An analytical technique that focuses on analysing the behaviour of a group of users over time, thereby surfacing insights about the experiences of those users, and what the organisation can do to improve those experiences. It is especially useful in benchmarking user engagement for a new service or feature or user journey refresh of an existing service.
This technique is only applicable where the average user interaction rate with the service is regular, e.g. at least monthly.
A typical trigger to use this technique may be the launch of a new service, new feature in an existing service, or major redesign of a key user journey.
👥Who
Product manager or data analyst
🛠 Running the technique
There are 3 key anchors to employing this technique: Define the cohort group.
Define the cohort group - the trigger event to start tracking - e.g. deploy new web site, new user journey - and the attributes for inclusion - e.g. existing users, use of mobile channel.
Define the time box, the lagging period, that you want to run your analysis for. A typical value would be one month.
Define the period on which to run the analysis. This should be long enough to identify any trends from the new deployment. A typical value would be 3 months.
Configure the tracking on the web analytics tool in time for the deployment of a key new feature or service.
Use the web analytics reporting dashboard to visualise the performance of each cohort group- weekly, monthly etc.
Identify unexpected trends emerging and prioritise these for more indepth analysis.
In depth analysis may be undertaken via the data analytics tools or qualitatively via user interviews to identify potential root causes.
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