Product usage data presents information on how customers are currently using your product. The analytics relay how customers use your product features. These directly impact company revenue by providing information that feeds strategy.
Strategic management relies on drawing insightful conclusions and meaningful observations from data and analytics. Product usage metrics are one of the sources of data. They relay key statistics on consumer journeys, user engagement, retention, and conversion rate.
These usage metrics are a superpower for businesses to stay competitive, reduce costs, find patterns of success, and increase their bottom line. Once a business can provide irrefutable evidence in support of an action, the probability of a project’s success multiplies.
If you’re a business looking to build the most engaging product on the market, you need to understand your usage metrics.
What is product usage?
A product usage metric is a data point that provides accurate information on how your users interact with your product. These include metrics such as usage frequency, feature adoption, and churn rate. As they gauge the quality of consumer interactions, these metrics pinpoint problem areas for users, such as:
- Where they get stuck
- What makes them dive deeper into the sales funnel
- Which features they spend the majority of their time on.
What insight about the product and user experience (UX) can metrics reveal?
Mockups and prototypes are created based on informed hypotheses of how the end user might interact with the application. No matter how historically accurate, these assumptions may not hold true in future cases. To find out how well these suppositions hold, you’ll need the correct, specific data.
Metrics come in after the product has been released to the consumers.
As you gain a deeper understanding of the consumer journey, you’ll begin to incrementally understand consumer behavior and create products and services that better appeal to your target audience. These insights could either be business-related or engagement-related while presenting data in a quantitative or qualitative way.
Examples of business-related metrics include recurring revenue, churn rate, customer acquisition cost, and customer lifetime value while engagement-related metrics cover product-centric measures that include adoption rates, active user counts, conversion rates, and promoter scores.
Unfortunately, there’s no one way to come up with a combination of metrics for you to use. No one has even agreed on universal metrics that should be used for all projects across industries! As each project is different, so should be the approach to measure success.
Why should you monitor product usage metrics?
Generally speaking, product usage metrics are used for these two arguments:
- Inform future plans and strategies
Different product metrics are useful in different scenarios. Whereas some offer a high-level overview of the product, there are others more concerned with the details and focus on minute aspects of individual features.
Whether these metrics track revenue, adoption rates, or active users, they all come together to help business leaders propose plans, strategies, and features that improve the overall product.
- Enhance customer experience
In the process of monitoring product usage, you directly find ways to improve customer flow and experience. What features get visited the most? Which pages lead to conversions? How many active users does your product have?
Answering these questions captures your product’s utility in the real world. It also informs product teams on what they need to improve to increase the amount of loyal customers and general customer engagement.
Contextually, product metrics could also be used to evaluate a launch’s success, test product hypothesis, measure a features’ impact, evolve product strategy, or segment a market. Any one of these goals will fall under one of the two arguments mentioned above.
Because product usage metrics have clear use cases, continuously tracking and monitoring these data points would give any business a clear-cut advantage.
Qualitative vs quantitative data
Aside from being business-centric or product-centric, product metrics could also be subdivided into qualitative or quantitative data.
Product metrics are very important to the business team and designers to gauge a product’s adoption and usage. However, these metrics should also work together with other important methods that can reveal qualitative data.
Interviews and usability tests will show the real user experience. These all together will hep you understand your customers comprehensibly. That’s crucial for business and its strategy.
Product usage metrics
How do you choose the correct metrics to follow? First, you’ll need to know your options.
Here’s a list of six product usage metrics to look out for:
Time spent in usage
This is the amount of time within a number of users who are using a whole product.
For different products this time will differ, as success results. Some products are meant to keep users in as long as possible (ex. Instagram, Facebook). There are also products where users should take important actions and a long time spent there may be a signal that the flow is not clear enough for the customer.
Additionally, other products were meant to funnel customers into the next page, so a long time spent in usage would mean inefficiency or a marketing message getting lost in translation. In either example, it is crucial to know how long customers are using the product.
