· 18 min read ·
Product Managers encounter a lot of different data and metrics in their work. Sometimes it’s tough to keep track of what each metric means. The goal of this blog is not to tell you to start measuring 30 metrics for your product management team or company. That’d be crazy! Please don’t try that at home (or office 🤣🤣🤣 ).
The goal of this document is to make you aware of what different product manager around the world measure for their products, apps and websites. Ask yourself the following questions before reading further:
It is only when you have a clear understanding of what your north star is that you can figure out how to track the result of your efforts. Otherwise you will read this blog and start collecting things that don’t matter to your business.
If you are able to answer the above questions above then read on! If not then go read our Guide to Becoming a Data Driven Product team and come back. 😜😜
We have compiled this list of 30 product metrics. You will definitely cross paths with them at some point in your career.
The total people who stop using your product after a period of time are the ones who have churned.
Formula for calculating Churn Rate
Churn Rate = ( Cb - Ce ) / Cb * 100
Ce = Total customers at the end of a time period
Cb = Total customers at the beginning of a time period
Churn Rate is one of the most critical metrics to gauge the success of a B2B SAAS product.
It’s easy to measure churn. The challenge is what to do with it!
What is a good time period for measuring the churn rate? Should you measure it daily, weekly, monthly, annually? That depends on your sales cycle.
Is all churn equal? Not really. Losing customers with low lifetime value (LTV) has a different impact than losing ones with a high LTV. But then what if a customer starts as a LTV before progressing into a higher value. Think about these things when calculating churn. Acceptable values and actions to improve churn will be unique to how your company and customers operate.
It is the inverse of churn. The people who stay with you for a time period are the ones you have retained.
Formula for calculating Retention Rate
Retention Rate = ( Ce - Cn ) / Cb * 100
Ce = Total customers at the end of a time period
Cb = Total customers at the beginning of a time period
Cn = New customers added during the time period
Retention rate are most valuable when calculated:
The value of a customer throughout their engagement with your product is their CLV. This is the amount of money you will earn from the customer till the time they stop using your products.
Formula for calculating CLV
CLV = (Average Life of Customer) * (Value of Customer)
You can increase CLV by increasing the two values that make it up.
A customer will stay longer with you if you:
You can increase value of a customer by:
Fred Reichheld and Bain came up with NPS after studying how certain questions correlate to customer behavior. One question worked really well.
“What is the likelihood that you would recommend Company X to a friend or colleague?”
This question is now used to measure NPS score.
Customers have to respond on a scale of 0 to 10.
Formula for calculating NPS
NPS = % Promoters - % Detractors
It is a way to understand how happy people are using your service. It makes sense because if someone is willing to refer your product to others they must be happy with it’s performance.
The entire organisation puts in effort to win customers. The total of all the effort in dollar terms is the customer acquisition cost for a product. This is generally one of the major expenses for an organization. The sales and marketing teams are the major contributors to this cost. This is because they focus on the upstream activities to attract and convert customers.
Formula for calculating CAC
CAC = Sum of all the money spent on Acquiring Customers
Bounce rate is the percentage of people who leave your site after visiting only one page. A low bounce rate indicates that visitors are finding what they are looking for on the page which is great. High bounce rate indicates one of the following:
Formula for calculating Bounce Rate
Bounce Rate= (Total Number of Visitors visiting only one page) / (Total visits to the page)
All the users who visit your site or app everyday make up your total daily active users. In order for the user to qualify as DAU they need to take meaningful actions on the site or app. You define these actions based on what engagement means for your product. Some examples of actions are:
All users who visit your site or app atleast once over a 30 day period are your monthly active users. Like DAU, they need to perform a meaningful action.
You know what DAU and MAU is from above. Stickiness is the ratio of MAU and DAU. It is expressed as a percentage.
Formula for calculating Stickiness
Stickiness = ( DAU / MAU ) * 100
Product stickiness is an indicator of how valuable your app is for user by looking at how often they use it. Stickiness as a metric might be crucial for you if you are a B2C app. But in some cases stickiness might not be the right metric for you. Especially if you are a set and forget type of app. For example - An integration software where users setup their integration between different applications and expect the data to stay in sync with each other. The value of such an app is that it is automating a process for users. If they have to keep logging in again and again it might be to troubleshoot errors.
It is similar to MAU calculation. MAU measures how active users are on the product in general, but feature adoption rate is more specific. It focuses on the adoption of a particular feature. It is useful when you are trying to find the initial response to a release.
Formula for calculating Feature Adoption Rate
Feature Adoption Rate = ( MAU feature / MAU product) * 100
It is expressed as a percentage.
You can use it to do a quick survey of how people feel about a particular feature or product update. You can trigger a survey question to pop up after a user has completed certain conditions. For example - You are a task management software and released a new update. It reduces the time for people to create a task. As product manager you want to find how the feature is doing. You can do this by finding the delight score of users who have tried the feature out. Here how you do it:
Delight Survey Question:
“How happy are you with using the “new feature”?
Scale: 1 - 5
We all shop online, right? After you bought your favourite 100th pair of sneaker 😝😝😝 you get an email. The company wants to know how your online ordering experience was with them. Usually this email comes right after you have placed the order. The experience is fresh in your memory. That is CES!
CES focusses on ease of use. You want your users to be able to use your products seamlessly without any hiccups. The primary goal of CES is to find how easy or difficult it is for the user to interact with the product and take action.
A CES survey is short and generally limited to one question where you ask the user about their experience with a specific action. A low CES score means that the user had difficulty in completing the action and you have your work cut out.
CSAT helps you get both qualitative and quantitative feedback from users. It not only tells you how satisfied or dissatisfied users are but also the reasons behind it. That helps in further understanding the cause of dissatisfaction. And eventually leads to solutions to address the issues highlighted by the users.
A simple CSAT survey consists of two question:
Question 1: Quantitative Question.
“How satisfied are you with product “X” on a scale of 1 to 5”
This is then converted into a percentage out of 100. Here is how it is calculated:
X = All Users who rated 4 or 5 for the above question
T = Total users who answered the question.
CSAT = ( X/Y ) * 100.
For example if 200 people answered the question and 100 out of them gave a rating of 4 or 5 then the CSAT score is (100/200) *100. This come to 50%
Question 2: Qualitative and Open Ended
“Tell us the reasons behind your rating”
It’s goal is to gather more information on the “why” behind the rating. This makes the feedback provided by users more actionable.
You can add more questions to the survey. Find the balance between the ease of filling out the survey with the amount of information you want to gather. It usually depends on the type of customers who are going to answer and how engaged they are with the product.
MRR is a key metric that SAAS companies measure and track. It is an important indicator of the profitability and cash flow of a company.
It is the amount of money that the company can expect to earn in a given month.
The calculation of MRR varies depending on how you bill your clients. For example:
You have a monthly subscription which costs $20 per month to customers and you have 1000 paying customers. Your MRR is 20 * 1000= $20,000
ARPA is the total amount of revenue earned from an account. It is calculated by dividing the total revenue earned by all accounts by the total number of accounts. The time period over which it is calculated can be monthly or yearly. Depending on the type of business ARPA can sometimes be replaced by ARPU. This is the average revenue per user.
Formula for calculating ARPA
ARPU = (Total Revenue of All Accounts)/ (Total number of Accounts)
A session duration is the amount of time a user has meaningful interactions with the website. According to google, Average session duration is the total duration of all sessions/ number of sessions.
It tells you how much time users are spending on your site. High value of session duration indicates high level of engagement which is great.
Conversion rate is the percentage of customers who pass through the different stages of a conversion funnel. There are four stages of a conversion funnel.
This is the first stage of a buyer’s journey. They are looking to improve their understanding of solutions out there to solve their problems. A company uses organic and paid means to drive traffic from this stage to the next.
Once the user lands on your site they become a lead. They start learning more about your product through the marketing collateral on the site.
At this point users are seriously considering your product. They might want to talk to your team to learn if there is a good fit. Your sales team talks to them to show them how your product stands out from competition and fulfills the customer use cases.
The customer signs up for your service.
MQL is a lead that has shown considerable interest in your product as a result of marketing efforts. Some examples of how users engage with marketing efforts are:
A sales qualified lead is one that has engaged enough with the product and is ready to talk to the sales team. There is often confusion about the difference between an MQL and SQL. So its important to understand it. Both sales and marketing teams have to agree to the definition of MQL and SQL. Let’s look at it through an example.
You work for a SAAS company. The marketing team has come up with a sales demo form along with other collateral (case study, infograhics etc.).
There are two leads. Let’s call them Sam and Tran. Here is how they interact with the company:
Should both qualify as an SQL? Probably not. Tran has shown a stronger intent to buy versus Sam. So we could argue that Sam is an MQL and Tran an SQL. This might not always be true. How you score your lead and define an MQL and SQL will depend on the unique needs of your business.
PQL’s are not as common as its counterpart MQL’s and SQL’s. The basic idea though is the same. It is a lead that is qualified by the product team before it moves down to the sales team. Let’s look at it through an example. You have launched a free trial for your SAAS solution. This enables prospects to signup for the product and use it. Now the traditional way to look at it is that every lead that signs up for the free trial becomes an MQL. But not every lead is equal 😀. The level of interest may vary and you can gauge it by how users behaves during the free trial period. This is a way to qualify the lead based on product indicators. You can go and then define when the lead becomes a PQL. It could be when a free trial user:
You define conditions that make sense for your product and industry you compete in. Over time as you gather more data on the behavior of the users you will be able to refine the conditions to more accurately represent it.
