starts with, basically,
identifying your top-level goal.
For one of our products called Sidekick,
our goal is weekly active users.
It's built for professionals.
It's an add-on to your email.
Professionals are on email often on a weekly basis,
and so we chose this weekly active-user metric
as our top-level goal.
At lot more went into it,
but I want to move on into the deeper pieces of this.
We start with this, our output.
This is what we want to drive.
This is how we monitor whether we're growing or not.
But we don't sit there and try to come up with ideas
like, "How do we move weekly active users?"
The big goal is, "How do we break this down
into small enough pieces where it becomes actionable?"
And that we can evaluate all of those inputs.
We break this down just like a math equation,
into smaller and smaller pieces.
Weekly active users basically equals
the number of new people I've activated or acquired
in a given time period,
plus all the people that have retained
from previous time periods.
But we don't stop there. We go deeper.
We can break down "new activated" into its sub-components.
The number of people that might have registered
via Facebook ads, times their activation rate,
plus the number of people that have registered via viral
times, their activation rates,
so on and so forth.
And then we can break those components
down even smaller.
The number of registered users via viral
is really a function of the number of impressions
I get to the invite page
times their conversion rate, times the invites per user,
times the email-click rates, so on and so forth.
And we go through this with all of our different variables.
We do the same thing with retention,
and at the end of it is that output, that model.
We build that model. We build that Excel spreadsheet
to basically look at things a year to two years out.
And we evaluate three things.
We look at the baseline, where are we today
on all of these different inputs and variables.
What do we think the ceiling is?
Basically, it's our educated guess based on where we are today.
Where do we think we can get that number to
if we worked on it, if we focused on it?
Then, looking at that ceiling, what is that impact?
Where is that sensitivity over time?
We're not looking for things for impact
that are going to give us really short-term impact
in the next week or 30 days.
What we want to look for
are things that have impact over time,
the next six months to a year.
If you look at just the next 15 to 30 days,
what you end up doing is missing a lot of core components.
Things like retention or virality that compound over time
get hidden when you take a really short-term view.
So we look at these three things to, once again,
go back and answer that question.
What is the highest impact area
that I can focus on right now
given limited resources?
After we identify that area, we set some goals.
Goals and Frameworks
We use a framework called OKRs, objective and key results,
that a lot of you are very familiar with.
This is widely used at Google, Zynga, LinkedIn,
a bunch of other top Silicon Valley companies.
But the goal here is that we state our objective.
This is kind of a "why?" behind this area
that we've decided to focus on.
We set a timeframe no shorter than 30 days,
no longer than 90 days.
Anything shorter than 30 days is just too short for us
to make a meaningful impact.
Anything longer than 90 days means we're probably biting off
more than we can chew at the moment. Then we set three quantitative metrics
that basically tell us whether or not
we're achieving that objective.
A good example of this is,
at one point on one of our products,
we wanted to make virality a meaningful channel to us.
We had this metric which was the number of new users
coming from viral divided by our weekly active users.
It gave us a rough percentage of what our active user base
was generating in terms of referrals
on a week-to-week basis.
We set a timeframe of 90 days,
and then we really focused in
on trying to achieve these KRs.
The one I really want to focus on is this first one. We always set OKRs on the inputs, not the outputs.
Meaning if our output, our top level goal,
is our weekly active users,
we're never going to set an OKR against weekly active users.
We always want to focus on the inputs.
Focusing on the outputs basically leaves it
to be too broad,
too tough to come up with ideas,
to really see if you're moving that number
in a short enough time period.
And so once again we go back to that model,
and we focus on one of those inputs
that we believe will lead to the outputs.
After we set some goals, we go through our third stage
of the zoom-out process.
This is just exploring the qualitative and quantitative data
of the area that we chose to focus on.
How we draw insights is in combining three things,
not just the quantitative, not just the qualitative,
but also our own intuition.
A lot of people debate, "Is growth an art or a science?"
The reality is it's kind of a mixture of both. A lot of times the quantitative data
tells us what's going on,
but it doesn't tell us why it's happening. And so we have to move to our qualitative data.
And then, even if we get why it's happening,
we're never going to get
all of the information out of our users
that tells us what the solution is.
Our users don't really design the solution for us,
so you have to combine your own intuition
to really draw massive insight into growth.
The way we explore
that qualitative and quantitative data
is through a number of different measures
on the qualitative side, whether it's user surveys,
one-on-one conversations, or support tickets.
On the quantitative side,
we have a number of different tools
that we use, whether it's event-based data
or revenue data, or just raw data in our data store.
