How to use calculated metrics?
A metric by itself is just a number, not a KPI or an insight. “Calculated metrics” are dynamically updated values built on the data you send (API, integrations, or manually entered). With Custify, users can transform these values into meaningful insights and effectively utilize them in segments, dashboards, or health scores.
Create trends and identify usage patterns
Compare past and present usage data to identify changes in user behavior. Look at relative changes, rather than absolute values.
This helps you understand the evolution of product usage over time, which could be integral in spotting potential issues or opportunities for increased engagement.
Build benchmarks and normalize your data
Looking to compare segments and accounts over time? Benchmarks are a simple mechanism for normalizing data in order to achieve this.
Basically, these allow you to create a standardized reference point that facilitates a fair comparison, allowing for more accurate insights. For example, you can compare logins between large and small accounts.
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Here's a few examples of how calculated metrics could revolutionize your customer success strategy:
Gain insights into customer engagement by calculating the average number of live chats over the past 30 days.
Keep track of user engagement by analyzing daily or monthly login data and setting benchmarks.
Understand user interest by assessing download frequencies of various report types over 30 days.
Identify the highest and lowest changes in health scores over a week to pinpoint accounts needing attention.
Discover your most expensive customers to manage by dividing the recurring revenue by the number of interactions.
Compare current customer feedback volume with the previous period to analyze changes over a specific time period.
Contrast weekly and monthly average health scores to identify emerging trends or issues.
Detect drops in product engagement by comparing activity counts over the last 30 days against the prior month.
Detect early signs of disengagement by combining data from login patterns, support ticket trends, and feature usage.