Measuring time spent in usage is a simple formula of taking the total time spent in-product by all customers over a period divided by the total number of unique log-ins in that same period.
In practice, it looks like this:
Time Spent in Usage = Total Time Spent / Total # of Unique Logins
Total Time Spent = the total time spent by all customers on a product or feature over a pre-determined period
Total # of Unique Logins = the number of unique customers accessing a product or feature over a predetermined period
Either way, this metric provides valuable insight on how customers are currently using your product.
This metric represents the amount of time a number of users are using a specific feature. It provides concrete insight into how users interact with a particular feature and gives developers the ability to deduce the most commonly used features and prioritize development.
By tracking this metric, product teams are able to make informed decisions on priority areas and plan future development based on current consumer trends; all while cutting down costs on development for lesser used products.
Measuring feature usage can be done by dividing the number of monthly active users by the number of user logins. Multiplying this ratio by 100 will give us the usage percentage and will give insight on which features matter for different user persons.
Here’s the formula for Feature Usage:
Feature Usage Rate = (# of Feature MAU/ # of user logins) x 100
# of Feature MAU = total number of monthly active users for the feature
# of user logins = total number of user logins for that month
If, for example, a product has 1,000 user logins in a month but only 100 actively use a feature, you could then compare this 10% feature usage rate to industry standards and the rest of your feature offerings to determine whether this feature is worth further investment.
The activation ratio shows how many users reached the activation moment. These are the users who have not only signed up, but also reached the moment when they realized the value and have a contact with the main features.
Activation rate could also be called: “Activation”, “Aha moment”, “Point of activation”.
Measuring activation rate requires you to track the number of users who’ve crossed a milestone
Here is the formula for the product activation rate:
Product Activation Rate = (# of activated milestones / # of signups) x 100
# of activated milestone = number of users who reached the milestone for activation
# of signups = total number of users who signed up in the last period
Time to value
Time to value is the time that takes for a user to get to either the “Activation” (product’s main value), the minimum value for conversion, or to the “Aha moment”. So the faster the user can see the value the better for the product.
We should remember that customers are more likely to continue using the product and not to quit when they can see the value of it very quickly.
To measure the time to value, we first need to define the end goal of a consumer – what value are they supposed to get out of the product? Whether it’s to sign up for a newsletter or to make a sale, action-oriented metrics provide concrete and reliable information that are easily tracked.
From here, it’s a simple question of how long it takes for customers to reach this activation point. This could be tracked through time spent on page or time since the customer was onboarded.
Expansion MRR rate
This expansion (monthly recurring revenue) rate metric shows the rate at which a company’s revenue grows monthly (as comes from the name). Expansion MRR indicates additional revenue that comes from the existing customers only. It’s a revenue that doesn’t count new customers.
Tracking expansion MRR can help companies understand the growth of their customer base and identify trends and patterns in customer behavior. It can also help companies forecast future revenue and make informed business decisions.
Expansion MRR is often used in conjunction with other MRR metrics, such as “new MRR” (the MRR from new customers) and “churn MRR” (the MRR lost due to churn or downgrades), to get a complete picture of a company's revenue growth.
This metric could be calculated as the sum of product upselling, cross-selling, and add-ons.
Upselling is the concept of a customer purchasing a more expensive version of a product. Free to paid conversions are also considered upselling, which serve as a basis for how freemium business models operate.
Cross-selling is the occurrence when customers purchase additional products or services related to an already-purchased product. Finally, add-ons are additional items that customers recurringly purchase on top of existing services.
Car insurance is an example of an add-on.
Expansion MRR rate could be calculated through the following formula:
Expansion MRR Rate = (Expansion MRRn - ExpansionMRR n-1/ ExpansionMRR n-1) x 100
Expansion MRRn= the Expansion MRR of the current month
Expansion MRRn-1 = the expansion MRR of the previous month.
