I spent the first year of my career as a Product Analyst at one of the fastest growing Fin-Tech startup of India. Trust me, when I say fast, it was really really fast.
I joined the team with no specific knowledge of product or analytics. I knew the tools, languages etc, but not how to do the work. How to use those tools to come up with insights that can help us build better products?
Over time, I learnt things and actually became quite good at my job. But it took some time to reach there. I am sure that just like me, a lot of you might struggle with that too. Hence, I decided to write this guide so that you don't have to learn everything from scratch like I did.
What does Product Analytics even mean?
Product teams spend months working on products that they want to be successful and to be adopted by their users. Once the product is launched, everyone awaits for the response from the users to see how well is the product solving the problems.
But, how do we know it is actually solving the user problems?
How do we really know that a lot of people are using our product?
If it is a digital product, we can track this using numbers and giving them terms like “number of signups”, “number of downloads” or “number of transaction”. You must have heard these terms right?
This act of measuring the performance of a product using these numbers is what product analytics means. These numbers and these terms are basically called product metrics and tells us everything about our product's adoption.
Introduction to Product Metrics
Before we jump into product metrics, let's first ask — What is a metric?
A metric is anything that can be quantified and measured in a specific number, and hence be compared over time.
When we have a product, we want it to deliver some value to the users. Understanding user behaviour and how they use the product can tell us if they are getting the value or not, right?
We study this user behaviour by mapping the product value to specific metrics that we call Product Metrics.
Product metrics refer to a set of data and indicators that reveal details about how users are interacting and responding to your product. Tracking these metrics can answer questions such as which features customers are using most, how long they are using a feature, what makes them stop using it, etc.
A helpful tool in measuring your product value is defining the so-called “value moments” — user events or actions that indicate that a person is getting value out of your product.
Your product's value moment is an event, an action, or a series of events and actions that represent the moment that a user found value in your product. Here are a few examples of value moments:
- For a dating app, the value moment could be liking a photo or sending a message
- For a food delivery app, the value moment could be placing an order.
- For a therapy app, it could be to take a therapy session.
So, we try to quantify these value moments and measure these as metrics.
Metrics that you should know
Awareness Metrics:
- How many people are interacting with your product?
- Example: Number of website visits, social media metrics (number of likes, shares, impressions, reach), time spent on a website, email open rate
Acquisition Metrics:
- How many people are interacting with your product?
- Example: Number of leads, number of qualified leads, sign ups, downloads, install, chatbot interactions
Activation Metrics:
- How many people are realizing the value of your product?
- Example: Number of connections made, number of times an action is performed, number of steps completed
Engagement Metrics:
- What is the breadth and frequency of user engagement?
- Example: Daily, weekly and monthly active users, time spent in a session, session frequency, actions taken in the product
Revenue Metrics:
- How many people are paying for your product?
- Example: % of paid customers; average revenue per customer; conversion rate of trial to paid customers; number of transactions completed; shopping cart abandonment rates; ad-metrics like click-through-rate and conversion rate (crucial for ads based businesses)
Retention Metrics:
- How many users are returning to your product?
- Example: % of users coming back to your platform each day, month, year; churn rates; customer lifetime value
Referral Metrics:
- How many customers are becoming advocates?
- Example: Net Promoter Score, viral coefficient i.e. the average number of people that your users refer you to
I have covered all of it in this image, feel free to save it.
How do we track these metrics?
Imagine you are a product manager at Facebook, and you want to track your user's behaviour. For example, you want to know how many people signed up on a specific day. How can you track this?
Whenever someone creates an account on Facebook, the data gets added to the database. You can query the database to get the number of user who signed up on a specific day. Congrats, you just tracked one of the most important metric. 🥳
But, not every action that user does is added to the database. That profile picture click your user just did on their friend's profile can't be added to the backend database. So, it's hard to track using the same method.
But we still need that information. It could be important. So, what should we do now?
What if there was a place where you can send the record of all these actions that users do on your platform? Like a log of all these actions being recorded in a book. Sounds like a good idea, right?
Creating and Tracking Product Events and User Behaviour
Well, that “place” we mentioned above exists. It's called a Product Analytics tool.
We have lots of different product analytics tools that let you track everything that the user does on your product and record that data using these logs. All such actions that user performs are called “Events”.
Not only this, nowadays these softwares even allow you to see exactly where the user scrolled, clicked and for how long as screen recording and heatmaps. Basically, you can track everything that your user does on your platform to understand them completely.
But how would this exactly help us?
Lemme give you an example. Let's say you work at Amazon and you just found out that suddenly, less people are buying products from you compared to last week. Why would that be? Are people not coming to the website? Are they not visiting the checkout page? How can we know all this?
If we track user behaviour using product events, we can know how many people land on the checkout page and in which exact step of the process do they leave. This can be really helpful as this acts like a hint towards a bad UX or a technical problem that user might be facing. Without the product events tracking, we would never be able to know that.
Tracking user behaviour and product events can help you:
- Take data driven product decisions
- Increase the user retention by removing blockers
- Improve the user experience
How to track these product events and user behaviours?
- Google Analytics
- The most basic web analytics tool you can accommodate in your website is Google Analytics. It provides you with the most basic information like “number of users”, “average time spent”, “bounce rate”, “clicks” etc.
- There are many alternatives to Google Analytics that do web analytics.
- Product Analytics
- Mixpanel is a product analytics tool that allows you to add custom events to your product by adding a small snippet of code for each event and placing them carefully at where they would be triggered.
