After spending time in the trenches with a number of companies, I have been fortunate enough to witness firsthand why many growth-marketing engines fail. I believe I am lucky because the problems I have seen have taught me a lot about what makes a well-oiled, polished growth marketing engine run smoothly.
My time at Postmates taught me more from my failures than from my victories, and I learned how to scale a growth engine effectively while marching us toward the exit.
Most startups that try their hand at growth marketing make a similar set of blunders. Performance metrics that are not appropriately measured, product and growth teams operating in silos, insufficient testing velocity, and failing to evaluate the complete marketing funnel are all common problems.
This is not to argue that each company does not have its own set of issues. I am only pointing out that there are select handfuls that are pervasive. It will be a long time before you can turn on a switch and have paid acquisition; lifecycle management, social media, and content management all operate automatically. Until that day comes, it is critical to continue testing.
It is as easy as that: test more and the results will appear sooner. While the concept is straightforward, you will need a robust testing framework to implement it — one that specifies the number and type of weekly tests to run. A typical weekly test schedule would look like this:
Paid acquisition, two creative concepts x three versions of the copy equals six creative assets. Two copy variants multiplied by five emails equals ten email variations. Create a testing framework and, above all, stick to it. The outcomes will reveal later.
It is critical to have the relevant metrics before taking action when assessing the effectiveness of a campaign, whether on social media for sponsored acquisition or with a retention series on lifecycle – this is the core pillar of any growth-marketing stack. However if your performance indicators off? Why, if they are, are they doing so? The top three causes for not having accurate measurements stated below.