Customer engagement metrics are important because they show how your product strategy aligns with user interest.
Furthermore, customer engagement is related with overall profitability, as engaged users are more likely to buy, become repeat customers, and share the product/service with other people.
Before you decide on the specific user engagement metrics you want to track, you have to determine which ones make the most sense for your business.
Here are some of the most common (and most important) user engagement metrics.
Product adoption rate is similar in concept to license utilization rate, but rather than simply telling you whether customers are using their licenses, it tells you how actively customers are taking actions such as logging into their accounts or using specific product features. This directly correlates with how engaged they are with your product on a daily basis.
Some examples in different products are:
Red Flags to take note of in most products are:
Once you figure out the positive and negative behavior in your app, it is time to figure out who your ideal user is.
Your ideal user or power user is in your product regularly because it helps them get their job done better than any other solution they have tried. They are valuable resources to tap when looking for product feedback.
These users will swear by a specific feature of yours or the so-called power feature. You will find the more users who engage with this feature, the more they realize your product’s value.
Once you identify your power users, you will have a specific persona based on their role, behavior, and interests to create your product around. You will also know your key differentiating factor from the competition and should try to get every user to test it out.
So now you know what positive behavior you want to encourage, what you want to avoid, and who you are building your product for.
A great way to track and measure your progress in fostering this behavior is by using retention and action cohorts.
A cohort is essentially a chart focused on two data points. A retention cohort would be measuring the session frequency and the time after a new user signs up.
This user engagement metric determines which channels your users are originating from. Acquisitions can come from so many places, like organic search, direct, paid search, referral, social media, and countless others.
By figuring out where your downloads and interactions are coming from you will have a better idea of how to attract new users.
Think of retention rate as the opposite of churn rate, in that it measures the percentage of users returning to your app in a given period of time. By calculating your retention rate over days, weeks, and months, you will have a good idea of the longevity of the app in the market.
Retention rates let you track and watch out for problem areas of your app that can be improved upon. It is a continuous process, so be sure to keep up to date to make sure you aren’t falling behind.
Additionally, apps on average retain only 40% of their initial users after a month. Use this as a benchmark for your own app. It also is important to keep in mind that acquisition costs are on the rise in the industry, so it will pay for you to focus on retaining users over going after new ones.
Again, different from session length, time in app shows the amount of time a user spends in your app in a given interval. Time in app is a user engagement metric that’s relatively easy to understand. T
The more time users spend in your app, the more engaged they are, the better they like your app. Simple as that.
However, if you do want to take a deeper look, segmenting here can be of great value. For instance, there may be a group of users that has not been spending much time in the app for one reason or another.
Ask questions of them and your development team to find out what’s causing this and decide if it’s worth fixing or will pan out on its own.
Customer health score combines multiple metrics into an overall measure of engagement and satisfaction which helps you predict how likely an account is to renew or churn. A common misconception is that customer health is calculated with a single score like NPS. A true customer health score is a combination of metrics. It consolidates data on variables such as license utilization, product usage, customer success outcomes, customer feedback, and support volume. For the most accurate customer health scores, customize each based on your customer segments. Different types of customers want different things. The aggregated result is displayed as a color code. Green indicates an account is doing well, while yellow indicates underperformance and red indicates that the account requires attention to avoid churn. Understanding the importance of why an account is in either green, yellow or red can help your team know which actions to take to improve their overall health score.
Another important type of engagement to track is customer support metrics. Customer support KPIs can tell you a range of information about your customers. For example, a high volume of support tickets reflecting difficulty with onboarding may be a warning sign portending low adoption and high churn. On the other hand, questions related to product features may be a positive sign that customers are actively using a product.
You can track support both by measuring sheer ticket volume per customer and by segmenting specific support issues. For example, you might identify how often a customer’s support tickets were resolved on first contact or the average response time for resolution. In contrast, you could measure how long a support ticket has been open, which can be proportional to a customer’s frustration. Or you might use your customer journey map to divide support issues into onboarding, adoption and renewal issues.
Session frequency or session interval is often confused with session length. However, session frequency is simply the time in between two consecutive sessions.
While session length shows you how engaged your users are while in the app, session frequency shows how often your users are coming back for more.
When monitoring session frequency, keep an eye out for trends in how people use your app. If they only use it during certain times of the day, ask why.
User engagement metrics are only as valuable as the questions you ask of them. Use the answers to guide your decision making,
While product-led growth (PLG) and sales-led growth (SLG) may seem like they are at odds with each other, they can actually work together to capture full business' potential.
Connect journy.io to Segment to collect product traits and events, and send product-led growth (PLG) scores, signals, events and other metrics back to 400+ Segment destinations. In real-time.
Following about two years of research and model testing, we're proud to finally release Smart Signals ✨, small logical indocators that reflect the state of each account/user within a PLG motion. Leveraging Machine Learning 🤖, yet flexible enough to be altered by customers, they eventually shape the foundation for **automatically** detecting signups that are most likely to buy, to expand, or the churn.
Changing the way you do business, case by case.
Detect which signups are most likely to buy. Sell more with less effort.
Automatically surface product qualified leads.
Prioritize PQLs call lists and engage with quick actions.
Add tasks and full PQL context to existing CRM and other engagement tools.
Automated sales playbooks and collaborative inbox.
Onboard. Monitor. Get expansion signals. Reduce churn, proactively.
Automatically detect churn & expansion candidates.
Accelerate onboarding and product adoption.
Align activities around 360° customer view, with health and onboarding scores.
Automated CS playbooks and collaborative inbox.
Build revenue workflows, based on how people use your product.
Use machine learning to uncover new sales opps.
Add slow accounts to nurturing campaigns.
Optimize engagement playbooks for maximum conversion.
Leverage any data without needing engineering.
See which impact your product features have on revenue and churn.
Analyse feature importance, usage and impact.
Build key product metrics without SQL, nor coding.
Easily create customer segments based on any product interaction.
Comply to GDPR and CCPA.
Create your free account and start driving a product-led growth strategy with the tools you're already using.