The Multi-Chapter Guide to Product Management Metrics

Delivering an exceptional user experience is more important than ever in a SaaS environment, and product managers play a pivotal role in making this happen. Being the closest to product features, they make decisions to deliver value to users, drive feature adoption, and keep users engaged over time. 

Product managers are also responsible for translating product vision and initiatives to the executive, sales, and marketing teams. Product management metrics are thus a crucial tool to gauge user experiences and vet new product functionality. They empower product managers to prioritize the product development backlog, avoid waste of engineering resources, and maximize a product’s revenue potential. 

This guide explains the best practices for implementing and tracking the essential metrics used by product managers to understand how users interact with their products. It also includes lists of common pitfalls to avoid. 

Top Product Metrics

Here is a summary of ten common product and business-level metrics that a product analytics tool can help you track.

Domain Metric Description
Product-Level User Retention A product-level metric that measures the percentage of users who return to a product within a specific time period.
Time to Value (TTV) The time between purchasing a product and realizing its value; the shorter the number, the better.
Daily Active Users (DAU), Weekly Active Users (WAU), and Monthly Active Users (MAU) Measuring active users is a way to determine the engagement of your product. The type of product you manage determines which time interval is most appropriate. For example, DAU would be more useful for a messaging app and WAU for contract management software.
Customer Effort Score (CES) Tracks responses from users on how hard it is to use a product feature or module
Stickiness Stickiness measures the ratio of DAU to MAU, which tells you how many users return to use your product over a month. Higher stickiness indicates that the user gets recurring value from a product and is less likely to churn.
Adoption rate Adoption rate measures a product’s usage, including that of a specific feature over time. Product adoption can be indicated by other metrics, including stickiness and DAU/WAU/MAU.
Business-Level Customer Acquisition Cost (CAC) CAC measures how much it costs to convert a prospect into a customer. 
Customer Churn The rate at which customers leave or don’t renew over a given time period. It’s calculated as the percentage of customers who cancel out of the total customer base. It is also the inverse of customer retention, the percentage of customers who stay over a period of time.
Net Revenue Retention (NRR) NRR indicates the change in revenue generated from the existing customer base during a given period, excluding the revenue from new customers 
Net Promoter Score (NPS) Measures the loyalty of customers by asking how likely they are to recommend the product to a peer.
CSAT Measures a customer’s satisfaction by averaging responses to the question: “How satisfied are you with this product?”

Product Metrics in Detail

While product teams commonly use the ten metrics above, the type of business and product you manage will determine which metrics you should track. Here’s a more in-depth look at each of them.

Product-Level Metrics

User Retention

User Retention measures the percentage of users that return over a specific period of time. You can measure retention via logins or specific feature usage within an application. 

This metric looks at behavior at the user level, whereas customer retention looks at the behavior of an entire account, which may consist of multiple users. In B2C, users and customers are often the same, but B2B accounts typically have multiple users per account, so user and account behaviors may vary.

This metric is most meaningful when analyzed in the context of user cohorts to understand if users from a certain sign-up period behave differently due to new feature releases, for example.

Time to Value

Time to Value is the time it takes for a user to benefit from your product after purchasing it. The shorter this time is, the better your user retention will look.

A good Time to Value benchmark will depend on the type of product and your business sales cycle, but you should generally aim for this to be as low as possible. A large enterprise app may take months of implementation work before a user realizes full benefit, while a consumer app should deliver value minutes after a user signs up. 

Product managers must first identify what feature or usage constitutes a value milestone before measuring the time it takes to achieve it. A user journey typically includes multiple value milestones along the user journey, so product managers must interpret this metric in the context of an end-to-end product experience.

Daily/Weekly/Monthly Active Users (DAU/WAU/MAU)

Activity is a good indicator that users regularly receive value from the product. Over time, this metric shows how well the product retains its users and delivers value. 

The type of software you manage will indicate the appropriate frequency. If your product is used regularly, such as an email client, DAU will be appropriate. For a travel booking app, on the other hand, MAU would make more sense. The important thing is to measure this over time to identify usage trends.

Customer Effort Score (CES)

Users prefer easy over difficult and are likelier to stay loyal to a product if the level of effort to achieve value is low. This common human behavior makes CES a leading indicator of customer retention and predictable recurring revenue. 

An in-app survey form typically collects this information by asking users a simple question, such as: “On a scale of very easy to very difficult, how easy is it to use this feature?”. It’s common to use a Likert psychometric scale for rating, which ranges from 1 (very difficult) to 7 (very easy). 

The customer effort can be associated with a use case (e.g., getting certain information from the product) or a step (e.g., an activation process that requires an activation).

Stickiness

Stickiness is the rate at which users revisit your product. High stickiness drives product growth by increasing customer lifetime value, improving retention, and reducing churn.

Product Stickiness = Daily Active Users (DAU) / Monthly Active Users (MAU)

The goals of a startup SaaS company are growing and acquiring new customers, whereas a mature SaaS aims to retain existing customers. Therefore, stickiness becomes more important as your product matures.

Product Adoption

Product Adoption is more of an umbrella indicator that can be measured using a few other metrics, such as stickiness or active users, but is typically measured through feature adoption. It aims to show how many users are using or adopting your product to solve their specific problems. High product adoption is critical for SaaS businesses: It increases retention, Lifetime Value (LTV), net promoter score (NPS), and customer satisfaction score (CSAT) and has a direct impact on revenue.

Feature Adoption, as a proxy for Product Adoption, measures the usage of a specific feature in your product. It is typically expressed as the percentage of total users who use the feature as intended. High Feature Adoption indicates that you are solving the user’s problem and is also a great way to improve other key product management metrics.

