13 Product Manager Analytics You Need to Know
Product management is a highly data-driven field. Tying tangible numbers to the progression of your product and how your customers see it can reveal useful business insights. To that end, knowing certain product manager analytics metrics and tools is essential.
With product analytics, a PM can get an honest look at the performance of the product they manage. This, in turn, equips them with the insights they need to create plans of action and lead product teams towards success.
In this article, we’ll share:
- The essential product manager analytics metrics you need to know.
- The essential analytics tools you could try.
- Answers to some frequently asked questions.
Let’s get started.
13 Product Manager Analytics Metrics You Should Know
To truly follow the agile methodology, you need a solid product analytics framework in place. However, to create that framework, you need to identify and start tracking certain product-focused metrics (that also align with the business goals).
These metrics should be made a part of the core product strategy and should help ensure that the business is on its way to achieving the product vision.
If it’s not, the PM then collaborates with the concerned departments (product development, product strategy, or marketing) to work on potential fixes.
Regardless of what your product is, you should definitely know the following product metrics:
1. Product-Qualified Leads
Product-qualified leads (PQLs) is an essential sales and marketing-focused metric that shows you the number of people who go through an activation event and use your product. It measures the number of users who have experienced your product’s value. This can be done either by completing regular transactions or through trials/freemium experiences.
If only a small number of users are experiencing a new product’s value, there’s a good chance that the product won’t succeed. That’s why product-qualified leads are an important metric.
Every company defines its PQLs differently. That’s because the specific activation event(s) that lead the user to try and experience the product can be different.
No matter how you choose to define them, PQLs can reveal potential strengths and weaknesses in the pre-conversion processes. These insights can be shared with the marketing and sales teams, which, in turn, can use them to improve their processes.
2. Feature Adoption Rate
If you already have an existing product that has been around for some time and you add a new feature to it, you need a way to tell how many people actually use it. That particular metric is known as the feature adoption rate.
Feature adoption rate shows how many of the existing users have adopted and regularly started using a new feature or functionality of the product.
This crucial product manager analytics metric can help reveal any potential problems about the new feature and can help set KPIs for its acceptance and adoption.
Feature adoption rate is expressed in percentage and can be calculated using the following formula:
Feature Adoption Rate = (# of Users Using a New Feature/# of total users) x 100
A low feature adoption rate could mean several things, including, but not limited to:
- There’s not enough demand or need for the feature.
- The feature is complicated for the users (UI/UX/engineering team must be informed).
- Not many users are aware of the feature (marketing must be informed).
To pinpoint the exact problem, PMs may seek user feedback and/or look at other related metrics.
3. Time-to-Value (TTV)
Time-to-Value (TTV) is the total time it takes for the users to realize or see the value your product has to offer. This particular, customer-focused metric, is similar to return-on-investment – the only difference is that the “return” doesn’t necessarily have to be financial/tangible, but rather the overall efficacy of investing in that particular product.
Ideally, the average TTV should be as short as possible. The sooner your customers experience your product’s value, the better, since it will translate into quicker adoption and a potential virality.
If it takes a considerable amount of time for customers to realize the value of your product, chances are that many of them would instead go for competitor products.
Long time-to-value can indicate potential problems with the customer onboarding process, the user-flow, or both.
It can be a bit challenging to measure time-to-value, as it requires gathering user feedback. A quick tip would be to ask them an in-app question like “Are you enjoying our product?” after the customer has been onboarded and seeing how they respond.
4. Daily Active Users (DAU)
Onboarding thousands of users is great, but how many of them actually use your product on a daily basis is the real question. The daily active users (DAU) metric can help answer that question. A significant number of daily active users reflects healthy product growth and acceptance.
There are a lot of ways you can define daily active users. The most common way is to look at the total number of visits, the number of times an app has been used, or any other platform-specific activity.
However, make sure that the way you define DAUs makes sense from a strategic perspective. If a particular activity doesn’t help you move the needle for your product, there won’t be any clear business end goal for tracking it.
