Difference between Product Analytics and Marketing Analytics

When most people think of analytics, they think of marketing analytics. However, product analytics is a very different beast.

Marketers use historical data to understand customer behavior and craft messages that resonate with them. They look at what worked well in the past to try and recreate those successes.

Product managers, on the other hand, use analytics to understand how customers are using a product and identify areas for improvement. They’re always looking ahead to optimize the user experience and make sure their product is successful long-term.

When you think about it, product analytics is pretty important. After all, if you don’t know what’s happening with your product, how can you make decisions that will help it succeed? But what exactly is product analytics?

Product analytics is a process of examining product data in order to improve products and services. This can include improving user experience, increasing conversions, or anything else that impacts the bottom line. Product analytics encompasses everything from understanding user behavior to measuring the value of your product. By using analytics, product managers can make data-driven decisions that will help their products succeed.

In the world of startups, Google Analytics is a common solution for companies that are looking to optimize their product in order to increase conversions. Product managers will often use Google Analytics tools in order to see where users are dropping off and what they might be doing wrong.

However, there is one major problem with using Google Analytics as your primary source for data: it doesn’t give you any information about why people aren’t converting on your site.

Although it can be useful for marketers, it’s important that product managers to do more than just look at data from Google analytics when trying to understand user behavior and make changes accordingly.

Let’s take a step back and think about how many established companies improve their digital products and services. Many companies use customer interviews to figure out how their customers interact with their product. The idea being, that once we know how people use our product, we can improve those so that we can make our customers reach their goal easier.

A major part of product analytics is trying to solve the same problem. We use behavioral data generated by our users interacting with our product, in the form of clicks and events, to analyze what they do and generate insights from that behavior. Once we have enough insight into what goes wrong along the user’s journey, we can improve our products to make sure as many people benefit from our product.

As a simple example, take a music streaming app. We can have a simple expectation of our users journey, starting with installing the app, signing up for an account and then playing their first song. Instead of doing customer interviews or watching in person how our users are using the app in a lab setting, we can use a few tools from the product analytics toolkit to get the same insights.

To measure our expectations about the user journey, we could simply define a behavioral funnel. Where each step in the funnel corresponds with first installing the app, then signing up and finally playing a song.

We can see from the conversion funnel that a significant percentage of our users never reach the goal of playing a song. This is a basic and somewhat rigid way of looking at user behavior. A better approach would be to perform a User Journey analysis, which can be used to show all steps that lead to a certain action, like playing a song in our example.

We can see now, reading from right to left, that users take many different paths to finally play a song. We see that a majority of users first pick a genre from the catalog, and then play a song instead of playing a song directly after signing up. While our User Journey analysis gives you much more information to improve your product, it doesn’t tell you what path leads to your ultimate goal.

For example, your goal might be to increase retention, you want as many users to come back at least 14 days after installing the app. To get to these kinds of insights, you can use an Aha-Moment analysis as shown below. This will predict what behavior will lead to your goal, in our case coming back after at least 14 days.

As we can see, when a user plays a song at least once in the first four hours of installing the app, that user is much more likely to come back after 14 days. This is a unique and actionable product insight that no amount of customer interviews can give you, all fully automated.

When it comes to insights, Stormly is your one-stop shop. It offers plenty more than other platforms and provides actionable data that aligns with goals for product management in a way no other analytics tool does!

Stormly offers plenty of unique ways to look at product analytics, such as feature retention, power users, revenue analysis and much more.

Combined with UX design and product development, Stormly helps you transform these unique product insights into changes to your product that improve the key metrics your business is after.

If you’re looking for a comprehensive product analytics tool that will help you quickly and easily improve your product, look no further than Stormly. It offers more features and insights than any other platform out there, all of which is tailored specifically to the needs of product managers. Sign up today and see how much easier it is to hit your goals when you have actionable data at your fingertips!