Chapter 3: Tracking the Right Growth Metrics

3.0 Good Growth Metrics

Without good data, you won’t be able to reliably analyze the results of your growth experiments, and you’ll be back to a guessing game. Assuming that your data collection methods are sound, you still have to ensure that you are capturing the right metrics — ones that can help you understand whether an experiment has met its objective as well as your success criteria. In this chapter, we will review the characteristics of good growth metrics, and some standard metrics that can get your team started.

There are many qualities that make metrics “good,” but key characteristics to look for are the following:

  • Captures your objective
  • Easy to measure
  • Accurate
  • Actionable
  • Predictive
  • Based on your growth model

Captures Your Objective

Some might claim that it’s intuitive which metrics are appropriate for certain experiments, but this is not true. Experiments are meant to meet certain objectives and satisfy success criteria that you set. For example, there is often a tension in trying to optimize customer acquisition. Should you focus on quantity or quality? You can cast a bigger net to get more customers, but those customer probably won’t be as engaged as customers that you could get through more targeted acquisition efforts.

If your objective is just to grow the raw number of customers, you might track metrics such as the rate of new registrations or the conversion rate, which captures the proportion of people that finish the registration process to the number of people that start it. However, if your objective is to build a customer base of very engaged customers, you might prefer to measure things such as your daily active users, which captures how many of your users spend time with your product each day. Another metric that might be appropriate for capturing your objective to build an engaged customer base might be the average time that a customer is using your product in a given week. As you can see, the metrics that are appropriate for each objective are as disparate as the objectives themselves, so it’s important to take the objective into account when picking a metric to capture.

Easy to Measure

With the digital age and the proliferation of tools, many metrics have become easy to capture. However, some metrics remain difficult to capture. Those data points tend to relate to the non-digital realm such as measuring the acquisition rate of customers that saw a billboard advertisement or the retention rate of people that saw a television advertisement. Often times, it’s possible to measure the effects of a given experiment using a number of different metrics, and it’s worthwhile to focus on those metrics that are easier to measure to ensure that you do not waste effort by running experiments without good data outcomes. However, it’s important to understand that there is a danger of defaulting to those metrics that are easiest to garner and not those that provide the best basis for analysis. You will need to determine the right balance for your goals. Sometimes it is preferable to opt for difficult to measure data if it will give you a clearer picture of whether or not an experiment successfully met your objective.

Accurate

Good metrics should also be strongly tied to outcomes rather than loosely represent them. For example, let’s say that you wish to impact how many purchases customers make. You could choose to measure either the customer’s intent-to-purchase or actual purchases. It is well known that intent to purchase is not always an accurate indicator of actual purchases. Therefore, you would likely be better off measuring the actual volume of purchases rather than a virtual metric such as intent.

Actionable

All metrics should be actionable in theory, but some are definitely easier to act upon than others. How actionable metrics are greatly depends on how specific they are. Let’s say that your product is an online, subscription-based task management tool. If I told you that the average customer satisfaction score for your product is 6.9 out of 10, would you know immediately what to do next? What if I told you that 4 out of five 5 customers that register for your product start but never finish creating their first task entry? Would you have a better idea of where to focus and what to try next? I hope that the second metric would arm you with much more actionable data. It takes a lot of time and resources to conceive and implement an experiment, so it’s well worth it to give some thought to how actionable the data that you intend to measure will be.

Predictive

It is always a good idea to track data that gives you insight into your business and are outside the context of a particular experiment. These metrics are different in nature than those that you should be tracking to identify if a particular growth experiment has met your objective or success criteria. Instead, the data should help to shape your growth strategy by helping you anticipate problems before they become hugely disruptive to your business. A classic example of this is the distinction between the rate of customer service emails and customer churn rate, which indicates how quickly existing customers are abandoning your product.

Let’s go back to the example of an online task management software. Imagine that your team changes a big piece of your product, such as, how tasks are created by users. Should you be tracking the customer churn rate or the rate at which your customers are contacting customer support. The former is usually calculated after the fact, whereas you can see a spike in customer support inquiries immediately. Therefore, the rate at which your customers are contacting customer support will let you react much more quickly than the churn rate, so you should be keeping your eyes on customer support emails or calls. As you think about what metrics you should track to give you general insight into the health of your business try to think about what data can tip you off about problems earliest.

Based on Your Growth Model

Although metrics such as daily active users or conversion rates are fine, nothing beats creating a data capture strategy based on your own growth model. Doing so ensures that you have precisely those metrics that are tied to variables that most directly affect the growth rate of your business. Your revenue and customer growth models should tell you precisely what metrics your team should be tracking.

Marketplace Revenue Growth Model

Let’s consider an online marketplace with the above model as an example. At first glance, you can see that you should be capturing the following metrics:

  • The number of sellers
  • The average number of posts per seller per time period
  • The number of views of items for sale
  • The number of buyers
  • The number of times an average buyer visits the marketplace in a given time period
  • The ratio of the number of times that buyers visit an item page to the number of completed sales
  • The average fee that your company charges per transaction
  • The average sale price of items in the marketplace

Not only did the revenue model help us identify novel metrics that are perfectly suited for an online marketplace, such as the number of views of items for sale, but it also helped us make standard metrics, such as conversion rate, specific and contextual to the business.

As mentioned above, making changes to marketing, product implementation, and operations is resource intensive, so it’s well worth spending some time to identify metrics that will help your team clearly determine whether your growth experiments meet their objective and satisfy success criteria to ensure that you are not wasting time and resources. Of course, it would be ideal if all of your metrics satisfied all of the criteria above, but the reality is that many will not. In that case, it’s best to focus on ensuring that the metrics that your team chooses to capture align with your model and are accurate. Beyond your tailored metrics, there are others that have become standard practice to track. We will explore these in greater details in the following sections.

This post is part of the Growthzilla Book series, which is an online draft of the print edition that will be available in 2018. Be sure to check back on tomorrow to learn about common acquisition metrics. New sections of Growthzilla are published every weekday.