Marketing ā Growth
As you can probably tell, Iāve been using the term āgrowth practitionerā rather prolifically these days. I tend to think a lot about the work non-data people working in data-adjacent roles can do with data ā the kind of work Iāve done in the past and would like more people to do.
And one thingās for sure: Itās not Marketing.
So what is Marketing?
Well, itās an umbrella term that includes many different types of work including demand generation, content marketing, product marketing, and so on, each requiring a distinct set of skills. If you wish to dig deeper, I havenāt found a better breakdown of the subgenres of marketing than this post by
:Now, if I were to define Marketing in simple terms, hereās how Iād do it:
Everything an organization does to attract users is Marketing.
However, ecommerce and enterprise sales aside, thereās a lot that organizations need to do to make a new user do the following things:
B2B
Come back to the product the second time (one of the hardest things)
Do things in the product to derive enough value to keep using it until the free limit is reached
Upgrade and then expand usage to continuously derive value and remain a happy customer
B2C
If mobile only, keep the app installed after the first usage
Keep coming back to the product regularly to transact or consume content
Itās not a marketerās job to do these things because they require a very different mindset and skills, ideally found in a growth practitioner.
Whatās the definition of Growth Practitioner?
Iāve been asked that question multiple times recently and my definition is continuously evolving; hereās the latest:
A growth practitioner uses data to get more people to use a product more often.
They donāt do marketing just like they donāt manage the product. However, a growth practitioner clearly understands where users come from and what it takes for them to succeed with the product. Additionally and most importantly, they have a sound understanding of every stage of the data lifecycle ā origination, collection, integration, analysis, and activation.
Keeping that in mind, hereās how Iād define the practice of Growth:
Everything an organization does to retain users and turn them into buyers is Growth.
First Marketing, then Growth. The two are distinct and overlapping responsibilities lead to confusion and frustration.
Growth Team Structure
So then, what does a growth team look like?Ā
is the only person who comes to mind who has done a lot of thinking in this area. I particularly like her definition of Growth:Growth is a blend of marketing and product, built on a foundation of data.
The source of that definition is this growth team structure map by Elena. I think itās a useful rubric for teams looking to build their first growth team; however, I firmly believe that acquisition is a function of Marketing and should not be conflated with Growth).
The way I see it, a solid growth team must include people who deeply understand the business, the audience, and of course, the data. And the person who leads the team must have a deep understanding of all three.Ā
In terms of the structure, Growth is a cross-functional team comprising at least one of each of these:
Software Engineer
Data Engineer
Data Analyst
Product Manager
Growth Practitioner
This is also the team that owns instrumentation (collecting data that originates in the core product) and leverages data contracts to maintain a high bar for data quality.
Check out last weekās post for more on data contracts:
At smaller organizations, itās normal for a software engineer to own data engineering workflows, and for the growth practitioner to manage the product and perform analyses.
Ultimately, organizations need to experiment with team structures rather than replicate what has worked for others. In fact, Iād argue that now is the best time to disrupt team structures of the past because everything ā how new technology is built, how itās used, and what customers expect from it ā everything is changing.
From Marketing to Growth
When I joined Integromat in 2018 as the first go-to-market hire, product education and organic user acquisition were my primary responsibilities. I kickstarted the user community, created lots of product tutorials, collected feedback from customers and partners and relayed it to our CTO, wrote blog posts, and ran webinars ā essentially, a combination of product and marketing activities.
Later, when user acquisition was no longer a problem for us (we were acquiring almost a thousand new users every day), only then did we decide to build the growth team. And thatās when I was able to take off my marketing hat for good and embrace the growth mindset as the first Head of Growth at Integromat.Ā Ā
Even though it was the first time I was going to work with data at scale, by then, I had already gained a solid understanding of every stage of the data lifecycle. I knew that event data from our core product (the web app) was necessary to run experiments, and running experiments ā both in-app and outside ā was the only way to drive incremental growth for the business.
Keeping that in mind, and the fact that we had the opportunity to start from scratch, I focused all my energy on setting up a robust data foundation in collaboration with two of our software engineers. As part of the effort, we evaluated tools, set up processes, and created documentation to collect and move product data to our activation tools.
In the absence of dedicated Product and Data teams, besides running experiments, my team and I did everything we could to increase adoption, activation, and retention. With so much to do, Marketing was the last thing on my mind.
But you know whatās funny? In my next job where I did most of the same things, Growth didnāt exist and I was a Product Marketing Manager. I was essentially doing two jobs, splitting my time between growth activities and some product marketing ā it didnāt seem sustainable so I decided to quit.
The Attributes of a Growth Maverick
Like software engineers doubling up as data engineers in the absence of a dedicated data team, marketers and product managers too can double up as growth mavericks.
So what does it take to become a growth maverick?
They have a sound understanding of every stage of the data lifecycle and make a concerted effort to stay abreast with the engineering lifecycle of the product theyāre trying to grow.
They can move data between tools and databases and write some SQL to build analytical data models to power reports and segments in the absence of an analyst.
They certainly donāt mind getting their hands dirty when they encounter new technology and are able to navigate a productās API docs to understand its true capabilities.
Needless to say, they intricately understand the product and the business, can empathize with the different audiences they cater to, and are motivated to drive data-powered growth.
If this sounds like you, well, youāre a growth maverick my friend! š¤
And if you need my help becoming one, get yourself an upgrade and join the databeats community!