As the name suggests it is the total number of users who are engaging with your site for a given time period. It helps you understand:
Here is a google help article that describes it well.
Is there an industry benchmark for this? Not really. It varies depending on a variety of factors. For example, what is the product or service you are selling and how long does the sales cycle last?
If your active user numbers are below what you’d expect them to be then look at your marketing programs and see if:
Impression is the number of times your content has been displayed on the screen. The user does not need to engage with the content to be counted as an impression. Let’s look at an example:
You tweeted something today and you have a total of 500 followers on twitter. Your tweet shows up on every users feed. In this case your total impressions are 500. After a week you come back and post something else. It again shows up on every followers screen. Your total impressions now are 1000(500 +500).
Reach is is the total number of people who have seen your content. Let’s follow the above impression example. 500 people saw your first tweet. So your reach is 500. After a week your second post was also seen by 500 people (i.e all your followers). You reach is still 500. Unlike impression which doubled. Reach is to users what impressions is to screens.
It is the total traffic that comes from unpaid sources. It does not come from an ad you published on google,facebook or instagram. It happens when your content shows up as a search result for someone. Organic traffic is great because of a few reasons:
Traffic that comes to your site from paid promotions that you run on different platforms like Google, Facebook, LinkedIn. There are times when paid ads will make sense for you to invest in. They are great to:
Email is an important mechanism to engage with customers. Click through rate is the percentage of people who clicked a link in your email. For example if you sent an email to 100 users to book a demo and 60 clicked on the link then the CTR is 60%. Whether these users finally complete the demo booking form on the link does not impact CTR.
In the above example we assumed that the email sent is equal to email delivered. If out of the 100 emails, 10 bounced back then CTR will be 60/90 = 66%
This applies to mobile apps. Tracking the total number of times your app has been downloaded and installed is a good metric to be aware of. You shouldn’t, however look at it in isolation. For example the data can be misleading if you have :
It means that there are issues with your app that you need to sort out.
It gives both quantitative and qualitative measurement of how the app is performing. It is also a great way to increase customer confidence in your app. People will prefer downloading an app with positive reviews than one without any. This does not mean that you start pestering users to go and rate you on the app store. That seldom works. You should work to provide them with an exceptional experience and nudge them to share it with everyone else. Make sure you always respond to feedback you recieve on the app store. It shows that you are engaged with your customers and care about them.
TTV is the total amount of time taken to complete a project and reap the benefits of the solution. For example- Your company is adding new capabilities to its SAAS solution. It enables customers to close deals faster. Sounds easy enough to understand? The challenge though lies with measurement. How do you measure the end of a project which is when the expected value has been created for the end user. In this case for the value to be realized the product has to be developed, launched and sold to the customers. Then the customers have to implement the changes, train their staff, use it and close deals using the new capabilities. Only when all this is done that the TTV is realized.
Your goal should be to reduce TTV 😀!
ROI is the benefit you get from an investment. As a product manager you will be calculating ROIs while prioritising different features. An important thing to remember here is that the ROI’s will have a lot of underlying assumptions backed by historical data. Make sure to identify each of them and how it impacts the final calculation. This will help you improve your predictions overtime.
Some of the key inputs in your ROI calculation will be:
We have all heard about viral videos. You see something on facebook. You find it hilarious. You forward it to 10 friends who have a similar sense of humor. They do the same. Before you know it the total number of people who’ve seen it has risen exponentially.
Total Views = You(1) * 10 (Your Friends) * 10 (Their Friends)…
This is what virality is. How does this work for your product? Your customers love your product. They refer it to their colleagues and so on. And it sets of a chain reaction. All of a sudden there is a surge of people signing up for your product through referrals and other forms. Add the following to your product to tap into virality.
Virality Factor Formula
Virality Factor (V) is expressed as a ratio.
V = X/Y where,
X = Numbers of new users that signup through existing users
Y = Number of total users
Wow! That was a really really long post 🤪🤪🤪 One of the longest that I have ever written.
This post speaks to the wide variety of measurement metrics that are used across the board. I want to reiterate what I said in the beginning again. DO NOT start measuring all of them for your product. That is not the point of this exercise.
Once you go through these 30 metrics take a moment to reflect on your product management experience. Would any of these metrics have made sense for your past work? Would they have helped you improve your understanding of your users? I’m sure you will find some that would be beneficial. And that’s part of why I wrote this blog. You don’t have to reinvent the wheel. There is a ton of research that has already been down around these metrics. You just need to find ones that make sense for your tea.