But we explore it in a number of different ways
depending on the area.
A few different tools that we use in this process
are what we call "mini models,"
basically a more narrow version
of that big macro model.
We got through the same process.
We look at the baseline, ceiling and sensitivity over time.
If we're really focused on that viral piece,
we'll break that viral piece down into even smaller pieces.
We'll look at which of the smaller pieces
are the most impactful ones that we can work on
and where we can start in this OKR period.
We also, to gather qualitative data. We do a lot of
one-to-one, open-ended email surveys.
If we want to know why a user didn't convert
on an invite page, for example,
we will actually email users who hit that page,
but didn't invite anybody, and ask them why.
Like, why did they decide not to do it? What we end up with is a bunch of qualitative-trend information telling us things like,
they didn't know who to invite.
The reward isn't high enough.
They didn't want to spam friends.
The big purpose behind this is we can take
this qualitative information,
and now we can come up with a lot more actionable ideas.
We can look at one of these things and say,
"Oh, they didn't want to spam their friends."
Well, we can add trust badges.
We can show them the email that we're
going to send to their friends. We can add copy that talks about and ensues trust.
All of these experiment ideas come up
in a very targeted direction.
I should say, we use these techniques
not just on acquisition channels like viral and referral
in the B2C spectrum, but all parts of the funnel,
whether it's acquisition, retention, revenue
And so once we complete that zoom-out period,
which will take anywhere from
a few days to a week most of launch into this weekly cycle.
What we start off with is basically a brainstorm
which generates a backlog,
and the big key here is that we brainstorm
on the inputs, not the outputs.
This is a common theme that you'll
be hearing throughout this whole thing.
If we look at something we want to improve
like activation rate, the number of people
who have converted from registering
to activating in the product,
we will break that down into its smaller pieces.
And we will go through one by one on the smaller pieces and
brainstorm against those individual pieces first.
Then we will brainstorm against the whole.
Once again, the smaller you break down the pieces,
the more actionable and easier it becomes
to come up with ideas.
There are four different tools that we use
to come up with growth ideas.
These are all stolen out of a great book
called "The Innovator's DNA."
I totally recommend reading it.
There are just four different techniques,
and the reason we design specific exercises
are so that nobody on the team should be sitting there
saying, "I need ideas."
They always have a set of tools to
go back and generate more ideas with the team.
This helps as you scale this team out over time.
We do a couple things. I'll highlight a couple of them.
One is we do do some observation of
how others are doing it.
So if we want to look at that referral mechanism,
we will go and observe how a bunch of other companies
are basically operating their referral mechanism.
But we'll specifically look at companies that are
not in our competitive space,
because it actually does a much better job
of generating ideas.
We do another thing called "question storming."
What that is is we get in a room,
three or four people,
and we do nothing but ask questions
about our given focus area for 15 minutes.
That might be questions like,
"Out of users who are inviting,
who are they inviting?
What are the common elements?
Out of the people who didn't invite,
why didn't they invite?
How many people are they inviting over time?"
All these questions start to lead and start to point out
a bunch of places that we don't understand
in our growth model and in our product.
And so typically any great answer, any great solution,
starts with a great question. And so this is a way to come up with a lot of inciting ideas, questions that incite ideas.
The next step is that we'll go and we'll prioritize,
and so the team members can choose any ideas to work on.
But they have to prioritize,
and they have to talk about it in a very common language.
That common language is about three things.
The first is the probability of success,
and we look at low, medium and high.
It's very quick assessment.
Low probability things are things
that we've never done before; it's an area we've never focused on. We know very little about it.
High probability is this experiment,
something that's generated off of an experiment we ran
and learned something from very recently.
The second is impact. We'll actually
make a prediction, which I'll talk about in second.
And the third is, of course, resources.
How many resources is this going to really take
But the impact, the prediction,
is the most important part.
And the way that we do that is we generate a hypothesis.
It pretty much comes in this form.
It's saying, "If this experiment is successful,
this variable will increase by this much because..."
And then we list out our assumptions.
What this causes the team to do is actually
think ahead about the ideas.
It removes that gut and that emotion from the process,
and they're really looking things
in a common language across the entire team.
They're really taking impact into account,
and so what this does is, and
we don't expect people to be perfectly accurate on it,
but it does two things. It basically
gets people to think about their experiments
in a more structured way, ahead of time,
and then what we'll talk about
in the last stage of the cycle
is that it actually helps us extract
way more learnings out of the process
than we would if we did not do this.
The way they come up with these assumptions
is they look at the quantitative data, the qualitative data,
or even secondary data, things that they might have read,
ideas they might have gotten from other places.
We use a tool. It's basically a Google doc.
It's called an experiment document.
They take about five minutes to write out
this hypothesis and their assumptions.
That way the team can access the reasoning behind
all of the different experiments that people are running
at any given time.
It also acts as a very quick knowledge store for us,
so that we can build off the learnings,
build off the successes over time,
especially as the team grows and grows and grows.
So, once we prioritize,
we design what we call a minimum viable test,
and that's really the minimum viable thing
that we can do to understand
that hypothesis that we produced.
There's two really big forms of this.
It's about one, the efficiency.
What is the least amount of resources that I need
to gather this data?
But it has to be reliable and valid data.
Sometimes we can come up
with a super efficient hack or thing to test
this idea or this experiment,
but if we're not going to get valid data out of it by the end,
we're not going to learn from it.
And like I mentioned before,
if we don't learn from it, that's the true failure for us. In some cases we would actually put more work into it
to just make sure that we get really valid data
out of the experiment.
We write out the experiment design
in a bullet-pointed list.
That way other people can understand the context
of the design, and it also helps
really reinforce that second piece,
the valid piece.
It forces the team members to think through
how they're going to run this experiment ahead of time,
to make sure that we're going to get
really valid learnings out of it.
After we design the test, we'll go implement.
This is super easy. Go get shit done.
There's really nothing to explain there.
And then we'll go into the most important step.
This is about analysis and learning.
After we run the experiment, it's all about
how to extract the most learnings out of this
as we possibly can.
The first thing we'll look at is, "Was this a success or a failure?
Did it improve or not improve
the thing that we were targeting?"
The second is impact.
By how much, how close to our prediction were we?
And then third, and most importantly, is, "Why?"
Why did this thing succeed, why did it fail?
Why were we way off on our prediction, why not?
Because in this question, "Why?" of what's happening,
it really forces us to think about how our users
might be reacting to the certain experiments
that we're running.
Our users or our channels. It helps us generate not only new learnings,
but also new experiment ideas
that we can then go and run.
They'll write out in really quick, bullet-pointed format
the "Why?" and the results and any sort of action items that they can
into the experiment document.
And then, last but not least,
we take the successes and we systemize them.
There are two ways we can systemize.
We try to systemize as much as we can with technology
and automate things.
Certain things we can't automate.
Certainly things in content marketing and stuff
just require a human involved,
and for those things we write playbooks.
The playbooks are basically there for us
to make sure that we're standing on the shoulders
of our successes and that we're not constantly repeating
things and learning the same thing over and over over,
and that the whole team can move forward.
We'll just continually repeat this
over and over and over,
within a 90-day-OKR period.
What that looks like for a member on a team is,
on Monday we have one meeting as a team.
It's our growth meeting.
We look at a few things,
but we really focus on learnings.
We don't really focus on what we did.
It's just all about the learnings that we extracted
out of the process and sharing them across the team. Then the rest of the week, they're
going through the other steps.
Most of the time is probably spent
on the implementation stage or the analysis stage.
A little bit more about this growth meeting.
Once again, first and foremost, it's about learnings.
That really hammers home the number one
important thing about this process.
But then, second, we'll go through our goals
and talk about anything that might be blocking us
from achieving those OKR goals
through one of those cycles.
We have a template for our weekly meeting.
This just kind of forces everybody
to follow the same format, speak the same language
and make sure we're extracting as much learnings
as we can out of the entire process.
Then what we do on a quarterly basis,
or even maybe semi-annual,
is that we take a step back
and we say, "Well, how can we optimize this,
our whole system, from a macro level?"
And so we look at a few things.
The first is kind of our batting average.
How many successes to failures did we have?
Within an OKR period, what we should find
is that that batting average should improve over time.
What most OKR periods look like is that
we start off, and every experiment is a fail
because we're learning.
We don't know anything about that area.
But as we learn and we feed that back into the cycle,
instead of "fail, fail, fail,"
we'll start to hit a few.
It's like, "fail, fail, succeed, fail, fail, succeed."
And towards the end,
where we really know and understand the area,
we're like, "success, success, success,
occasional fail at the end."
But that's kind of what a typical OKR period
feels like for us.
We also look at accuracy,
our predictions getting accurate more of the time,
and we look at throughput.
How many experiments are we running
in a given time period?
We talk about three things of how we can optimize this.
First is team.
We know we can improve our current team skills.
We can add to the team, and we can remove from the team
in some cases.
The second is our infrastructure whether that's our analytics,
both on the quantitative and qualitative side,
or even the experiment infrastructure we use
to run a lot of these tests.
And then the third is our process.
We could, basically, blow up this whole process
and refine pieces of it to make it more efficient,
produce better quality ideas, or even get
more learnings out of it.
Three quick final words
on things that I often get asked.
The first is that iteration does not equal incremental.
This is, I think, a common misunderstanding, is that
even though this cycle is really based on
taking learnings and iterating on those learnings,
that doesn't mean we're always doing incremental things.
We might actually take a learning, and that learning
may inform us to do something really, really big.
Also, basically, the size of the project
does not equal the impact of the project.
A lot of times, very small things
can have much larger impact
than very large initiatives.
The second is that it's never too early
to start this process.
I think a lot of you guys are probably
You might not adopt all pieces of this process,
but the more important things
are, basically, the checkpoints,
the questions that you ask along the way.
Thinking about the impact of your initiative,
thinking about how you extract learnings
out of everything you're doing,
and basically feeding that back into
your ideas and how you prioritize everything else.
And so, once again, you might not adopt this
but the core elements of this still hold true
whether you're early-stage or late-stage.
Last but not least, a final final word
is that there's nothing special about this process
absolutely at all.
It's basically just a combination of the scientific method,
some lean start-up principles,
and some own specifics to the topic
and initiative of growth.
Success in this really comes down
to grit, focus, and persistence.
Most people just don't have the three things
to maintain this process.
When they have a failed experiment,
they won't sit there any analyze
and understand why it failed.
They just want to move on to the next shiny object.
Or they don't want to prioritize based on
that impact probability and resource,
and they just want to work on things
that they feel like working on.
It's those kinds of traps that actually derail people
and get people off the track from building
a really solid foundation and growth machine.
That's it. Thank you.
Building a Process Around Content Marketing
I would actually say most content marketing
is even more process-driven than things like
referral and virality.
And so long-term success in content marketing
is around establishing a series of steps
that a number of team members can walk through
to produce a quality piece of content
that you know with a very high probability
is going to get a lot of traction with your audience. It's less about producing those one-off hits.
One of our blogs, for example,
our product called Sidekick,
we write some very long-form content
specifically optimized for SEO.
We developed a playbook over time
that was about three or four pages long
about everything on
how you start the research process,
what to go after,
and then how to choose which one to go after. And then how you
break down and build an outline for that page.
Then how do you take that outline
and execute the research to fill in that outline.
So on and so forth, all the way through to promotion.
Those are things that we've
experimented with over time
and have been able to prove
with a fairly regular piece of success
that they are going to produce
a high probability of success.
And so I think that's probably
one of the bigger mistakes in content marketing: people think about pieces of content
as these one-off things.
Content marketing is really about
how do you build a flywheel, a system,
that continually attracts the target audience,
captures them in a conversion funnel,
nurtures them into some sort of specific point.
A system is even more important,
and playbooks are even more important
for things that require more humans,
like content marketing.
Otherwise, you end up with
all sorts of variability that can lead to
a lot of initiatives that aren't really useful.
Content and Network Effects
If we're talking about content marketing specifically,
I think one of the big secrets about content marketing
is that, and specifically what Hubspot talks about,
is that marketers were actually the perfect audience
for content marketing.
When you think about the loop of content marketing
in terms of producing content and distributing it to a base,
that base shares it out to draw in newer people
and then capture those people.
There's a few pieces of that funnel
that are a lot lower-friction than other audiences.
Particularly that distribution piece.
A lot of marketers share a lot of content, right?
And a lot of marketers are okay with handing over
their information to download ebooks
and all sorts of other stuff.
Compare that to a developer audience.
Developers tend to be a little bit more conservative and
a little bit more cynical.
So, if you put a pop-up form in front of them,
they're going to tell you to go away or screw off.
They're not going to come back.
And so you have to treat audiences very differently,
but that doesn't mean that you can't use
content marketing for different audiences.
At Hubspot we've also built a great content presence
Salespeople actually share even less than developers,
which is kind of crazy.
But if you even look in the developer space,
you can look at things like New Relic or Docker,
they've done an amazing job
of building amazing content presences
using a different set of tactics, techniques and systems specifically designed for those audiences.
That goes back to first point of,
yes, they're both content marketing,
but the underlying variables, the underlying inputs,
are actually completely different
when you actually break it down.
