Having spent time in the trenches with many startups, I’ve been fortunate enough to see why many growth marketing engines aren’t performing well. I say I’m lucky because the issues I’ve seen have taught me so much about what makes a well-oiled and polished growth marketing engine work on all cylinders. My experience at Postmates taught me more through mistakes than triumphs, and I learned how to properly size a growth engine while guiding us to an exit.
A common thread of errors connects most startups that are trying their hand at growth marketing. Some common mistakes include performance metrics that aren’t properly measured, product and growth teams working in silos, poor testing speed, and an inability to factor in the marketing funnel as a whole.
That’s not to say that there aren’t unique issues every time you start up. I’m just saying that there are a few that are ubiquitous.
Low test speed
The day is far into the future when you’ll be able to flip a switch for paid acquisition, lifecycle, social media, and content, and everything to run automatically. Until that day arrives, the need to continue testing is paramount.
It’s simple: test more and the results will happen sooner. While the concept is simple, you will need an appropriate testing framework, which defines the number and type of weekly tests that are deployed. An example of a weekly test plan might look like this:
- Acquisition for a fee: two creative concepts x three copy iterations = six creative elements.
- Lifecycle: two copy variations x five emails = 10 email variations.
Create a test framework and, most importantly, stick to it. The results will follow.
Use of incorrect measurements
When measuring the success of a campaign, whether on social media for a paid acquisition or with a series of lifecycle retention, it’s critical to have the correct metrics before you act – that’s is the fundamental backbone of any growth marketing stack.
But what if your performance metrics are inaccurate? And if they are, why? I’ve listed the top three reasons the metrics aren’t correct below:
- Source of attribution.
- Loss of attribution.