- There are other tools like Mixpanel, Clevertap and Amplitude that allow you to track page views, button clicks and other custom events.
- Heatmaps
- Heatmaps are used to track the volume of user interactions on different parts of your product's interface like landing pages, product dashboards etc. using different shades of color, ranging from blue (light) to green (heavy).
- Tools like Microsoft Clarity and Hotjar allows you to track user's interaction on your website and see which section is mostly used by them.
- Session Recordings
- Many tools allow you to capture a real-time video of user sessions so that you can watch and observe exactly how your users navigate through the product.
- Both Hotjar and Microsoft Clarity provide that functionality.
How to come up with user behaviour insights?
Product Analytics tools give us a lot of superpowers. Suddenly, you can get to know everything about your users and what they are doing. It's amazing. Here are some of the superpowers you can and you will use as Product Managers:
Funnel Analysis
Funnel analysis is a method that is used to analyse the events that are being done by the user and in what order before they complete a milestone, a transaction or any target event.
It lets product and marketing managers understand user behaviours, the path they took and the obstacles they encountered throughout the customer journey.
As an example, let's say you're trying to convert free trial prospects into paid subscribers. Your funnel might look like this:
- Step 1: Prospects open an email and discover an offer for a free trial.
- Step 2: They click on a CTA button to redeem the free trial.
- Step 3: Prospects create an account and use your product for free.
- Step 4: Prospects convert to paid customers after the free trial expires.
Many distractions or barriers can happen in between each of these steps, and there are likely patterns of behaviour that can tell you what's working and what's not.
How to interpret and use data from Funnels
Let's take the example above. We can visualize the data for that funnel like this:
- Emails Sent - 1000
- Emails Open - 300 (70% drop from previous step)
- CTA button clicked - 150 (50% drop from previous step)
- Free account created - 100
- Paid Customers converted - 20 (20% conversion from last step)
Now this is a funnel and we can do certain analysis on it to understand the conversion on each step and see which step can and should be improved the most.
Conversion data in funnels might look like this:
The image above is a funnel analysis screen from Amplitude - a product analysis tool.
Seeing conversion data as funnels tells you a lot about which step has the biggest drop offs and you can then dive deeper into why so much drop off is happening on that step.
Example: Patreon Increases Subscriber Conversions with Funnel Analysis
Patreon provides creators, artists, and entrepreneurs with the opportunity to earn a living through donations.
Users can “pledge” donations to creators on Patreon's platform, and when creators win, Patreon wins. Patreon faced a conversion challenge—they needed to find new ways to incentivize monthly subscriptions to creator content.
Patreon discovered an opportunity to improve the pledge flow funnel through funnel analysis chart. They wanted more clicks on the pledge button. Patreon tested a new feature called “blurred posts” to encourage more users to click through the pledge flow.
These blurred posts concealed a portion of creator content, enticing users to delve deeper into the pledge flow funnel and ultimately subscribe. The result? Patreon was able to double pledge conversions on creator pages.
User Segmentation or Cohort Analysis
In cohort analysis, you take a group of users, and analyse their usage patterns based on their shared traits to better track and understand their actions. A cohort is simply a group of people with shared characteristics.
For example, let's say you had a cohort of people who enabled push notifications during their first session. By comparing that cohort to another cohort, such as all active users who don't have push notifications enabled, you can see whether that action affects how the users engage with the platform.
The 2 most common types of cohorts are:
- Acquisition cohorts: Groups divided based on when they signed up for your product. Typically, the shared characteristics of this group of users offers an opportunity to measure retention and churn rates within a specific timeframe.
- Behavioural cohorts: Groups divided based on their behaviors and actions in your product. This type allows you to view your active users in different demographics and with different behavioural patterns.
Tools like Amplitude and Clevertap give you option to create different segments and cohorts based on various behavioural traits. Once that is done, you can easily check how these different segments are using the product and compare them.
This image is one such example of how the behaviour of 2 different segments of users is different for the same feature.
Example: Understanding New (and Underused) Feature Adoption by Calm
Meditation app Calm wanted to test their reminder feature. They noticed that a small set of highly engaged users actively used the feature, but the feature was buried in the settings menu.
Calm wanted to know if the reminder feature was helping increase engagement or if the users who were dedicated enough to wade into the settings were just already highly engaged, regardless of the reminders.
The meditation company ran a test in which select users got a prompt to set a reminder after their first meditation session.
Using behavioural cohort analysis to compare those who set a reminder with all active users, Calm was able to see that using the reminder feature increased engagement across the board and not just for those users who explored the web of menus.
Retention Analysis
Retention analysis simply means analysing how many people are coming back to use our product after some interval of time.
For products, one of the most important metric is the retention. We don't want our users to forget about us and never to use the product again. We need them to stay on our product and come back to use it regularly.
So, we keep a track of this using retention analysis.
This is what a retention analysis chart might look like for a product. This is telling us that out of all the people who were acquired on a specific date, how many of them came back on Day 1, Day 2, Day 3 and so on.
Tools like Clevertap, Mixpanel and Amplitude provide this information
Benefits of Retention Analysis
- Understanding Customer Behavior
- Improving Customer Retention
- Increasing Customer Lifetime Value
Where do we go from here?
Well, this was like analytics 101 for you. Product analytics empowers product managers and the business to optimise their products and drive product growth effectively.
Whatever we discussed above is good enough for you to start getting better at your job. But to actually get better, you will need to practice.
So, go and practice!!