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Platform
Out-of-the-Box KPIs
Segmentation
Feature-Level Adoption & Retention Analysis
Cross-Channel User Engagements
Engagement Impact Analysis
A.I-Powered Product Feature Mapping
Mobile Application Support
Gainsight
Heap
Pendo

Business-Level Metrics

Customer Acquisition Cost (CAC)

CAC measures how much it costs to convert someone into a customer. The metric includes expenses like ad spending, marketing costs, and sales salaries.

Product managers can impact CAC by focusing on product-led rather than sales-led opportunities. How can you leverage your product to convert a lead rather than depending on a sales team?

Free trials are a key part of product-led growth (PLG) strategies. Companies that offer free trials should track the conversion rate from free trials to paid customers, which affects CAC calculations.

Customer Churn

Churn is the percentage of customers that have discontinued using your company’s product over a period of time. For example, if you start your quarter with 500 customers and end with 460, your churn rate is 8%. 

Obviously, you want to aim for as low a churn rate as possible. It costs more for a business to acquire a new customer (see the CAC metric) than it does to retain an existing one, which is why preventing churn is so important. 

Customer retention is simply the inverse of customer churn and calculates the percentage of customers that remain with the company after a period of time. 

For subscription-based B2B organizations, the period of time for this metric is typically a year, and retention means how many customers have renewed their annual contracts. For consumers, this metric typically tracks monthly renewals.

Net Revenue Retention (NRR)

NRR measures the change in revenue generated from a company’s existing customer base, excluding revenues attributed to new customers. The changes in customer spending that drive NRR include upgrades and downgrades in subscription levels, increases or decreases in license volume, service cancelations, and account expansions such as purchasing new products by existing customers. 

On the other hand, monthly Recurring Revenue (MRR) includes revenues generated from new customers that can compensate for lost revenues and hide customer dissatisfaction or negative competitive pressures. As such, NRR reflects the fundamental health of a business. 

Net Promoter Score (NPS)

NPS asks a customer to answer the following question with a number on a scale of 1 to 10: “How likely is it that you would recommend this product to a colleague?” Customers who answer 9 or 10 are considered “Promoters,” 7s and 8s are “Passives,” and 0-6s are called “Detractors.” To calculate NPS, subtract the percentage of Detractors from the percentage of Promoters.

NPS is a “gold standard” metric for customer experience that indicates customer loyalty, so product managers must track this number. 

Customer Satisfaction Score (CSAT)

The Customer Satisfaction Score measures how satisfied customers are with products or experiences. The question is typically framed as “How satisfied are you with X?” and scored in a range from very dissatisfied to very satisfied. 

While NPS gauges customer loyalty, CSAT gauges customer satisfaction. A positive CSAT improves retention and revenue while reducing support costs.

Recommendations for Adopting a Product Analytics Solution

Every product team is unique, but here are some key factors to keep in mind to help you avoid common pitfalls when adopting a product metrics system.

Keep Things Simple

With the right product analytics tool, a vast world of user data will open up to you. It can be tempting to track everything, but it’s best to start with the goals of your product, then decide on a few key metrics (sometimes called your “North Star Metrics”) that will most empower you to make good product decisions and understand user behavior. 

For example, if you’re in a hyper-growth stage, it may make more sense to focus on CAC and Time to Value. In contrast, a mature SaaS company may care more about stickiness and retention.

Employ Segmentation

Product metrics tools help you break your user base into several subgroups based on specific characteristics, such as geography, job title, and user persona. Adding segmentation to your product metrics sheds light on how specific groups use your product and where the user experience might fall short for a particular user persona. As a product manager, you can use these insights to improve a feature or increase engagement for a specific segment, ultimately improving that segment’s customer satisfaction score (CSAT).

Select a Tool That Can Improve Product Metrics

A tool like Gainsight PX helps you understand user data and lets you influence user behavior. In-app guides and tooltips can help users navigate paths to value and adopt specific features. 

The combination of analytics and user engagement features (such as in-app guides and surveys) lets you measure the impact of engagement strategies and improve user experiences further.

The native integrations with customer success management (CS) and customer relationship management (CRM) tools allow an exchange of data necessary for cross-departmental collaboration and strategic planning.  

Key Takeaways 

Product management metrics not only communicate the past performance of a product but also help steer its direction. Engagement metrics like adoption and retention tell you how well your product delivers value to your users over time. They also help you prioritize features and initiatives on your product roadmap.

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Use A.I.-powered product feature mapping to eliminate manual work, drive data accuracy and accelerate your time-to-value

Use A.I.-powered product feature mapping to eliminate manual work, drive data accuracy and accelerate your time-to-value.

Dive into deeper adoption and user-retention insights with feature-level analysis to uncover parts of your product that are making the biggest impact

Dive into deeper adoption and user-retention insights with feature-level analysis to uncover parts of your product that are making the biggest impact.

Target users with in-app guides and emails, and then analyze how those engagements move the needle on KPIs

Target users with in-app guides and emails, and then analyze how those engagements move the needle on KPIs.

What’s Next

Read our guide’s chapters if you are interested in learning more about product and adoption metrics, user segmentation techniques, enterprise application metrics, and the must-have features of product analytics tools.

Chapter 1: Enterprise Product Metrics

This article will provide a concise guide to the essential SaaS enterprise product metrics your business can use to plan its growth.

Chapter 2: Product Analytics Tools

Learn the must-have features required by modern product analytics tools such as user path analysis, hierarchical feature tagging, user segmentation, and real-time data processing.

 

Check back with us; more chapters are coming soon!