Here’s an interesting use of R outside the “usual” statistics domains: using advanced analytics to estimate how long a typical content-management system (CMS) remains in use. Some industry analysts cite a lifetime of 3 years, but can that estimate be backed up with data? To investigate, Michael Marth uses the customer records from a CMS provider, and looks at how long their support contracts were maintained (as a proxy for the system actually being in use). These data require a special kind of analysis, so lets take a look in detail. In the data, some of the contracts are still…
Here's an interesting use of R outside the "usual" statistics domains: using advanced analytics to estimate how long a typical content-management system (CMS) remains in use. Some industry analysts cite a lifetime of 3 years, but can that estimate be backed up with data? To investigate, Michael Marth uses the customer records from a CMS provider, and looks at how long their support contracts were maintained (as a proxy for the system actually being in use). These data require a special kind of analysis, so lets take a look in detail.
In the data, some of the contracts are still active: for example, the customer took out a support contract 4 years ago, and the contract has not yet been terminated. In statistics, this is called a right-censored data point: we know the contract will terminate eventually, but as of today, we don't know when. We do know that when it does terminate, it will have lasted at least 4 years. A naive analysis would just include this data point with a duration of 4 years, but that would bias the estimated average lifetime downwards. By the same token, we can't just ignore this data point either (not least because it would waste much of our data!).
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