Variables That Make a Difference
There are a couple things,
you know, distribution methods of the content. In Hubspot's case, the marketing case,
to distribute they're going to get a bunch of marketers
to either share via Twitter or Facebook,
because of where they live and share things.
Or they'll run a bunch of webinars and stuff,
because marketers will get on webinars.
Docker, on the other hand, is like,
"Well, you know what?
Actually, the sharing rate for developers on Facebook
is not that low. We're not going to really do that."
What they actually did is they reached into
their user base, they got their customer base
to write a bunch of different, unique ways
that they were using Docker,
and then Docker basically used
the rest of their distribution base
to put a bunch of traffic
towards that person than wrote about them.
To vote things up on Hacker News, right?
Docker was on the front page of Hacker News
every single week for, like, four months straight.
It was kind of ridiculous for a while
because they identified Hacker News and these other places
as, "This is where this content is going to be distributed,"
which is totally different than Hubspot.
It's going to be using a totally different set
of distribution tactics for that audience.
So that would be one difference.
If you want to talk about more details,
at Hubspot you're going to have
New Relic and Docker, you're not going to have that,
because that's going to scare the developer audience away.
You're going to have a bunch of webinars over here.
You're going to have more things like free trials
and free setups, because developers are a little bit
more autonomous and individual and self-reliant.
There are all sorts of different, minute details
that will be totally different,
but at the end of the day,
it's still content marketing. It still follows
the same essential content marketing loop of
creating compelling content,
sharing it to your distribution base,
getting that distribution base to bring in more people,
capturing a piece of those new people
and rinse and repeat.
Cost of Creation Vs. Distribution
It's a spectrum. At the beginning
you want to spend more.
Content marketing is one of these flywheels
that has a very different trajectory
than a lot of other channels.
It takes a lot longer to get going,
but once you get it going,
it carries itself under its own weight and momentum
and takes off.
A total 180 from content would be paid marketing
where you can do a bunch of experiments
at a small scale, find something that works,
turn the knobs up really quickly
and you get this spike. Then you do a little bit more testing and experimenting,
find something else that works, scale it up.
Content marketing has a lot longer base,
it takes a lot longer, it kind of gets going,
so the key part of shortening that base
and getting that ROI is actually
is that distribution element of it.
I would recommend,
at the beginning, it's much more like 70/30.
Focus on a low quantity of really high-quality
pieces of content, and make sure they get distributed
as broadly as you possibly can.
And so one tactic I even use for my own blog
is that, when I started,
I wrote maybe once every month but focused on a really high-quality piece of content.
I developed a list of 30 friends,
and these 30 friends were all in the Boston tech community.
So there was some sort of density effect there,
and so I would launch this quality piece of content.
I would write all my 30 friends and say, "Hey,
I spent a lot of time on this piece of content.
I just need your help. I need a favor
with these next few pieces of content
to help get it going.
Here's what I'm trying to achieve,
do you mind sharing it here, here, and here?"
The key of that is maybe 15 out of the 25
would share it, but they had such a density effect
that most people saw two or three people share it,
and then it created this weird psychological effect of,
"Well, if they're sharing it, I must share it too."
And they just kind of exploded. I actually went from zero to 1,000 subscribers
on my blog in three posts.
And it's very doable on content marketing
and the B2B side, too, if you
execute that distribution piece
right up front from the beginning.
Then, over time, that spectrum changes.
You'll shift more towards 50/50 for a while,
content versus distribution.
Now Hubspot's at where they've got a
massive email newsletter list
and a domain authority of 90 something.
They can write some piece of content,
and it's going to get distribution no matter what.
For their assets, for where Hubspot's at,
it actually makes more sense to spend more time
on content production than it actually does promotion.
It depends where you're at in that spectrum.
How to Hire for a Growth Team
The concept of growth teams is still fairly new.
Facebook was the first one.
They instituted it about five years ago,
and they've become more and more popular.
Now pretty much all Silicon Valley companies,
from LinkedIn to Google to Uber to Pinterest,
all have some sort of concept of a growth team.
The likelihood that you'd still find somebody
with one or two years of experience
working on a very well-established and developed
growth team is still pretty small.
And so at the end of the day, you've got to look for
the people that have the inputs that
are going to generate the kind of output that you want.
The things that we look for on our team
are a few things.
This is actually on my site,
But the common things that we look for,
a few of them would be, we look for people
who are motivated by impact over everything else.
One way I evaluate this is, "Tell me about
your favorite project that you've worked on and why."
