Startups implement new growth strategies by messing with sales cycles, A/B testing, moving roles around and deploying operational tools. Strategies are led by an individual that’s proven in past startups to have found something which stuck. But vague circumstances immediately test that person.
Is the marketing team spending too many hours on content rather than conversion? Is the sales team owning too much of the cycle and should parcel some duties off to pre-sales? Is it worth switching from our current CRM for more functionality? Should we just hire recently laid off monkeys for forced labor?
More often than not, pre- and post-sales/marketing/customer service ops are disproportionate to what is needed to grow at maximum efficiency and “growth managers” find themselves at a loss for specifying what change to implement. Early-stage founders like to afflict managers, even Chief Revenue Officers, with their own ideas. A lack of experience will cause some managers (marketing, sales, or otherwise) to strategize by mimicking companies with similar products or, even worse, to scrutinize ops by which department can handle another hire/resource/strategy. Lesser known: the best middle and upper growth managers rely heavily on math to solve for team size, KPIs and even daily duties. We all generally follow things like don’t bring in a VP of sales until 80% of your reps are hitting a quota that is set at 3x their on-target earnings, but here we’ll introduce examples that can alleviate pain.
What should this month’s marketing spend be?
Probable answer: C (x)=(conversion rate*cost per lead*market cap variable)/(avg sale cycle length in days*net revenue generated per average sale)
Some linear marketing equations, like that one, can be so accurate it feels dehumanizing to have such reliable data. How about basic derivative calculus to solve for personnel spend…
Our startup has max budget $250,000 OTE to hire our first two salespeople. The average sale nets $3,200 and salary ranges are known to produce a specified sales range output listed in the function below. If x is the cost of the sales hires, in dollars, is given by, P(x)=−8x²+3200x−90,000
How many thousands of dollars should we spend in order to maximize our sales with the two hires? Answer: 230 or $230,000.
As this will likely be part 1 of a short series for introducing front-end functions, a great place to begin is with your own conversion rates. Arguably the most crucial growth measurement for any department in the company, conversion rates single-handedly serve as translator to marketing and sales efforts/relations with more universality than a Swiss Army knife. Let’s specifically look at Sales Conversion Rates (once they’ve become a lead in the sales funnel) against Net Revenue of first year. And let’s also overlay that with Customer Lifetime Value in the y-axis. Below is a chart showing a series of Split Tests performed by a SaaS company seeking to uncover their optimal conversion rate within their first year of structuring a sales cycle. For each month, the one-person team tried new ways of closing business. In the second half of the year, the person brings someone from the product team to help with demos and onboarding; increasing the lifetime value of the customer by being a technical contact early on. The reason this example is long-winded and having multiple Split Tests is because this is how early-stage startups operate. How often a lead converts to a sale is not indicative of maximum revenue and even maximum net revenue is not synonymous with optimal conversion rate. Assuming the product remained the same throughout, can you find their sweet spot? In the second part of this series, we will get into how to extract a function from the set below and apply to other areas, e.g. sales team structure.
All strategies should be led by Key Performance Indicators (KPI). KPIs serve as the foundation for the sales/marketing/biz dev/CS team to focus on what matters. In return for sparking the imagination of anyone reading this — I simply ask that you stop putting “growth hacker” in your bios.
