* Rodrigo Bernardinelli is CEO and co-founder of Digibee
Behind the aura of startup success there is a frequent pain: scaling your company globally. Businesses who can solve this challenge are invited to walk on the red carpet of the investment rounds known as series A, B and C.
Why is global expansion so challenging, even for startups that won in local markets? Developing competitive solutions doesn’t always work across geographies, and success in one region doesn’t mean your business will have success in another.
The challenge is to format a replicable model in any geography, with the budget and revenue strategy very well defined. The investor’s perspective is focused on the return of the investment.
In an economy that is becoming more digital and global every day, finding new opportunities to generate revenue is essential. And one of the main challenges is to prove that the value seen by current customers has the potential to scale, regardless of differences in geography, industry and regulation.
Machines to help scale
It is no longer enough to show current revenue to prove success to investors. Investors expect that revenue is growing, predictable and can be modeled in ways that are easily understood. Investment teams continue to become more and more diverse, and presenting them with revenue models that are not easily understood can alienate investors.
To help with this challenge, our team has built what we call “Scale Machines”. This model is different because it focuses on creating models that are replicable and can be scaled autonomously, regardless of geography or industry.
This model has three principles to sustain the growth at scale: The “Product Machine”, “Revenue Machine” and “People Machine”. Using these models and machines, investors can clearly see how their monetary contributions help grow the business and what type of financial return the investment might deliver.
The first machine of this model, the “Product Machine”, is the strategy which helps product teams focus on building the right features for potential and existing customers. At Digibee, we use the “team topologies” methodology, based on the book “Team Topologies: Organizing Business and Technology Teams for Fast Flow”, by Manuel Pais and Matthew Skelton.
This method manages the interactions between teams by dividing them into small groups which focus on solving specific challenges. As products and companies need to scale, each time new products or initiatives need to be worked on, a new group can be formed, without an impact to the structures already in place.
As the platform expands, new functionalities are added. With this methodology, creating new teams can be done easily, even if the resources are physically separated and on different continents. This allows them to expand the product lines without losing the quality or culture.
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Exponential revenue gains
The second machine, called “Revenue Machine” is powered by prospective clients, generating new business to the company. In this part of the machine, multidisciplinary teams called pods are created. Each pod is made of professionals from demand generation, prospecting, sales and customer success.
The advantage of this structure is the replicability. You can have a pod focused on markets in the United States and others for markets in Brazil. Teams can also easily scale into new verticals, structuring one pod, for example, to focus on the pharmaceutical industry.
The beauty of this model is the possibility to replicate these structures in any geography or industry, with clear understanding of how much revenue each will generate and at what cost. The result gives visibility to the business and, consequently, to the investors.
The third machine in the model is focused on people. The “People Machine” has the potential to hire candidates and build company culture.
The goal here is to hire and develop people that can lead the processes defined in each of the machines. This process helps define what the size and structure of the team should be, based on size, making it easy to scale into new markets.
Autonomy and governance
As we created this model, we realized that its autonomous nature lends itself towards teams that can work independently. All of the processes that are necessary for each machine have already been integrated. Each new team within the respective machine helps build strong alignment and integration across the business.
Another benefit is the creation of a process library. We have encouraged everything to be documented, so when people join the team it is easier to understand the process and what is expected. These process libraries allow for team members to share feedback in order to promote and improve each machine.
If we are able to improve even 1% everyday, by the end of the year teams have the potential to triple their efficiency. It only takes paying attention to small adjustments that, as they are added, make the machine even more efficient. A watchful look is capable of identifying when even the smallest of changes put the machine off track, thus giving us the ability to make changes rapidly.
To complete the strategy, we track a powerful set of metrics called KPIs (Key Performance Indicators). These KPIs are essential to show what is working and what needs to be improved. With the right rhythm, all three machines can work under the sacred cover of governance.
The three machines guarantee the autonomy of processes, allow teams to develop new ideas and can help attract new talent. These well oiled machines have allowed us to bravely enter new markets and scale revenue.
