Product Usage Analytics

How much value do users get out of your product? The answer lies in product usage analytics.

What is Product Usage Analytics?

Product usage analytics is the process of analyzing data related to how users interact with software, an app or a digital resource. The results can be used to figure out:

  • Which features are most popular
  • Overall product quality
  • How engaged users are
  • The type of customer that is using the product most often
  • Whether customers find value in a product/service
  • What issues users are running into
  • How well you are retaining customers
  • The stickiness of the product/service
  • Effectiveness of engagement strategies
  • User workflow
  • How adoption varies across user segments
  • When and how you need to communicate with a customer
  • How to integrate compelling upgrade funnels
  • The results of A/B testing

This is just a sampling of what product usage analytics can reveal, which is why this type of analytics is considered to be an integral part of research and development. Instead of guessing or theorizing then testing you can learn what’s working and what’s not directly from users to prioritize product management.

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Key Difference of Product Usage Analytics

Product usage analytics is one tool in the product manager’s toolbox for learning more about customers – but it’s arguably the best tool. Customer interviews and surveys have been used forever like a traditional hammer. However, product usage analytics is like a nail gun. You’ll get the job done a lot faster and know what you’ve built is solid. 

The difference is in the information gathered. Surveys and customer interviews tell you what users think they are doing or what they’d like to do with your product. There is room for human error and it’s subjective. Product usage data, on the other hand, is objective and tells you exactly what the customer is actually doing with your product.

This quantitative data is critical for optimizing product development and prioritization.

Gathering Data for Product Usage Analytics

The key to product usage analytics is obtaining usage data. Every action that a user takes can be logged by software. But it still isn’t usable yet. The data then has to be extracted and loaded into a database. From there, the data can be analyzed to look for trends and variations over time. 

But parsing out and interpreting product usage data isn’t easy if it’s just a bunch of numbers in a table. That’s where an analytics platform comes into play. At Mixpanel we put emphasis on clearly visualizing data using charts and graphs that are automatically generated yet also customizable.  

Most companies find that third-party platforms like Mixpanel are the most efficient way to extract, load and transform the raw data into reports that their entire team can access. The product usage data can be used by everyone from customer service to marketing to product development without any analytics training or special expertise. 

Analytics data doesn’t lie. You need it to make informed product decisions rather than guesses. The goal is to use this actionable information to improve the quality of your product as well as the customer experience even before people start using the product.

Product Usage Analytics: Terms to Know

Are there some terms in this post you’ve never seen before? While performing product usage analytics you will likely encounter the terms below:

Account Level – Refers to product usage data and activity for an account.

Adoption – The use of a product or feature.

Churn Rate – The percentage of customers that discontinue service in a given period. 

Conversion Rate – The percentage of users who take a desired action.

Empathy Debt – Term used to describe your understanding of your customer and what they need.

Engagement Rate – The number of active users. Also indicates how often users interact with a product.

Key Performance Indicator (KPI) – These are product usage metrics that help you gauge the performance of a product. 

MAU – MAU stands for monthly active users. 

Retention – How well a product or service keeps existing customers.

Segments – Categories or groups of users that can be compared with product usage analytics.

Stickiness – This is a term used to indicate whether users come back. 

Super User – Customers that use your product the most. Super users are great product advocates that are usually the most likely to leave positive reviews and refer others.

User Level – Refers to the product usage data and activity of an individual user.

Visualization – Visual representation of data, such as charts and graphs. 

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