Wait, so what’s a “Growth” team and why does my organization need one?

Recently, I’ve received a number of questions around “Growth” teams— particularly what the team even does, and whether or not it even makes sense to have as part of an organization. I tend to invert these questions back to the inquirer in order to survey general opinion. The responses that I’ve gotten are quite varied and illustrate the lack of clarity around this relatively new organizational function:

“Isn’t that just what Marketing does?”

“That’s within the scope of a Product Manager, right?”

“Oh you mean Sales and Business Development!”

“Hmm, I think either our Business Intelligence or Data Science team does that”

Let me start by sharing definitions from a couple of thought leaders:

From Andrew Chen’s “How to Build a Growth Team”

From Casey Winters’ “What are Growth Teams and What Do They Work On”

I like to think of and describe Growth teams in the following way:

Growth teams partner across functions to cultivate a data-driven culture of experimentation in order to drive product development or business decisions, and are especially relevant for digital ventures that are looking to scale.

Ok, there’s a lot to unpack in the above statement, so what I’m going to do is decompose it into several components…

1. Data-driven culture & the scientific method: Growth teams are only relevant for organizations that strive to develop a culture of being data-driven. Don’t take this assumption for granted as it’s not true for all organizations. A simple litmus test to determine whether your organization is data-driven is by asking any individual from a random function what data they look at, how they access it and how it informs their decisions regularly. Drawing from my own experiences:
   A) Kiva did a fantastic job of this by empowering everyone throughout the organization with self-service analytics via Looker (reference).
   B) Oportun built this into the organizational DNA by establishing it as a core competency, thus ensuring that every single function would rely heavily on data for any decisions (reference).
   C) Gojek, similar to Oportun, ingrained this in the company’s values from the get-go (shout-out to Crystal Widjaja); within its Financial Services Platform, I initially focused a lot on this evangelization while leading the BI team (reference 1 and reference 2).
   I cannot understate how important this piece is! Without broad organizational buy-in on the crucial role of data in decision-making, a Growth team will be doomed to fail from the get-go.
2. Product development & market fit: In the early stages of product development, the PM is typically running plenty of mini experiments in order to establish the foundation of his/her product for a given segment or market of users. Before there is significant quantitative data available about users’ behavior under a stable product, these decisions are informed by more qualitative sources such as UX research, market research and/or past industry experience. As the product naturally develops with product-market fit beginning to materialize, the PM’s role will tend to focus more on longer term product roadmaps, ensuring product operations are running smoothly, and working closely with the engineers to sort out tech debt. Effectively, the PM will not have the required bandwidth to think day-in, day-out about quantitatively analyzing user behavior as well as designing & running rigorous experiments alongside his/her other many hats. It is at this evolutionary stage of the product development life cycle when Growth teams become especially relevant as it will require that there be a dedicated team that is scrutinizing, owning, and acting upon user data + key metrics regularly.
3. Cross-functional collaboration & passing off the baton: Growth teams should be optimized for quickly running and iterating various experiments, informed by the scientific method. By failing fast, killing what doesn’t work and scaling up what does, Growth teams can effectively build a knowledge base that drives product and business decisions for other teams to scale and operationalize. Generally-speaking for most digital ventures, arguably the three most important functions for Growth teams to have strong relationships with are Marketing, Product, and Research. Why these three?
   Marketing needs to figure out which campaigns are worth taking to scale to execute regularly. The distinction between experiments and campaigns is key here as Growth ≠ Product Marketing.
   Product needs to understand what features are worth building to contribute to user engagement. This should be further enhanced by surveys from Research, as mentioned below.
   Research should partner closely with Growth to combine quantitative and qualitative insights to formulate a more robust understanding of user behaviors and market trends, as well as informing hypotheses that are worth testing.
   From my experiences with Gojek’s Financial Services Platform, we also forged close relationships with the Data Science, Data Engineering, Risk and Collections functions given our specific operational requirements. Do keep in mind that different business models will require different degrees of Growth teams in terms of size and organizational integration.
4. Digital ventures and the art of data-driven scaling: one final point is that Growth teams are not necessary for every single business (saying this might be tantamount to heresy within the Growth profession, oops). Growth teams are especially relevant when there are two ingredients:
   1) Requirement to scale - to what degree does the organization/enterprise require scaling in order to fulfill some objective — whether from investors or donors?
   2) Data richness - how readily available and robust is the data within your organization? Does it enable rapid iteration/experimentation with visibility into intimate user behavior?

This is a simple (and non-exhaustive!) 2x2 matrix diagram that I’ve created to illustrate how I interpret this context. Notice that upper right-hand quadrant, where data richness and high requirements for scaling is where organizations like Facebook, Uber, Airbnb, Gojek, Oportun, and Kiva — which all have Growth teams — fall under. Another note is that the upper left-hand quadrant will begin to move towards the right hand side of the data richness continuum as technologies such as IoT become more broadly adopted in various industries. As this trend unravels, Growth teams may begin to become relevant to corporations that were previously not rich in data.

The above summarizes how I think about Growth teams and when they can add value to an organization. Before I end though, I’d like to share some closing thoughts on two related and somewhat misunderstood functions: Business Intelligence (BI) and Data Science (DS).

As mentioned above, I formerly led the BI team, but what I did not mention is that in fact Growth was initially subsumed as a subfunction of BI. In the early days of our product development lifecycle, this was done because we were effectively responsible for the analytical aspects of Growth, while the implementation was largely carried out by the Product team. Furthermore, DS was a much smaller function earlier on in our organizational evolution because, in effect, most of the decisions that are now driven by machine learning-based models were made by more heuristics-based rules created by the BI team in the beginning. However, as the business requirements matured, the need for increased accuracy and precision in our decisions grew, hence the increased scope and size of DS as a function. In most FinTech startups, DS will be a mission-critical function, especially as it pertains to credit scoring/underwriting, but it won’t necessarily be the case across all organizations. Generally speaking, when I reflect on how organizations should think about BI vs DS teams and what roles they should play in their operations, I often come back to the analogy from the medical industry…

BI is like a general practitioner in that they take the vitals of their patient (the business) in order to diagnose some illness (business problem), have some limited ways of treating said illness but ultimately can refer them to a specialist/surgeon if the problem is out of their technical scope; DS is the aforementioned specialist/surgeon, deep-diving into specific issues with life-saving precision. With respect to this analogy, I guess this makes Growth the medical lab scientist, conducting experimental research to develop treatments that can be greenlit for broader application.

With that final analogy, I hope that this post can help to clarify the purpose of a Growth team and articulate the rationale for having a Growth team in your respective organization.