We need to monitor and track metrics to verify our product's current performance and make future decisions. So what metrics are available, and which ones should we be checking for our product?
Let's examine key data metrics and why we need to monitor these metrics.
Finding the Right Metrics for Your Product
Since the products we manage all have different characteristics and each has its own unique goals, challenges, and ways of defining success, selecting appropriate metrics is crucial.
Additionally, metrics that need to be monitored can vary depending on team members' roles within the organization, as they have different objectives and responsibilities.
Connecting with Product Growth
The goals to focus on for product growth vary depending on the product's nature.
For example, social media apps like Instagram or messaging apps like KakaoTalk and Discord prioritize frequent and repeated customer usage. In such cases, product growth can be tracked through metrics like Monthly Active Users (MAU) or customer retention rates.
For subscription-based services like Netflix or SaaS platforms like Cafe24, monthly recurring revenue is crucial, so you can monitor product growth through metrics like Monthly Recurring Revenue (MRR) or Average Revenue Per User (ARPU).
It's most effective to first determine which data most closely correlates with our product's growth and then identify the metrics that best track this.
Based on Team Member Roles
The metrics that need to be monitored and tracked can vary depending on team members' roles within the organization.
Executives need to monitor overall company performance through metrics like revenue growth rate and gross profit margin to understand the company's overall status.
Product development teams need to monitor customer behavior and engagement metrics like Monthly Active Users (MAU) or session duration to understand if customers are using our product effectively.
It's best to first identify each team member's role and goals for our product/company and then monitor related metrics.
Introduction to Metrics
We'll introduce metrics in three main categories:
- Revenue-related metrics
- Customer behavior and engagement metrics
- Customer satisfaction metrics
It's best to check the most appropriate metrics aligned with the product growth goals and team member roles mentioned above.
Revenue-related Metrics
These metrics are directly related to company revenue and costs. They're associated with metrics that can track overall company revenue, costs, and growth.
| Name | Description | Calculation Formula |
|---|---|---|
| MRR (Monthly Recurring Revenue) | Represents the total recurring monthly revenue in subscription-based business models. MRR is particularly important in subscription service models like SaaS, used to evaluate continuous revenue flow and growth. | Monthly Subscription Fee × Number of Users |
| ARPU (Average Revenue Per Unit) | Represents the average revenue generated per user or subscriber. ARPU is a crucial metric measuring how much revenue a company generates per customer. | Total Revenue ➗ Number of Users |
| LTV (Customer Lifetime Value) | Represents the total revenue generated by a single customer throughout their entire relationship with the company. A crucial metric for developing marketing strategies, customer retention strategies, and business plans. | ARPU × Average Customer Retention Period |
| CAC (Customer Acquisition Cost) | Represents the total cost spent on acquiring new customers. CAC is an important metric for evaluating how efficiently a business operates its marketing and sales activities. | Total Advertising Cost ➗ Number of New Customers Acquired |
Customer Behavior and Engagement Metrics
These metrics indicate how well customers are using the product. They're useful for evaluating product quality and user satisfaction, and finding areas for improvement.
To understand these metrics, it's important to first establish product activation criteria. For example, for a messaging app, these should be based on customer actions that align with product goals, like "sending a message to one person" or "receiving replies from at least one person."
| Name | Description | Calculation Formula |
|---|---|---|
| MAU (Monthly Active User) | Represents the number of unique users who used a specific website, application, or service during a month. Daily Active Users (DAU) and Weekly Active Users (WAU) are also monitored. | Number of Active Users in a Month |
| Conversion Rate | A metric showing the percentage of users who took specific actions, generally used to measure user actions for achieving specific goals. Used to verify marketing efficiency or revenue growth. | Number of Active Users in Period ➗ Total Users in Period × 100 |
| Session Duration | Represents the total time users maintain activity after accessing a website or application. Usually measures the total time from a user's visit until departure, an important metric showing how actively users consume content or interact. | Sum of Active Time in Period ➗ Number of Active Users in Period |
Customer Satisfaction Metrics
Satisfied customers are likely to show loyalty and use the product long-term. Furthermore, they help with repurchases and attracting other customers, allowing us to verify how much value our product provides to customers.
| Name | Description | Calculation Formula |
|---|---|---|
| NPS (Net Promoter Score) | A widely used metric for evaluating customer satisfaction and loyalty. Primarily measures how likely customers are to recommend the company or brand to their friends or colleagues. | Survey Result Analysis • 0 to 6: Detractors (dissatisfied customers) • 7 to 8: Passives (neutral customers) • 9 to 10: Promoters (likely to recommend) Percentage of Promoters - Percentage of Detractors |
| Churn Rate | A metric showing the percentage of customers or users leaving the service during a specific period. Churn rate is primarily used to evaluate the success of customer retention strategies or predict long-term service sustainability. | Number of Churned Customers in Period ➗ Number of Customers at Start |
| Cohort Retention | Retention shows the percentage of customers or users continuing to use or maintain service over a specific period. Cohort analysis particularly helps understand how groups of customers (cohorts) who signed up during the same period continue to use the service over time. | Define customers who signed up during analysis period as same group (cohort) Create table with revisit/activation metrics by time unit |
Conclusion
While there are many metrics beyond those introduced today, the key point is deciding which metrics to monitor based on role objectives and interpreting data according to criteria appropriate for each product.
If you haven't decided which data to monitor yet, why not start with easily trackable metrics like number of subscribers and monthly revenue, then look for more detailed metrics that can lead to better insights?
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