📄️ Introduction
Consumer analytics can mislead when their interpretation is carried into complex professional software unchanged. DAU/MAU ratios, session length, conversion funnels and click rates describe activity, but they do not establish whether the work is going well. A 3-hour session isn't "more engaged" than a 20-minute one if the user spent 2.5 hours struggling with the interface. A high daily active user count does not mean your software is well designed if the organisation requires people to use it. An A/B test is only as useful as its outcome measure. Compare task quality, successful completion and avoidable effort when those are the questions at issue.
📄️ Understanding Your Users
Understanding your users requires evidence of who they are, what they can do and how their use of the software is developing. Personas built from invented details cannot provide this. The analytics in this section examine capability, experience and changes in usage, providing a basis for interpreting the other metrics you collect.
📄️ Learning & Expertise
In complex professional software, learning can continue for months or years. Usage data can identify changes in the methods people use and suggest where to investigate. It does not directly measure understanding, skill or the quality of their work.
📄️ Retention & Churn
Standard retention metrics, such as D1, D7 and D30 return rates, tell you that users leave. They do not explain why or where. Complex professional software requires more informative measures. People may stop because they cannot learn it or accomplish their work. They may also finish a project, change roles, lose access or move to another tool. Non-return does not identify the reason.
📄️ Workflow & Complexity
These analytics measure what users are building and doing, beyond simply recording their presence. Value depends on whether users can produce work of the required quality, retain control and avoid unnecessary effort. Project size and feature breadth help describe that work; they are not substitutes for evaluating it.
📄️ Analytics for Usability Evaluation
Usability Harmonics provides a structured way to evaluate complex software, but evaluations are periodic and manual. Between them, how do you know whether problems are emerging, interventions are working, or a new release has disrupted an established workflow? Analytics provide continuous, automated signals between manual evaluations.