GITNUX MARKETDATA REPORT 2023
Must-Know Product Usage Metrics
Highlights: The Most Important Product Usage Metrics
- 1. Daily Active Users (DAU)
- 2. Monthly Active Users (MAU)
- 3. Retention Rate
- 4. Churn Rate
- 5. Average Session Length
- 6. Session Interval
- 7. Stickiness Ratio
- 8. Time-to-First Key Action
- 9. Feature Usage
- 10. User Growth Rate
- 11. Conversion Rate
- 12. Completion Rate
- 13. Customer Satisfaction Score (CSAT)
- 14. Net Promoter Score (NPS)
- 15. Lifetime Value (LTV)
Table of Contents
Product Usage Metrics: Our Guide
In our latest analytical research, we dive deep into essential product usage metrics that every marketer should be aware of. Highlighting recent studies, this blog post offers comprehensive insights into key metrics that could dramatically transform the way you understand and respond to your user behaviors. Prepare to navigate the landscape of product usage tracking, decode customer engagement, and leverage retention for your products.
Daily Active Users
This metric measures the number of unique users who engage with a product or service on a daily basis. It shows how many users find value in the product daily.
Monthly Active Users
Similar to DAU, this metric measures the number of unique users who engage with a product in a month. It provides an overview of monthly user engagement.
Retention Rate
This metric calculates the percentage of users who continue to use a product over time. A high retention rate demonstrates that users are finding value and staying engaged.
Churn Rate
Opposite of retention rate, churn rate measures the percentage of users who stop using a product over a specific period. Higher churn rates indicate waning user satisfaction.
Average Session Length
This metric tracks the average amount of time users spend within a product during a single session. Longer session lengths imply higher engagement levels.
Session Interval
This metric measures the time between a user’s sessions in a product. Shorter intervals suggest users are more frequently engaging with the product.
Stickiness Ratio
Calculated by dividing DAU by MAU, stickiness ratio represents the percentage of monthly users who engage with a product daily.
Time-To-First Key Action
This metric examines how long it takes users to complete a primary action (e.g., making a purchase or signing up).
Feature Usage
This metric identifies which features within a product are used the most or the least. It can help prioritize improvements on popular features and revamp underused ones.
User Growth Rate
This metric tracks the number of new users acquired over time. A steady or increasing user growth rate reflects a successful product that attracts and retains users.
Conversion Rate
This metric measures the percentage of users who complete a desired action (e.g., upgrading to a premium account or making a purchase).
Completion Rate
This metric calculates the percentage of users who successfully finish a task or process (e.g., completing an online course or filling out a form).
Customer Satisfaction Score
This metric uses surveys to gauge users’ satisfaction with a product on a scale (e.g., 1-5 or 1-10). Higher CSAT scores indicate more satisfied users.
Net Promoter Score
This metric asks users how likely they are to recommend the product to others on a scale from 0 to 10. The NPS score can help identify promoters or detractors.
Lifetime Value
This metric estimates the total revenue a user will generate during their time using the product. A higher LTV reveals more valuable user relationships and greater product success.
Frequently Asked Questions
What are product usage metrics?
Why are product usage metrics important?
What are some common product usage metrics to track?
How do product usage metrics aid in decision-making processes?
Can product usage metrics help in identifying user issues and pain points?
How we write these articles
We have not conducted any studies ourselves. Our article provides a summary of all the statistics and studies available at the time of writing. We are solely presenting a summary, not expressing our own opinion. We have collected all statistics within our internal database. In some cases, we use Artificial Intelligence for formulating the statistics. The articles are updated regularly. See our Editorial Guidelines.