To get a deeper perspective, you can also track monthly active users (MAUs) and compare them with DAUs.
5. Customer Satisfaction Rate/Score
As the name suggests, customer satisfaction rate (or score) is a metric that reflects how satisfied or pleased your users are with your product. Like TTV, customer satisfaction rate can only be measured by directly asking users for feedback.
To do this, you can ask different in-app/site questions (such as, “on a scale of 1-to-5, how satisfied are you with our product?”) and quantifying the feedback you receive.
It can be calculated as:
Customer Satisfaction Rate = (Total # of Positive Surveys/Total Number of Surveys) x 100
You can also go one step further and A/B test the question by asking customers to figure out what needs to be improved at different stages of your product/service.
Customer satisfaction rate can be increased by improving the overall user experience, launching new features, or actually implementing customer feedback.
6. Customer Retention Rate
Considered to be the most important business metric, the customer retention rate reflects the total percentage of customers/users that continue to use your product, in a given time period (usually measured from the time they are onboarded).
To calculate the CSR, use this formula:
((# of Customers at the End of Period – New Customers)/Customers at the Beginning) x 100
Getting new customers is a lot more expensive than keeping them. If you’re succeeding at retaining a significant number of customers (while also consistently onboarding and activating new ones), you’re providing better value than your competitors.
At this point, you can continue doing what you’re doing or experiment and test ways to increase that retention rate. Taking a holistic view of the product manager analytics can help with that.
7. Customer Churn Rate
Customer churn rate is the exact opposite of customer retention rate. It shows the percentage of customers that unsubscribe, stop using your product, or leave your business in a given time frame.
A high customer churn rate should be a major cause of concern for the product management team as it can be devastating for your business.
Customers can leave due to a number of reasons. It’s up to the product managers and analysts to determine the cause. Some broad culprits include:
- Low value (competitors are offering something better)
- Outdated features
- Poor customer support
- A public event that besmirched the company’s reputation resulting in boycotts
A high churn rate should be investigated and the issues causing it should be resolved immediately.
If you’re interested in learning more about how to excel with product management analytics, take a look at our certification course.
8. Session per User
Session per user is a metric that’s especially used for web-based products (it’s also a search engine optimization metric). It shows you the total number of specific actions taken by a user on your website in a given time frame.
These actions can be as simple as clicking on a page to initiate some sort of transaction (you can track them separately). Here’s a quick formula:
Session per User = Total # of Sessions/Total # of Users
You can use Google Analytics to automatically track sessions per user.
9. Time Spent on App/Site
Whether your digital product is app-based or web-based, you need to evaluate how much time users spend on it.
The more time people spend using your product, the more revenue you can earn.
If people aren’t spending as much time on your product as you’d like them to, try improving the UI and modifying the user flow. The reason some people spend hours on certain social apps is that they’ve intentionally been designed to keep their users hooked for as long as possible.
10. Net Promoter Score (NPS)
The net promoter score (NPS) is perhaps the most important metric for marketing and customer success teams. It’s calculated as a percentage and shows the likelihood of your existing customers recommending your product to others.
To calculate NPS, you need to directly ask your customers for feedback, with a question along the lines of “How likely are you to recommend our product to a friend, family member, or colleague?” – calculated on a scale of 1 to 10.
Once you start receiving feedback, you can categorize your customers as the following:
- Promoters – people who either respond with a 9 or 10. You need more of these.
- Passives – people who respond with either 7 or 8.
- Detractors – people who respond anywhere from 1 to 6. These are the customers that you should be focusing on the most (getting additional feedback by asking them what you can do differently).
After getting a count of your promotors, passives, and detractors, you can calculate your NPS using the following formula:
Net Promotor Score = (# of Promoters – # of Detractors) x 100
The higher your NPS, the better. You may also end up with a negative NPS, which indicates huge flaws in the product or related processes.
11. Monthly Recurring Revenue (MRR)
This is easily the most vital business metric. The monthly recurring revenue (MRR) is the guaranteed revenue that a business will earn every month.
To calculate your MRR, simply add the revenue earned from your total number of paying/monthly recurring customers.
