A Guide to Customer Metrics: What to Track and Why

Blog Banner

If you’ve worked at any company, whether product-focused or big tech, finance or retail, you’ve encountered metrics. Some places have standard metrics and others have custom derived KPIs. Whatever the situation, it’s no secret that understanding and leveraging metrics provides an edge.

In an increasingly competitive environment where a company must grow more than 90% year-over-year to be in the top 25% of its peers, it’s important to have an edge. Companies like Netflix and Facebook have used metrics and solid experimentation practices to help guide their strategic decisions and create massive gaps between them and their competitors.

But there are a lot of metrics to keep track of, and it can seem overwhelming. I didn’t even know what ARR meant until I moved to the Bay Area. I’m not kidding. I had to embarrassingly raise my hand in a meeting once to ask what the term meant because it was being thrown around as if it were common knowledge.

Now, to be clear, metrics are only part of the story. A lot of context is needed to make actionable decisions. But metrics are important for critical things like alerting teams of issues quickly. They also help businesses make better decisions and better understand which features to focus on.Customer metrics in particular are useful because they give businesses immediate insight into potentially massive changes. Facebook is so serious about metrics that one of their technical interview questions is “if you saw metric X change, what would you do?” It’s because even metrics as simple as daily active users can help companies understand how their overall service is being perceived as they make changes. However, metrics go from simple to complex quickly as companies seek to gain even more context and dig into the actual root cause of the changes exposed by more simplified metrics.

In this article, I’ll outline why companies utilize metrics and distill some of the classic metrics that are important for everyone to understand.

Why customer metrics are important

At the highest level, metrics give directors and the C-suite visibility into their company's health. They help product managers gain insight into the customer journey - where customers are spending their time, what functionality is being used, where products may be confusing or have bugs, etc. On the growth and marketing side, metrics play a huge role in terms of how companies decide where to invest their advertising dollars. Every team in a company benefits from utilizing metrics. Metrics show teams where workflows can be improved, how to better invest their dollars, and which features are most important to focus on.

You could argue that reliance on and respect for metrics is a matter of survival in this era. As I mentioned before, the most sophisticated companies are crushing it because of their capabilities around metrics. Anytime a small change in product occurs, these companies have KPIs they are paying attention to, and they experiment across different segments to see if certain metrics change. This rapid experimentation process leads to knowing exactly what drives growth.

The next question becomes, where do you start? Which KPIs or metrics are right for your team to track? As I said earlier, metrics go from simple to complex quickly, but everyone should have an understanding of these classic metrics that almost every company relies on.

Key metrics you need to understand

Customer lifetime value (CLV) - Customer lifetime value is the total revenue you earn from a customer over time. This metric isn’t just for online services and eCommerce platforms. Many companies utilize this metric to understand how much they will likely make in the long run per customer. It’s a great metric because it can help size up customer satisfaction, customer loyalty, and the viability of a brand.

Repeat purchase rate - As per most metrics, what they are is in the name. In this case, the repeat purchase rate calculates the percentage of a company's existing customers who come back for another purchase. This is more pertinent to companies who sell products vs. have a SaaS model

It’s also important to note ethos or repeat purchase rates will always range from 0% to 100%, with the higher the number the better.

Customer retention rate - Customer retention rate designates the percentage of customers the company has retained over a given period. Retention rate is the inverse of churn rate, which shows the percentage of customers a company has lost over a specific timeframe. The importance of retention rate as a metric varies depending on the industry. For example, customer retention is crucial for businesses providing services or selling software goods because it directly affects the profitability of the business.

Customer Churn rate - Churn is one of the most important metrics a company needs to watch. Churn is the rate at which customers stop doing business with a company over a given period of time. Churn may also apply to the number of subscribers who cancel or don't renew a subscription.

This is important because let’s say your company is adding new customers, and its daily active users are growing at a rapid rate, but your churn is 30% per month. Well then you have a giant hole in your bucket, and you’re likely to spend a lot of money on new customer acquisition only to have them leave.

Customer acquisition cost (CAC) - CAC is the cost of convincing a single potential customer to buy a product or service, adding a count of one to the total number of customers

Monthly Recurring Revenue (MRR) and Annual Recurring Revenue (ARR) - If you’re a SaaS company, then you have to know what MRR and ARR mean. Monthly recurring revenue and annual recurring revenue are generally numbers that are required when talking to VCs about funding.

But wait, there’s more! There are a plethora more metrics and KPIs companies rely on to make decisions on their products. Although they can be high-level, metrics like DAU, MAU, HAU and all the other versions of these metrics can tell a manager a lot about their products. So let’s dive into these:

TAM - TAM Simply refers to the total addressable market (or how big is your market) in revenue form. This is often used when referencing a new product or service someone wants to sell. A VC or investor might ask the question, “What is the TAM” because that tells them how much money they could hypothetically make. In Theory, there are ways to grow TAM, but in general you can view it as the current ceiling for a product.

HAU, DAU, and MAU - Hourly, daily, and monthly active users are very popular metrics. They are also very high-level. While they tell very little in terms of context, they help quickly detect if things are going wrong. Imagine suddenly you see your daily or hourly active users drive far below the expected rate. What could be the cause? Did you push code that suddenly made the site inaccessible in certain regions or for certain operating systems? Well, you’re going to have to dig to find this out, but from an operational perspective, these active user metrics are immediately valuable.

As referenced above, Facebook often uses metrics such as these in their interviews to assess how an engineer or data scientist may drill into issues such as sudden drops in HAU. All that said, these metrics can also be very high level and can mask other issues such as high churn or poor engagement.

Engagement metrics

Finally, let's talk about engagement. Engagement metrics fall into two big buckets:

  • Passive engagement - such as time spent on the site or time spent viewing a post on Facebook or instagram. Basically, passive engagement is any form of engagement that doesn’t result in a direct action that can be counted.
  • Active engagement - such as likes, comments or other actions that can easily be counted and require the end user to actually do something.

Engagement metrics are tricky. For example, you want to see user engagement increase, but only for the right reasons. If the reason your user engagment metrics increase is because your users are struggling to figure out how to use a new feature, that's not good.

If the users are engaging and finding more value, then great. You want to see an increase in time spent on your product as this generally equates to an increased stickiness. At the end of the day, engagement does remain one of the hallmarks of a solid product, but usually you will need a combination of a few metrics to ensure you’re not over indexing on one strategy that could be causing unforeseen problems.

Improving the customer experience with metrics

Metrics remain an important method to get a high-level view of what exactly is going on in any company. They help companies quickly assess the health of their customer base and determine if their SaaS is operating as expected. On a more granular level, they can help teams detect where they might be losing customers in terms of retention or where new features may be pushing away their loyal customers.

To some degree, metrics can quickly divulge from metrics to vanity metrics. Vanity metrics, for those unfamiliar, are metrics that are only viewed when they are doing well. They aren’t really actionalble when they are doing poorly. So, it’s important to take the right approach with metrics. However, with the right approach, metrics are the secret weapon that allows a comapny to understand why its growing or shrinking and take action accordingly.

Now that you understand which metrics you should be tracking, the next question is how do you start managing all this data. All metrics start with a solid set of data sources. So, this is where building your customer data stack comes in. Fortunately modern tooling can help your team quickly go from 0 to 100, so you can develop a scalable system for tracking customer metrics and providing better insights to leadership to drive the business forward. Check out this guide to Architecting a Scalable Data Stack to learn how to make foundational decisions now that will future proof your data stack for the long-term.

April 13, 2022
Benjamin Rogojan

Benjamin Rogojan

Seattle Data Guy, Data Science and Data Engineering Consultant