Expansion MRR rate shows off a products’ growth rate in terms of expansion MRR. A positive MRR shows growth in offerings and means customers are interested, happy, and loyal to your services. At no customer acquisition costs, businesses love to see instances where the expansion MRR continues to grow.
Stickiness, also known as user retention or retention rate, is a measure of how well a product or service is able to retain its users over time. It is often used to evaluate the long-term viability of a product or service.
Stickiness is an important metric for companies, as it can indicate the level of customer satisfaction and loyalty.
A high stickiness rate suggests that customers are finding value in the product or service and are likely to continue using it. A low stickiness rate may indicate that the product or service isn’t meeting customer needs or that there are issues with the customer experience.
Measuring stickiness can be done through the following formula:
Stickiness = DAU/MAU
DAU = daily active users
MAU = monthly active users
If, for example, a business has categorized 200,000 users as monthly active users and 4,500 as daily active users, using the formula gives us a retention ratio of 2.25%.
While standard retention rates would differ across industries, it would be best for you to use your first stickiness ratio as the benchmark and retroactively compare retention metrics moving forward.
Netguru tips for working with metrics
Understanding the user, the problem and business goals help to choose and apply the right metric. This is to ensure that we measure exactly what we need, which leads to better decisions.
But the most important is to be flexible, draw conclusions and be able to adjust metrics that were chosen. In Netguru each project is special and we look for individual approaches to similar problems. Here are some conclusions we draw working with metrics.
Track multiple metrics
Don't rely on a single metric to assess the UX or performance of your product. Instead, track multiple metrics to get a more complete picture of how users are interacting with your product.
Picking your options
Metrics should be selected individually to each project; additionally, it’s worth to check and use product usage metrics during the whole product development cycle.
Chosen metrics should be rooted in product strategy and only then go gradually deeper to specific functions and features
More benefits and a much wider overview come from selecting metrics by the whole team engaged in building and developing a product instead of an individual (e.g. only by project manager/CEO, etc.)
One metric to control them all
In some cases, it’s worth to choose the main metric and combine it with additional one/ones that will be responsible for validating the value provided by the main metric.
Example: a blog with the number of users as the main metric.
If the metric counts only users number, the conclusions regarding the general blog performance can be faulty, as the high number of readers can be ‘achieved’ with clickbait titles followed by poor content quality.
As a result, the number of readers will be high, but they will leave after just a few seconds, the content won’t convert and won’t bring assumed results; to cross-check this, the number of readers has to be validated with another metric, like time spent on site.
Determine your KPIs
Define success results for each metric before you apply it. This helps to avoid matching results in your favor. Without well-defined success results you won’t be able to validate whether the results are satisfying for product performance plan or not
Defining the sample size
Defining metric results in percentages can lead to a misguided understanding of the results. For example, a 90% satisfaction rating sounds very impressive; but if only 10 people were asked, then this would be a misleading representation of the population. Instead of percentages, you could use the actual numbers.
Use benchmark data
Compare your product usage metrics to industry benchmarks or data from similar products to see how your product compares.
Use qualitative data
In addition to tracking quantitative metrics, make sure to also gather qualitative data from users through surveys, interviews, or focus groups. This can help you understand the reasons behind changes in product usage metrics and identify specific UX issues.
Test and iterate
Use product usage metrics as a guide for testing and iterating on the UX of your product. Make small changes and track the impact on usage metrics to see if the changes are having the desired effect
Product usage metrics are a business necessity
Having systems in place to track the correct usage metrics will provide you with valuable insights and data that drive strategic decision-making and logical goal-setting. The data has always been there, it’s a matter of taking the extra steps to make sure they’re tracked.
Product usage metrics are a superpower in and of itself. As a business, usage analytics do one thing and one thing well: sharpen your value proposition.
They inform you on the best ways to optimize your app, increase adoption, decrease development costs, and streamline the customer journey. Design services provided by an experienced team will help you identify important usage metrics and design the solution that will deliver meaningful insights for your business.