To increase your MRR, you can either increase the price of your product (provided you can justify it) or focus on increasing your total number of conversions and customer activations.
12. Customer Lifetime Value (CLV)
Customer lifetime value (CLV) is an estimated total revenue that you can earn from a specific customer in their given lifetime (or the period they stay with your business).
Use this formula to calculate CLV:
CLV = Value of Sales x Number of Transactions x Estimated Retention Period
CLV is a product marketing metric that’s used to create customer segments and prioritize certain accounts over others. This prioritization can help you deploy your resources where it matters the most.
13. Cost per Acquisition (CPA)
Last but not least, a critical metric to track is the cost per acquisition (CPA). It shows the sum of all the costs associated with your efforts resulting in acquiring a new customer or lead.
Here’s how you calculate it:
CPA = Total Cost Spent on Marketing and Sales/Total Number of Conversions
It’s mainly relevant to marketing and sales teams and can help reveal optimization opportunities in their processes. If you’re paying a lot more than you should to get new customers, find out how you can cut down on those costs.
Popular Product Manager Analytics Tools
If you’re currently building your product management stack, it’s vital that you pick the right analytics platforms.
You can use the following types of product analytics tools to collect relevant data (with suggestions):
- Engagement – help track engagement analytics. Popular tools include Amplitude, Google Analytics, and Mixpanel.
- Product Health – these tools help collect feedback from your customers. DataDog, Doorbell, and Apptentive are some of the most reliable tools you can use for this purpose.
- User Behavior – user behavior data can reveal hidden patterns and help improve the customer experience. You can use Hotjar, Crazy Egg, and Localytics for this purpose.
- Data Visualization – to present their analysis to stakeholders and business leaders, product managers need data visualization tools. This is something that Tableau can help with.
Some companies develop native tools for their data analytics needs, provided they have the resources for it.
Frequently Asked Questions (FAQs)
Below, we’ve answered some of the most popular queries related to product manager analytics:
Do Product Managers Need Data Analytics?
Yes, product managers do need data analytics. In fact, it’s one of the most basic responsibilities of a typical product manager. With the help of analytics, PMs can get valuable business intelligence, empower their product decision-making, and improve their processes.
How Do Product Managers Use Analytics?
Product managers use analytics to improve their internal processes, communication, and the product itself. The insight discovered through product analytics can be used to create action plans and to resolve any potential issues or capitalize on opportunities.
Here are a few examples of how a product manager might use analytics:
- Identifying a bug causing users to quit, reducing total time on app/site.
- Evaluating the success of a new feature based on the feature adoption rate.
- Discovering holes in the user flow that might be contributing to long time-to-value.
Depending on their goals, product managers can use analytics in a number of ways.
What is an Analytics Product Manager?
Analytics product managers work with business stakeholders and internal teams to develop, oversee, and manage the product roadmap of analytics products or data models for products that aren’t primarily used for analytics.
They use their business know-how with knowledge of data science and marketing to create visions for such products. They also act as a bridge that connects internal teams, enabling them to work towards that goal.
Do Product Managers Use Google Analytics?
Product managers responsible for the success of web-based products use Google Analytics to gather user data and get insights. For mobile/products that don’t run on websites, they use other types of analytics products to gather insights and make business decisions.
Who Earns More Data Scientist or Product Manager?
According to Glassdoor, data scientists earn $116,017 per year (based on 16,799 salary reports). Product manager jobs, on the other hand, pay $112,020 per year (based on 30,687 reports collected on Glassdoor). Both professions earn more or less the same.
Remember, at the end of the day, the salary you get paid depends on your employer, where you’re based, and level of experience you have. For instance, a senior product manager at Google will most likely get paid more than a data scientist working at a startup in West Virginia.
Wrapping it Up
Product manager analytics is only as effective as the quality of data you have and how you use it.
The key to success is having a defined framework in place for data collection, analysis, and feedback. As long as the metrics are tied to the strategic objectives of your business, and the internal teams act on that data, your product is likely to succeed.
