User acquisition KPI #1

Date

August 18, 2023

In this series of posts, I'll share some key KPIs (key performance indicators) about user acquisitions. As we already know, not all customers are created equal, so we've invested a significant amount of time and effort to identify the right KPIs. These indicators help us understand the quality of new customers and guide our strategy and budget towards optimal performance. I'll skip the more common ones, such as device, landing page, marketing channel, 1-month cohorts, etc., to focus on those that may not be as popular but are still important.

Due to confidentiality, I won't share our specific numbers, but I'll offer some aggregated-level KPIs that I believe are valuable for various consumer products.

KPI:

Share of user acquisitions generated through word of mouth (WOM)

Definition:

New users who place their first order on our platform and, when asked, “how did you learn about us”, respond with, “a friend told me about your platform.”

To enable this, by the time a new user performs his first order he will see a survey “how did you learn about us” with a list of options that correlate to communication channels, in a randomized way.

Why it matters:

WOM acquisitions are pivotal for several reasons:

When a user recommends our platform to someone else, it indicates they had a positive experience. Satisfied users are the cornerstone of every thriving company. They often serve as our most effective promoters. Notably, WOM has consistently been among our top three feedback responses since our business began.
This kind of recommendation builds trust and positivity for new users. Starting with a positive outlook is crucial because it enhances the new user's likelihood to complete an order. Some users even mentioned having friends guide them through their first order, ensuring they understand the process. It’s like having a strong onboarding system in place.
Organic referrals from existing users often come at no or minimal cost. A lower Customer Acquisition Cost (CAC) means more resources can be allocated elsewhere, such as experimentation or further growth.

Challenges:

Two main challenges come with this metric:

The metric relies on user feedback, which can be prone to type I and type II errors, especially with limited sample sizes. A user's first order is a unique event, and considering we've grown significantly over the years, the proportion of new users to existing ones is smaller.
Optimizing this feedback isn't straightforward, particularly without incentives. We can't predict when an existing user will recommend our platform to a potential new user. While we always aim to provide the best user experience, several factors might encourage a user to recommend our service. These can range from unique offerings to excellent customer support or even habit formation. However, it's challenging to determine which aspect most influences a user's decision to recommend us.

Relevant posts: