Segmentation lets you see how product usage impacts metrics such as Customer Retention Rate, Trial-to-paid conversion rate and Customer Lifetime Value (LTV).
Segmenting your metrics by product usage enables you to measure the correlation between user actions in your product (e.g. feature usage, login frequency, etc) and subscription metrics such as MRR. This also lets you compare how certain features or actions impact your conversion and retention rates.
This article is broken into two parts:
Tracking product usage
Unless you're already tracking product usage on your website, you will need a developer to help you set this up.
Let's say you run an online streaming service like Netflix and you wanted to see if customers who watch 'House of Cards' were more likely to churn than customers who watch 'Daredevil'.
To do this, you could track if customers had watched the shows and then add an attribute to them in ChartMogul. For example, if a customer watched House of Cards, you could add the attribute 'Watched_House_of_Cards = True' to the customer in ChartMogul.
Once you add an attribute to a customer, it will appear on their customer profile, and in the 'All Filters' drop-down menu.
You can track product usage with analytics tools such as Google Analytics. By adding product usage attributes to customers in ChartMogul, you can see the impact of product usage and features on metrics such as MRR, LTV, Customer Churn rate, and Net Cash Flow.
If you're not already collecting customer-level product usage, you'll need some help from a developer to start tracking customer attributes. You can track product usage using Ahoy.js, Purser.js, or with a custom script. You can add attributes to customers using the Enrichment API. You might find it useful to refer to the following article: Tracking marketing attributes with Purser JS.
Segmenting metrics by product usage
Once you have added product usage attributes to your customers, you can filter and segment your metrics and customer base by product usage.
Customer lifetime value (LTV) by feature usage
You can see which features are associated with a higher LTV to drive future product development. If customers that use a particular feature set have a much higher LTV, this might indicate that expanding on the feature would increase revenue. Alternatively, driving wider adoption of this feature set might increase the LTV for your wider customer base.
As before, using the method described above, add feature usage related attributes to your customers. For example, Twitter might add the attributes 'number of times tweeted' (Integer) and 'account linked to Facebook' (Boolean) to their users.
To see if feature usage is associated with a higher LTV, follow these steps:
- In ChartMogul go to your LTV
- Click Add filter and select (e.g.) Number of connected_devices - is less than or equal to - 4
- Click New segment and then click Add filter and select (e.g.) Number of connected_devices - is more than or equal to - 5
In the example below, we can see that customers that have fewer devices connected on Netflix on average pay less than customers that have more than 5 devices connected.
By driving for a higher feature adoption of the number of devices connected, it's possible that the LTV for your entire customer base would increase.
Trial-to-paid conversion rate by product actions
You can improve your Trial-to-paid conversion rate by finding which actions are usually taken by leads who converted to paid.
Using the method described above, add onboarding-related integer attributes to your leads and customers, such as the 'number of logins', 'age of the customer' or 'average income'. These will be specific to your product. For example, Twitter might add the attributes 'number of active logins' and 'number of followers' to their users.
To see if a product action leads to a higher trial-to-paid conversion rate follow these steps:
- In ChartMogul go to your Trial-to-paid conversion rate chart
- Click Add filter and select Average_minutes_per day - is less than or equal to - 60.
- Click New segment and then click Add filter and select Average_minutes_per day - is more than or equal to - 61.
In the example below, we can see that trial customer who watches less than 60 minutes of Netflix per day convert less than the trial customers who watch more than 60 minutes of Netflix per day.
By adding onboarding attributes specific to your product, you can learn which actions are most frequently associated with leads that convert into paying customers.