Product Analytics

When you’re looking for insights into your product’s users, the usual method is to ask for feedback from them directly. However, if done manually this opens the process up to user error, inaccurate reporting, and other problems inherent to self-reported methods. For more objective and timely reporting of user information and activity, you need to turn to product analytics.

In this article, we define product analytics and discover why they’re such a rich source of data about your users that you can use to glean actionable insights.

What is Product Analytics?

Product analytics is the process of automatically gathering quantitative data about your users and their usage patterns in your product. You can do this using tools that work within the product itself. These track various kinds of activity, like button presses, time spent on each page, and churn. With permission, these tools can also gather personal information about your users.

What is the Importance of Product Analytics?

Product analytics has several benefits over other methods of getting information about your users and their activity:

1. They generate accurate, granular quantitative data, which can produce valuable insights.

Product analytics represents the most accurate way of getting quantitative data from your users. Self-reported quantitative survey questions, like “How many hours do you use our app each week?”, will invariably suffer due to faulty user memory. However, you can track the quantitative data within the app itself, so the results you get are more objective and correct. 

With product analytics, you can even track user activity with much more granularity, down to how many seconds they spend navigating each part of your product. Qualitative surveys just don’t offer these kinds of insights.

This more accurate user data removes all the guesswork when it comes to answering how users derive value from your product. 

2. You can gather data from a larger pool of users.

Studies show that the average in-app survey response rate is about 13%. That’s a tiny portion of users, and if you’re running with a small user base, it might not be enough to give you meaningful insights. In contrast, your product’s analytics allows you to gather usage data from all users at once. This exponentially increases the sample size of the data that you collect.

3. Product analytics exposes certain types of data that surveys might not give you.

It’s not just granular information that you can get from product analytics. You’ll also be able to see data points such as:

• Where users are coming from
• Which channels they use the most
• Which parts of the product are least useful to them
• How long customers use your product before churning
• The various paths customers take through your application to get to what they need

You can then use this data for insights about customer acquisition, experience, retention, and engagement.

Who is Responsible for Product Analytics? 

Understanding your product’s analytics and knowing how best to leverage their insights is a critical research-driven task. Typically, the product manager, owner, or another strategic product role will have ownership over the product analytics process.

However, as companies become more aware of the importance of using analytic tools and analyzing their data, a dedicated role has emerged: the product analytics manager. 

This manager is responsible for guiding the implementation of product analytics for each product. This includes deciding which metrics are tracked in each product, which product analytics tools you’ll use to analyze the data gathered, and how to use the findings to inform your product strategy and advance business goals.

The role is best suited to someone with strong problem-solving skills, a fluent understanding of how big data is used to gain insight, and experience in market research and marketing strategy. 

How Do You Employ Product Analytics?

You should have a plan of action to implement product analytics if you want to integrate it meaningfully. Here are some best practices:

1. Outline your objectives for collecting data.

You need to know why you’re gathering product data and what you’re using it for if you want to use it effectively. Set specific targets to achieve with your data, so that you know what metrics to focus on and what data to set aside. 

For example, in product launch analytics, you may want to know how effective your initial marketing strategy is. That means watching out for how many new users are coming on board, which channels users are coming from and what they’re using, what kind of feedback they’re providing, and how quickly they’re churning. 

2. Before development begins, have a tracking plan.

Product analytics depends on “events,” or tracked user actions. Before you start work on your product, you should have a tracking plan that describes all the events you want to keep track of. The key here is to ensure that you’ve covered critical actions across the entire user journey so that you can have data about every important point of interaction between your users and your product.

3. Leverage the power of real-time reporting.

One of the advantages of this data is that it’s reported to your analytics platform in real-time, so you have almost instant access to the results as they come in. This allows you to make rapid iterative changes to your product based on very up-to-date insights.

What this means is that the results of your analytics data should always be used to inform what to do for your next sprint. For example, when A/B-testing new features, you can use product analytics to determine how customers feel about them. Alternatively, you can also see whether users are able to discover the new features at all. Because the data is updated in real-time, you can make changes to A/B test iterations rapidly and continuously improve your product.

Product Analytics: Providing a Wealth of Insights

Product analytics gives you a fountain of valuable data about your users and how they use your product. However, it takes a good eye and a sound strategy to integrate them meaningfully into your product plan. They should also be used with qualitative data derived from customer feedback to get a bigger picture of why certain user engagement trends are happening.