Myth#6: Growth Hacking is less analysis more action
By: Can Taner
Tags: Growth Mindset, Growth, Entrepreneurship, Startup, Growth Hacking

If growth hacking is anything, it’s exactly the opposite of the sentence. In fact, you are meant to do more analysis so you can take more precise actions which in return will save you money and time. But the catchy name and how it’s been promoted can deceive you towards otherwise.

The Stereotype

Think about what is the first thing you picture as soon as you see or hear the words “Growth Hacking”. Usually, it's a rocket going-up towards the sky (🚀) or an actual hacker-looking guy emerging from the darkness and he has his laptop to save the day, or a marketer looking at -unrealistically- perfect data trends with a happy face. It would be a priceless fantasy to someone to step into the room and look at your dashboard to point out one little change to fix everything. Like forgetting a comma in your code that was preventing your code to compile and you’ve been going crazy to fix it for hours. I wish that was the case. I would walk like a rock-star in slow motion. sadly, it’s not.

Why Analyse in detail?

Most of the time what we do as growth professionals are to analyse the data endlessly and tirelessly. In fact, the entire process should be built on the lean model where we analyse the data which may be -and would be- spread across analytics metrics, heatmaps, surveys, screen recordings, user research, competitor and market data, word clouds, patterns, support cases and many more… then we create experiments to test. Guess what; then we repeat. Maybe not the entire list at every experiment but those are directly related to the experiment. This is a key point in this article so I will stress it as much as I can: “those related” is not singular, never. It’s a rare occasion that any action you take on your product has a singular effect. If you reduce the bounce rate you ought to increase sign-ups. if you don’t probably your experiment caused another problem to unearth or worse create a new one! Thus you need to check your key metrics. By check I mean analyse your data of course.

And please make sure you take the median. One quick joke about this:

Why did the statistician drown in the lake?

Because he took the average depth.

Biases

I guess we also need to talk about how we define analysing the data. It’s very tempting to make an induction especially when we see what we want to see. Data is golden, data is science but unfortunately unless analysed right, it’s sometimes easier to bend the results towards what you want it to say. The cure is to put your biases aside by understanding them and read counter indicators to ensure you are reading it properly.

Watch out for overengineering it!

Studying both sides of the story is good. Like being a neutral party in an argument that you are trying to help resolve. Going deep and wide is great. However, you also need to know what is enough. It’s another risk in data analysis that I’ve seen is the never-ending analysis problem aka analysis paralysis. Defining the scope of the work, what part of your product it can affect, removing outliers in your data are some of the things you can do to reduce this.

I’m still a learner in this field even though I’ve been into analysis all my life (it has to be part of your personality I guess) and I come from an analysis-oriented professional background. But my best suggestion would be to note down your current/targeted metrics before you run the experiment. Also, check if there is any metric you don’t want to change or effect.

Life will force you to be bold at times

Action is what humankind think creates the reaction. It’s what you, your colleagues, your boss, or your customer desires. But focusing on success and what is desired can allure you to take action without understanding the consequences. We’ve all taken bold actions no matter how much we love to analyse the data, the situation, the risks. Sometimes you end up in a situation where you have to use your intuitive decision-making process with educated guesses. But be warned; more than often this kind of decision-making that ends with a bold action rarely has a positive outcome without a downside. Especially in the long run. If you do your analysis (and avoid over-engineering at the same time ), you have a better chance of shooting in the right direction. But if you want to go blind-folded, make sure you watch out for your own foot.