While building a startup may seem easier than ever because of the assortment of tools available, the available statistics on being successful, still do not favor founders.
In the past year, I’ve had the privilege of co-founding Sales Kiwi, a virtual sales staffing and marketing service company, from ground zero to $1M+ Annual Recurring Revenue (ARR) and 25 employees.
What separates the startups that succeed over those who fail? While I don’t have a crystal ball to predict everyone’s futures, I do have a roller coaster of stories and experience gained from my work scaling our company. I’m here to share my top five growth lessons, with the aim to help you avoid making the same mistakes that we made early on.
My first lesson can seem a bit obvious, but not spreading oneself too thin early on is imperative. In the area of growth specifically, I never tested more than two paid channels at a time, which is how I was ultimately able to unlock acquisition for my team. This applies for all forms of growth, so if you’re trying to unlock lifecycle marketing, don’t also put efforts into unlocking four paid channels at the same time. For my startups, instead of going all-out and trying every single paid acquisition channel at once, I tested a maximum of two channels at a time. This gave me the ability to optimize and experiment with the channels that I was immediately working on, rather than taking the approach of throwing everything at the wall and seeing what stuck. Once we had found success on a specific channel, I’d follow the same principles with other forms of growth marketing, such as lifecycle, referrals, or affiliates.
In contrast, you also need to ensure you don’t spend excessive time focusing on one channel that isn’t showing any viability. A quick back-of-the-envelope method to assess whether you may find success on a channel, or not, is if your Customer Acquisition Cost (CAC) is 5x where it should be, or you’re only seeing sub-5% of your conversions coming from the growth pillar after a few weeks of testing. There are a few exceptions to this such as content or SEO which typically have longer timelines before you encounter success.
It’s not easy to have perfect reporting. This is especially true for startups, which frequently demand that staff roll up their sleeves and prepare for long workdays. During this early period, sudden fires will often arise requiring immediate action. One of the biggest shortcomings at my startup was attempting to perfect our tracking with complex dashboards on our Customer Relationship Management (CRM) software. As we scaled rapidly, we kept trying to create new dashboards to accommodate the new data points we wanted to measure, which was ultimately a big mistake.
Today, I am a firm believer that perfection can either make or break startups early on, and the first $1M ARR does not require expensive tools for reporting. Instead, one should leverage GSheets to create reports for your growth funnel, retention, and any other tracking that you’re looking to measure. There are also many resources, such as GooDocs, which provide free templates for revenue tracking or project management that can be customized to your startup. It does not make sense to spend time reinventing the wheel with fancy frameworks when you can easily download a free template.
One of the most important contributors to our early alerting of problems and other issues, as well as where we could improve our performance, was our hired performance consultant. When you scale rapidly, it’s very hard to keep an “in-the-weeds” pulse of every department. There simply isn’t enough time in the average workday. While at Postmates, I learned this firsthand as our CEO picked a problem-area department to be involved with for ‘sprints’ of typically four weeks at a time. He would attend all their meetings and strategy sessions for the entire period so that he could be deeply involved in the work.
Unfortunately, as you might have guessed this strategy is not sustainable for CEOs over the long-term. Instead, we later hired a performance consultant who was tasked with meeting every department weekly, tracking performance data, and reporting back to leadership. Nearly every week, we had something that was highlighted where we could improve our performance. Hiring a consultant instead of having senior management get involved in micromanaging individual departments is genuinely the difference between making fifty positive changes in a year, or only five.
Performance consultants are typically well-versed with data and have previous job titles such as “Business Analyst” or “Operations Manager.” What’s most important is to vet that they are business-oriented and do not only focus on locating problems, but also possess a solutions-oriented mindset. Such consultants can either be poached from recruiting sites like LinkedIn, or a simple job posting for one of these roles will likely yield you multiple candidates who would be best suited for this specific role.
As a growth marketing nerd and our CMO, this lesson resonates most with me, because it falls under my direct responsibility in scaling our startup. The first few months of the startup, we didn’t care who we spoke with, and were in what I call GGG or a “grow-grow-grow” mode. It’s okay to be in this mode, until product-market-fit and a nice bucket of customers are found. But as you try to scale an efficient machine, it becomes quickly evident how tough it is to fight churn operationally with the wrong customers. After finding our ideal customers, we implemented questions in our lead forms and top of funnel acquisition, to filter great types of customers versus high-churning customers. The faster you identify your ideal customer segment, the faster you’ll increase your retention and Lifetime Value (LTV).
By taking your time, you can also identify those qualities that separate great customers from the chaff. Is it the industry they’re currently in, their monthly revenue, geography, or age? Once you identify the common features of your most profitable customers, next you should add questions to your lead forms that will weed out unlikely buyers. You should also alter the creative assets that contextually target your ideal customer. For example, if you find that the male segment is a higher performant for your product, create assets that feature men using your product or service!
I found that the most efficient use of our team’s time was when we had meetings focused on one specific topic (i.e., customer retention, lead quality, <insert project name here>, etc). Instead of a high-level weekly recap meeting, narrowing it down to a startup problem area helps ensure the team is centered around solving one issue. I typically saved these meetings for the most pressing issues in the startup – limiting to a maximum of three per week. It defeats the purpose if you have a recurring focus meeting for every problem your startup is facing, while also diminishing the importance of the meeting. E.g., At Sales Kiwi, I added a meeting with our sales director to dig into lead quality each week and to better understand how we could improve the top of the funnel by looking at specific leads, how demos were going, and other various “in the weeds” type items.
The most effective method for identifying those areas that need specific follow-up meetings will likely come in your general meetings. If there are problem areas that are constantly arising, this is likely a sign that a dedicated meeting needs to be held on that specific problem. You must be curious and proactive in meetings, constantly probing those issues that could be problems hiding beneath the surface. If you’re not constantly asking “why” or “how” enough, real problems will linger and grow over time.
There are certainly many more lessons I’ve learned in scaling a startup from the ground up, but these were the five lessons I wish I knew when we first started. If you implement even one of these lessons into the build of your company, I can guarantee that you’ll be ahead of many others who are starting out.
I’ll walk you through when to start measuring diminishing returns and how to use a simple regression analysis to find optimal spend levels.