Growth analytics vs. product analytics

It’s understandable that people would think growth analytics and product analytics are one in the same. For starters, both examine customer behavior. There is also overlap in the metrics, for example churn rate could be a factor in growth analytics and product analytics. Basic metrics like how many users make a purchase, the number of repeat visits and retention can also apply to both.

There’s also confusion due to the term product growth analytics. It’s simply another way to refer to product analytics that helps you improve product adoption – something that does have an impact on growth analytics.

The two are inherently intertwined in most cases because product quality directly affects growth. While growth analytics and product analytics converge at certain points, there are undeniable differences.

The primary purpose of growth analytics is to:

The primary purpose of product analytics is to: 

Of course, doing these things can have a positive effect on growth analytics objectives and vice versa.

One of the most basic differences between product and growth analytics is where in the customer journey data is being gathered. Growth analytics largely focused on user actions before purchasing the product. Product analytics almost solely looks at actions after a user has been converted into a customer because it is only then that a user can interact with the product.

While both growth analytics and product analytics evaluate customer behavior, they do so in different ways and at different stages. The intent behind the data analysis is also different. 

Let’s look again at churn rate. 

In terms of product analytics, churn rate is useful in telling you the percentage of users that are disengaging, where they are falling off and why. This could point to a flaw in the product or that customers reach a point when they no longer need the product. The information can help product developers improve the product so that the customer experience is better and customers use the product for longer periods.

Here we begin to see how churn rate applies to growth analytics, because retention is a key factor. A business needs to know the churn rate to gauge how many new customers need to be gained within a given time period to remain profitable or increase revenue. 

The example above highlights that growth analytics focuses on data that helps determine performance in terms of meeting business goals (usually growing revenue) whereas product analytics is all about product development.

As such, growth analytics goes beyond product use. Product analytics takes a granular look at user behavior while interacting with the product, and growth analytics looks at general user behavior. There’s a lot more attention given to cost analysis, marketing channels, campaign performance and funnels that convert users into customers that use a product. Improving the product is always desirable, but growth analytics is more concerned with understanding how to convince more people to try the product in the first place.

Analytics is only valuable if you act on the data that’s gathered. The first thing you should do is test to see if changes based on the data will have a positive impact. There are a couple ways to do this depending on the analytics gathered and the end goal.

A/B testing is a vital component of product analytics. With an A/B test users are split into two groups. One group will see the product (or product feature) in its current form. The other group will see a modified version. It’s best to change just one element at a time for the most accurate results. Next, start gathering data to see which version performs the best. A/B testing often involves a number of iterations before a determination can be made. 

For growth analytics, funnel testing is sometimes more appropriate. The testing involves building a funnel in your analytics platform, which is a series of steps/actions/events that you want users to take. You then measure the percentage of users that make it all the way through the funnel and where they drop off. Not surprisingly, funnels are usually related to customer conversion or acquisition in some regard. 

With growth analytics you can also determine campaign ROI. Looking at metrics like conversion rate, retention and monthly active users (MAU) you can figure out which marketing channels bring in the highest value customers or which marketing messages drive the most qualified traffic. Marketing campaigns can then be adjusted to see if the growth metrics improve. 

Now that you know a little bit more about product analytics and growth analytics you may be wondering what’s the best way to start gathering data. While you are setting up your analytics tracking there are a few things to take into account. 

Pick an Analytics Platform That Can Handle Both

The first thing a business should do is look for an analytics platform that can track both growth and product metrics across all applications and sites. It’s a tall order, but a few third party platforms like Mixpanel can handle it. The only other option is to custom build a platform of your own, which is often much more expensive and difficult to support.

Make Sure You Can Track Data Across All Channels

This is particularly important for growth analytics. Without being able to track data across channels it’s impossible to know which ones are the best at helping your business grow. Being able to merge data from multiple sources also simplifies data analytics all around.

Ask Questions That You Want Answered by Analytics

Analytics starts with questions that you don’t know the answer to whether they are about a product or the general health of your business. These questions can help you focus the data gathering in both categories and find an analytics platform that’s best suited for your needs. Start by identifying three top questions for product development and three more for growing the business.

Ensure the Ability to Measure Upfront

Ideally, you’ll want a tool that goes beyond data gathering and can help you test theories based on the data. Things to look for include funnel creation and A/B testing features. Reporting is also important. How the data is visualized affects how much information you gleam and ultimately how it’s measured. 

At Mixpanel we provide customizable autogenerated reports that make it easy for users of all experience levels to evaluate the data that’s collected. There’s no calculations or set up required beyond establishing the data you want to track.