There are a few ways to think about the cost of growth experimentation. Are we focused on the actual dollar cost of running the experiments? The cost to the company in terms of lost revenue opportunity for ineffectual experiments?
Actual Dollar Cost
The number of experiments a company undertakes to drive customer growth varies widely based on factors like its stage, size, and selling motion. With that said, based on our research and in conversations with growth leaders, we discovered that, for most companies, ~3 experiments per month targeting different areas related to growth seems to be inline with what they can reasonably execute on.
From a staffing perspective, you have:
| Contributor | Compensation |
| Growth Manager | $150,000 |
| Developer | $120,000 |
| Designer | $110,000 |
| Data Scientist | $140,000 |
| Project Manager | $115,000 |
| Product Manager | $140,000 |
*This calculation is not factoring in the fully loaded cost of an employee. For salary data, we used levels.fyi, ziprecruiter.com, 6figr.com, and comparably.com.
Not all roles mentioned will be exclusively dedicated to growth. For example, Development will often share the responsibility across the team, but the overall volume of work is the equivalent of 1+ full-time employee.
From a tools perspective, Growth teams are spending approximately $30,0000 for solutions that support their team. This figure encompasses their portion of shared spending on Sales or Product solutions that they also use. This amount is much higher for larger organizations, and significantly less for start-ups.
So what’s the cost associated with growth? For a full team, it can range from $250,000/year for smaller organizations to $535,000/year for larger organizations.
Lost Opportunity
We can offer a way for you to calculate lost opportunity for your organization, but the approach significantly hinges on the experiment’s goal and its intended impact.
If your company is aiming to increase paid conversion and it takes 7 experiments to get there, you’d be averaging around 147 days to hit that. Now determine the value of your paid conversion increase at the end of that 147 days. During that time you’ve had neutral, negative, and potentially some positive experiments. We’ll assume net neutral until the final experiment is started around day 126.
Your lost opportunity is the value of that increase in paid conversion across those 126 days, tailing into the 147 days. That’s a significant period of time and a lot of lost opportunity.
Get to Value Faster
What would it mean to your organization if you could get to value faster? By that, we mean: what if you knew your target and could run fewer experiments, requiring less resources?
The cost of experimentation would be lower because you’d need to run fewer experiments to see success. The impact to the business would be significant because you’d see results far sooner. Maybe sign-ups grow faster, or paid conversion takes off, etc.
Or, if you and your company are really bold, you double down. You keep the resource and experiment investment the same, running the same number of experiments, but you get better results, so you outperform your goals.
Scenario modeling can help make your experimentation more efficient and effective. You’ll be able to demand less time from other teams while also hitting your targets. Dacture offers no-code options for scenario modeling. Just bring your data, make a few quick selections, and you start to get steered in the right direction. Interested in learning more about scenario modeling and how it can help your organization? Reach out or schedule some time with us.