Calculated Metrics - Best Practices

the calculated metrics feature offers significant capabilities for retrieving data from the system and merging various data points to create more precise and tailor made metrics for your organization calculated metrics categories there are numerous ways to utilize the calculated metrics feature to harness the data you’re sending to custify below, we’ve outlined several categories that may prove valuable simple metrics based on events/attributes if you are sending product usage data to custify, this data holds immense value for your customer success team it allows them to gain insights into how customers are utilizing your platform, identify usage patterns, and construct health scores, among other things some use case examples that can be useful are count the number of events within a specific timeframe calculate the number of unique individuals who generated events within a set timeframe count people who generated at least one of multiple events in a given timeframe determine the number of days since the last occurrence of a specific event utilize event metadata to create advanced metrics, such as summing event metadata, calculating averages of event metadata, or filtering by event metadata additionally, you can access attribute and metric values, both current and historical this functionality can assist you in various ways, such as calculating the time elapsed since or until a date attribute or tracking changes in attribute values examples of frequently used metrics number of logins in the last 30 days number of unique people that logged in in the last 30 days number of logins on the mobile app in the last 30 days days since the login was last performed current number of users – the number of users from 30 days ago (increase/decrease in users over 30 days) days until the renewal date days since the last touchpoint days since contract start date – date since onboarding was completed (=duration of the onboarding) maximum/minimum value of the metric “active users yesterday” in the last 30 days complex metrics based on combined data points similar to the previous category, you can build metrics in which you access different types of data – keep in mind that you can also combine data types in the same metric, to build more accurate or relevant metrics examples of frequently used metrics number of logins in the last 30 days / people count (= average of logins per user); (number of unique people who logged in in the last 30 days / people count) 100 ( = percentage of users that were active at least once in the last 30 days); (maximum/minimum value of the metric “active users yesterday” in the last 30 days / people count) 100 (=maximum/minimum % of active users in the last 30 days) normalized metrics as also advised in the https //kb custify com/health scores best practices article, we recommend using the calculated metrics to build as many normalized metrics as possible this means that instead of looking at the value of a data point, you’re combining other entities to obtain a result that would be equal across your customer base examples of frequently used metrics instead of looking at the actual number of used licenses, you can build a calculated metric that will show the % of used licenses out of the purchased ones ((used licenses/purchased licences) 100); instead of looking at the number of events in the last x days, you can build a calculated metrics that will show the average of events generated per user (number of events/people count); instead of building a health score based on the number of active users in the last 30 days, you can create a metric to calculate the % of active users out of the total product users ((active users in the last 30 days/people count) 100) trends in some situations, even after normalizing metrics, gaining a fair understanding of a customer’s health may remain challenging this can be especially true when your companies differ significantly in terms of size, the number of users, and the features they utilize in such cases, it’s valuable to monitor usage trends for instance, there might be cases where a customer is exceptionally active, and even if their usage decreases, they still generate more activity than other customers conversely, you may have a customer who typically generates a low volume of activity but remains content with the product in these scenarios, tracking trends becomes essential examples of frequently used trend metrics (number of logins in the last 30 days/number of logins in the previous 30 days) 100; (number of active users in the last 30 days current value/number of active users in the last 30 days value from 30 days ago) 100; (time in product over the last 30 days/time in product over the previous 30 days) 100 roll up child company data to parent company if you have a parent child relationship in custify, you can leverage the ability to consolidate data from the child entities to the parent entity while events are automatically aggregated, attributes and metrics from the child entities don’t impact the data of the parent entities if necessary, you have the flexibility to create your own aggregations through calculated metrics examples of frequently used metrics sum of product users across children companies; sum of the monthly revenue of children companies; average of the global health score of the children; count of children companies in a particular segment (e g onboarded children companies) metrics based on other custify entities an interesting use of the calculated metrics is to use the other custify entities to build metrics csms engage in various activities on behalf of their customers, and these actions can be transformed into meaningful metrics examples of frequently used metrics count the number of notes with the “client meeting” tag over the last 90 days; count the number of tasks with the “product training” tag that were completed over the last 90 days; count the number of emails you received from the client in the last 90 days; count the number of meetings you had with the client in the last 30 days; count the total number of hours spent in meetings for the last month; count the number of upcoming meetings; count the number of csm ratings given currency conversion another type of function that can be constructed using calculated metrics is currency conversion if you have revenue in multiple currencies, you can utilize metrics to standardize them to a common currency, ensuring accurate revenue related reports setting up calculated metrics you can check https //www custify com/kb/knowledgebase/calculated metrics/ to get additional information on how to set up the metrics things to keep in mind customize the recalculation frequency to align with your needs, whether it’s every 1, 6, 12, or 24 hours decide whether you wish to display the metrics on company 360 at all times, only when they have values, or never, depending on the intended use of these metrics when constructing metrics based on health scores, it’s generally advisable to utilize their normalized values rather than the raw scores leveraging the metrics once you’ve established your calculated metrics, consider these suggestions for making the most of them across the platform incorporate them into health scores as mentioned earlier, it’s often more meaningful to use trends or normalized metrics to monitor customer health, rather than relying solely on the actual values display them on the company 360 when they provide value to your csms utilize them as triggers for playbooks for example, when a specific trend descends, you can trigger a notification or task for the csm to review usage and re engage with the customer