In today’s world which is getting increasingly data-driven, one can’t successfully adopt a data-driven decision-making approach until key metrics are identified and tactics to measure those metrics are employed. There are plenty of tools that help us track such metrics and quantify the findings in the form of charts and graphs, so gone are those days where businesses and products were run on intuitions and assumptions.
Data is of utmost importance to every stakeholder of the organization, especially the product managers who need to keep a constant tap on the overall health of the product. But to derive sense out of such huge data, one has to find key metrics and KPIs that become the core parameter for measuring product growth and are in line with product vision, company goals, and customer needs. Moreover, utilizing custom software for visualizing such findings with charts, graphs or any other pictorial format helps the core team to grasp clear insights quickly.
Understand that the focus should be on only those metrics which matter the most to the product success and business objectives.
So what are those metrics and how do we make use of it?
Less is more. Try not to make sense of everything and then fall in trap of analysis paralysis. Metrics are not to beautify your reports but to actually bring your product’s performance on right path. So identify and focus on only few key metrics.
In such scenario, many times looking at a ratio or rate is better than absolute number, as it provides periodical insights and help you compare the performance over a month or a year.e.g. cohort analysis: you track a metric over different group of people, over a different period of time. If you are running a Google Adwords campaign for a week to increase your website traffic and also measuring the churn rate in terms of conversion, then you would like to use those numbers for another set of users for comparison. Use cohort analysis where each group of people visiting and signing up is a cohort.
Metrics for each stage of buyer’s life cycle:
You might question that there are lot of areas and facets which need analysis to improve the performance of the product and adopting two or three key metrics may not work to derive apt decision. In this case, identify one key metrics for every stage of the buyer’s life cycle and club that with product’s performance.
Below is the list of metrics that you can study and then identify one single metrics out of the list to focus at every stage of the life-cycle.
- Tracking number of installs and sources/channels
- Track your cost per install. It should never be higher than the user’s lifetime value (LTV)
- Number of uninstalls
- New users
- Active users and bounce rate
- API latency
- App load per period and network errors
Visualizing These Metrics –
- Time series graph is used to show similar types of data, where the x-axis represents the value of time and the y-axis, the metric values.
- Line graphs are the simplest way to translate metric data into visuals. For instance, a graph of visitors per application over a period of time (day, week or month).
Focus on how user is being engaged with your product and variety of features, based on your product’s stage in the growth identify one core metric that helps you grow by resolving errors.
- There are various types of retention:
- Day N Retention = Number of users retained on Day N(Depending on the definition)/ Number of users who installed the app on day 0(and could potentially be retained)
- Full retention
- Classic retention
- Rolling retention
- Return retention
- Bracket-dependent return retention
- Churn rate is the opposite of retention rate and is the measure of how many users stop using your app over a given period of time, usually a month.
- If your app has 100 users and only 15 users stop using your app, so this means you have a churn rate of 15%
- Understand how users are navigating in the app in what order, which are the functions they are performing on what screens and where are they getting stuck because of some friction.
- Session length
- Popular pages/ features
- Time spent within the app
- Number of time app is being opened and time between the app opens
Based on the user behaviour findings, you can integrate custom push notifications. Eg. If any user has installed the app and haven’t bought or performed any activity within 7 days of time, then send this particular email template to them to remind and entice engagement.
Visitors retention is a new metric that can help identify if your application is bleeding users. It also helps to determine whether your application is valuable to them or not by showing you how often they come back. Retention calculation can be represented in form of table which is also known as “cohort analysis”. Cohort analysis involves breaking customers apart into groups based on when they completed an actions.
Group Bar chart can be used to represent user flow metrics like Metrics of different actions per app.
Being a product manager or a key member of the top stakeholders, your major interest lie in metrics namely ARPU and LTV where you find the insights of revenue and customer interest for your product.
Goal conversion rate:
Track the goal conversion funnel at various touch points e.g. past purchases, In-app purchase
Average revenue per user:
ARPU is the revenue you generate, on average, from each user of your application, and this can be calculated by simply adding up the revenue your app generates each month, and dividing it by your total number of users
Life Time Value:
LTV, is the measure of the revenue a customer will bring during their lifetime of using your application.
To calculate the LTV you need below mentioned data points:
- Customer churn
- Income: all revenue from in-app purchases, subscriptions and advertisements
- Number of active users
Focus on KPIs and chase only most important key metrics that act as a set of vital signs for your product success. You can define different metrics for stakeholders, product team, managers, customers etc. Moreover, keep in mind that setting up such metrics is not part of the process after you launch your product. If you wait this long, you will miss the opportunity to measure your product growth from baseline.