If they talk about it the context of
the impact it had on the business
or the product or the output,
I kind of know what they're motivated by.
But if they talk about, "Well, I did this thing,
and it was just pixel-perfect design,"
or, "It was just this crazy-hard engineering thing,
and that's why I wanted to do it,"
I start to understand they're motivations
are a little bit different and probably
not well suited for that. So that's one.
The second is voracious learners, people who are not only okay with
just understanding what happened
but why it happened.
I find a lot of people are just okay
knowing that the numbers went up
or the numbers went down.
But it's the people that you want
that are constantly asking "Why, why, why?"
Constantly learning, constantly learning.
So that's the second.
The third is more of a hacker mentality
than a craftsman mentality.
People that look to solve things very efficiently,
not look for the perfect long-term solution up-front.
Those are probably the three most common,
and the three most important,
and then I think I have three or four others
listed on our site.
Collecting Large Qualitative Samples
Most people think about qualitative feedback
as just one-on-one user-feedback sessions.
We actually spend more time automating
a lot of our qualitative feedback.
There's tools like Intercom and stuff
that can help with this.
We have something internal at Hubspot now
that we built among our team.
This depends on the volume
of your customers and users, too.
For more B2C-type products with higher volume of users,
we basically can identify certain segments of users
that have taken a certain set of actions,
or not taken a certain set of actions,
to trigger a one-on-one email to them
that looks like it's coming from one of our team members
asking a very specific, open-ended question.
We'll send out maybe a couple hundred of those.
The response rates are typically 30%
if you do it correctly, if you do it well,
and very well targeted.
Then we take all of those responses,
throw them into a spreadsheet and
somebody goes through those qualitative responses,
categorizes them, and then we end up with
that pie chart I showed earlier of
directions of qualitative reasons.
If we have a little bit more time,
we'll follow up with a second step
and we'll respond to each one of those emails. We'll ask, "Oh, that's interesting.
Please tell me more.
Why? Why did you say that?"
Something like that.
Get more detail out of them,
and through that interaction of email,
and if you ask the right, open-ended question
and not provide them the answer,
we tend to get really good response rates.
We get a lot faster feedback and a lot higher-quantity
of qualitative feedback than we would
through one-to-one user sessions.
We also look at support tickets.
We extract a lot of information out of there.
Sometimes we've done some of the live chat
in certain areas that people are
getting particularly stuck,
where we feel like we need to talk to them in real time
rather than triggering this after the event. Those are some of the number of methods
that we use to collect it a little bit more efficiently.
What Keeps Him Up at Night?
Well, how many things do you want to go into?
What keeps me up at night?
I think the first thing that worries me
is one of those first things that I was talking about, the rate of change.
The faster things change,
the higher the throughput you need
in terms of experiments. And actually, as your teams grows,
it's harder to maintain that super high throughput
across all of the teams.
Because if you don't constantly, constantly experiment,
you're just going to be sort of left behind, right?
This isn't the best example
because it didn't produce the best companies,
but during the social gaming boom on the Facebook platform,
it was the companies that ultimately had
early information and executed on that early information
of changes to the Facebook platform fastest. That's how Zynga won.
That was one of the main reasons they won.
And so that keeps me up at night as well, how are we constantly
experimenting with things?
I think the second big thing is,
in terms of growth channels and platforms,
we tend to go on a macro level
through these waves over time where
a couple platforms will launch,
and they'll totally change the game
and open up a crazy amount of opportunity.
Obviously, iOS and the Facebook platform
were that at one point.
It's cyclical, right?
We go through those periods. And then we go through
these periods where there aren't those
new, massive launches and channels,
and you just have to get really freaking good at competing
in one of the more mature, competitive channels.
That's much harder said than done.
I would say, things like content marketing,
we're probably in that stage right now.
There are some things you could point to,
whether it's things like the Pinterest platform
or Instagram, or you could say that there's even
a little bit more on content marketing. But even content marketing's getting
really, really crowded these days.
We're in that period where
it's really, really hard. It can be really hard to compete.
And then I think the third thing is that
this stuff is going to continually shift to be
even more and more technical than it is today. I think ultimately
that means you've got to
get more and more engineers involved in this.
And here's two problems with that:
more shortage of that type of talent,
and second is getting engineers to be
very interested in solving these problems.
There's a lot of negative historical bias towards it.
But I think Facebook and Uber and a bunch of others
have done a really good job of reframing the problem
to engineers, how big of a problem it is,
and making it really exciting initiatives to work on.
So, those are probably the three biggest things
that would keep me up.