Join top executives in San Francisco on July 11-12, to hear how leaders are integrating and optimizing AI investments for success. Learn More
For months, your team has been working at a breakneck pace to build and refine your product idea, with feedback from early adopters. It’s going well, but it’s been so. Much. Work. The team is in a dual state of exhaustion and excitement. User retention is growing. You’ve built a product that you’re sure people will love. Investors are taking notice and conversations are heading toward funding for the next stage. Success is on the horizon. It’s so close you can feel it.
If this sounds like you, then congratulations! You’ve overcome major hurdles to get to this point. For many, the moment you get that funding starts a new clock: new features, new hires, new users. The next stage of growth.
But have you really thought about what will happen when you double or triple your team size to meet growth demands? Do you have the right team now to support this growth? The right infrastructure? The right culture?
Can your company successfully scale?
Join us in San Francisco on July 11-12, where top executives will share how they have integrated and optimized AI investments for success and avoided common pitfalls.
For early stage startups, warning signs pop up along the way but are often ignored. We say things like “culture doesn’t drive acquisition,” “it’s not important today,” or “we’ll deal with it when we get there.”
I’ve watched startups churn their way through the transition between early-stage to growth stage. The ones that avoid long-term, critical missteps are the ones that start planning for their growth early and deliberately. They bet on their own success by prioritizing the work that will ensure the company is built to scale.
If you’ve reached this important inflection point in the growth of your startup, pay attention to these warning signs that you may not be ready to scale:
- Your backlog is growing exponentially with technical debt.
There is no easier way to tell that you will have long-term growth problems than a backlog of technical debt that you never seem to have time for. Technical debt is a normal, expected maintenance for any product and shouldn’t be put off on the back burner sprint after sprint. If you’re struggling with this problem, there’s likely two causes (sometimes both): You haven’t prioritized a sustainable process for maintaining this debt, or your product is unstable.
You can address this by talking to your team directly and getting their feedback on how they feel about technical debt. Is this a prioritization issue due to unrealistic deadlines for features development? Give them the space to prioritize. Does the team feel the product infrastructure may be reaching its breaking point? Do an evaluation on pros and cons of a refactor versus a rewrite.
- During growth, your startup is slow to release features.
If you’re slow to release features and improvements, you’ll frustrate teams and users alike. This is often a cultural problem of trying to solve too many things at once.
If you haven’t already, you should follow best practices of continuous deployment, including breaking features down into small, valuable increments and getting things out to be tested as soon as possible. Fully embrace agile and iterative development now, not later.
- Your data is untrustworthy.
Quantitative data isn’t useful early on. Suddenly, your product has enough users to make data useful. There is nothing more frustrating than not having confidence in the accuracy and integrity of the data coming out of your platform. This is a common problem for startups that don’t prioritize singular sources of truth on data and end up with conflicting, messy information that makes decision-making near impossible.
So, how can you avoid this? Invest early in a customer data platform (CDP) like Segment that helps you collect, clean, and activate your customer data. Trust me, you will thank me later.
- You’re not staying focused on the measurements that actually matter.
Yes, data is important when you start to scale. But it can also provide an overwhelming amount of information that makes it difficult to derive meaningful insights. This mountain of data ends up bogging down decision-making and distracting from what truly matters.
Be clear about what data to measure and at which stage of growth. For most growing startups, the most important metric for a successful product is retention. It’s the best measure to understand that you’ve built a product people find useful and will fall in love with. Other metrics are necessary to sell investors, but do not lose sight of the fact that you’re building a product for your users. Without them, you won’t have a product that scales.
- You have more marketers than engineers.
A surefire way to know you’re focused on the wrong metrics is that you have more marketers than engineers. Acquisition – getting new users to try your product – is much easier than getting them to stay and love your product. Hiring too many marketers early may increase your visibility, but it won’t help retention if your product can’t support the needs of its core adopters.
If you see this imbalance on your team, then consider reallocating your dollars into building a healthy product team that can consistently ship features and keep user retention high. Until your company reaches later stages of growth, ensure you have enough engineers that the team is comfortable before investing more into marketing.
- You don’t have a formal product strategy.
Nobody wants to take the time to write a formal product strategy. I get it. It takes time, it takes (sometimes frustrating levels of) collaboration. And the very nature of startups is that they pivot, making the work of creating a strategy feel pointless and futile at times. But I promise, it’s not. Smart product companies do this, even if they aren’t talking about it publicly.
Have the diligence and fortitude during this growth transition to document your startup’s strategy and ensure your team understands it, can interrogate it and build from it.
- Your product team isn’t working cross-functionally.
Many product startup organizational structures are built from resource or financial scarcity. Because of this, they build a product culture that is either highly engineering-centric or highly design-centric. Product management tends to be filled by the owner of the company, if it’s considered at all. In early stages this can work. But as the company grows, so does the need for maturity in the product team makeup.
To improve cross-functional collaboration, reorient your product team leadership to consist of a product manager, an engineering lead, and a design lead (aka “the trio”). Each should be collaborating equally on decisions that ensure the technical needs, business needs and user needs are all considered as the product and its processes grow in maturity.
- You’re not writing things down.
If your processes, culture and ways of working are all living in the spirit of the small team you currently work with, scaling will be painful. This works when a team is small, since people understand norms due to the very nature of how closely they work together. But when startup growth happens and departments naturally silo, this is impossible to maintain. Deliberate growth includes deliberate documentation of what matters to the company. Without it, cracks will form in the culture and become a much bigger problem down the road.
“Just enough process” and “just enough documentation” are my two favorite mottos. Start writing down the most important things you want people to be accountable for: your values, processes, strategies. Over time, encourage team members to do the same.
- You don’t have a plan for your culture or organizational evolution.
Once investor dollars hit, it’s going to be the second stage of high-paced work to hire up a team — sometimes two or three times its current size. If you don’t do this with a plan in mind, it can end up costing you exponentially in the long run. Culture can change drastically and cause conflicts among old and new employees. The team you currently have can feel alienated and frustrated with this growth. Leadership involvement needs to change to support a large company versus the small, tight-knit group it once was. You can either drive this culture intentionally or you can let it happen to you.
Sit down with your current team and map out the future stage of the company. Talk about the culture. What do you want to keep? What do you want to change? How will roles change? Who will take on leadership roles as company ownership moves into more formal C-level leadership? Address the people in your startups’ excitement, fears and other emotions around this growth. Build a plan that everyone feels invested in.
When people talk about startups, they often focus more on the challenges early-stage startups face — building the MVP, achieving product market fit, and securing investor funding. Understandable, right? Without passing this stage, there is no future, so there is good reason to stay focused on the here and now.
But this tunnel vision can make the transition from seed to scale that much more painful and put even the greatest ideas at risk of failure. Data from the Small Business Administration shows that the failure rate of startups is around 90%, with 21.5% of startups failing in the first year, 30% in the second year, 50% in the fifth year, and 70% in their 10th year.
Startups face a higher risk of failure as they grow. Don’t let short-sighted focus cause your team to lose sight of the long-term vision: a sustainable product and company that continues to thrive well beyond MVP.
Summer Lamson is the chief services officer at DockYard, a digital product consultancy focused on helping innovative companies scale through the nexus of technology and design.
Welcome to the VentureBeat community!
DataDecisionMakers is where experts, including the technical people doing data work, can share data-related insights and innovation.
If you want to read about cutting-edge ideas and up-to-date information, best practices, and the future of data and data tech, join us at DataDecisionMakers.
You might even consider contributing an article of your own!