Without customers, there can be no business. So how do you drive new customers to your startup and keep existing customers engaged? The answer is simple: Growth marketing.
As a growth marketer who has honed this craft for the past decade, I’ve been exposed to countless courses, and I can confidently attest that doing the work is the best way to learn the skills to excel in this profession.
I am not saying you need to immediately join a Series A startup or land a growth marketing role at a large corporation. Instead, I have broken down how you can teach yourself growth marketing in five easy steps:
Setting up a landing page.
Launching a paid acquisition channel.
Booting up an email marketing campaign.
A/B test growth experimentation.
Deciding which metrics matter most for your startup.
In this last part of my five-part series, we’ll cover how to determine which metrics matter for your startup. For the entirety of this series, we will assume we are working on a direct-to-consumer (DTC) athletic supplement brand.
It’s very easy to get lost if you assume upper-funnel metrics are the most crucial for your startup. Don’t fall into this trap.
First, I’ll discuss what metrics mattered the most while I was with Uber and Coinbase, an example of metric analysis, and why it’s important to pivot metrics when necessary.
Uber and Coinbase
Many people will assume that the most important metrics for growth teams at companies like Uber and Coinbase will be new riders and traders. They would be wrong. While those metrics do matter, when I was with both companies, we focused primarily on much deeper metrics that could tell us how valuable various users were.
On the rider growth team at Uber, we measured the performance of each growth channel individually and segmented by city. When we looked at each growth channel and city combination, our guiding metrics were ROAS (return on ad spend) and pLTV (predictive lifetime value). While there were many calculations happening in the background to compute these metrics, they helped us understand how much revenue each rider would ultimately bring to the company.
Similarly, at Coinbase, we weren’t only concerned with how much it cost to acquire a trader, but instead, on the quality of each trader we acquired. The ROAS was calculated by using a rolling average of how much volume each user was trading based on the channel they were acquired from.
Acquisition metrics
It’s very easy to get lost if you assume upper-funnel metrics are the most crucial for your startup. Don’t fall into this trap.
Instead, think about the ideal user of your startup. For our athletic supplement brand, it would be far from ideal if consumers only purchased a one-month initial supply and then never ordered again. At Postmates, we called users “whales” when they consistently ordered a certain number of times every month. We would prioritize acquiring users from channels that net us the highest quality users and as many “whales” as possible.
Here’s a simple exercise you can do to understand which acquisition channel is best for your startup:
Teach yourself growth marketing: Which metrics really matter? by Ram Iyer originally published on TechCrunch
Without customers, there can be no business. So how do you drive new customers to your startup and keep existing customers engaged? The answer is simple: Growth marketing.
As a growth marketer who has honed this craft for the past decade, I’ve been exposed to countless courses, and I can confidently attest that doing the work is the best way to learn the skills to excel in this profession.
I am not saying you need to immediately join a Series A startup or land a growth marketing role at a large corporation. Instead, I have broken down how you can teach yourself growth marketing in five easy steps:
Setting up a landing page.
Launching a paid acquisition channel.
Booting up an email marketing campaign.
A/B test growth experimentation.
Deciding which metrics matter most for your startup.
In this last part of my five-part series, we’ll cover how to determine which metrics matter for your startup. For the entirety of this series, we will assume we are working on a direct-to-consumer (DTC) athletic supplement brand.
First, I’ll discuss what metrics mattered the most while I was with Uber and Coinbase, an example of metric analysis, and why it’s important to pivot metrics when necessary.
Uber and Coinbase
Many people will assume that the most important metrics for growth teams at companies like Uber and Coinbase will be new riders and traders. They would be wrong. While those metrics do matter, when I was with both companies, we focused primarily on much deeper metrics that could tell us how valuable various users were.
On the rider growth team at Uber, we measured the performance of each growth channel individually and segmented by city. When we looked at each growth channel and city combination, our guiding metrics were ROAS (return on ad spend) and pLTV (predictive lifetime value). While there were many calculations happening in the background to compute these metrics, they helped us understand how much revenue each rider would ultimately bring to the company.
Similarly, at Coinbase, we weren’t only concerned with how much it cost to acquire a trader, but instead, on the quality of each trader we acquired. The ROAS was calculated by using a rolling average of how much volume each user was trading based on the channel they were acquired from.
Acquisition metrics
It’s very easy to get lost if you assume upper-funnel metrics are the most crucial for your startup. Don’t fall into this trap.
Instead, think about the ideal user of your startup. For our athletic supplement brand, it would be far from ideal if consumers only purchased a one-month initial supply and then never ordered again. At Postmates, we called users “whales” when they consistently ordered a certain number of times every month. We would prioritize acquiring users from channels that net us the highest quality users and as many “whales” as possible.
Here’s a simple exercise you can do to understand which acquisition channel is best for your startup: