Getting the balance right between hotline support and user documentation

Many organisations find it hard to know how much time, money and effort to put into supporting their users. There’s a competition, of sorts, between the Technical Support and Technical Publications departments over how much budget they should receive. In some organisations, these departments are also competing, in a way, with content generated by users and others outside the business.

One way of looking to answer this question may be to use Web Analytics from Web based user documentation and call statistics from the Support department to see of there is a self-regulating system in play between all the different forms of user assistance.

Let’s explain what we mean by a self-regulating system, by looking at the ideas of famed scientist James Lovelock. Lovelock argues the Earth is full of many positive and negative feedback systems that lead to a long term balance or equilibrium being achieved. To prove his theory, he created a computer model called daisyworld, to show how these self-regulating systems work:

With the move to Web-based documentation, and the ability to measure its use through Web Analytics, it’s now possible to determine whether there’s a correlation between the amount of documentation and the number of support calls. Where there is a large user base, you may find the number of support calls goes up or down depending on the amount of documentation that is available. You can then decide the most cost effective way for the organisation to allocate its budgets.

It may also be the case that the amount of user generated content developed by users correlates to how much or how little official documentation is available.

Indeed, with enough data, we may be able to identify what level of user assistance, in all its forms, is needed to sustain a typical software application.

One Reply to “Getting the balance right between hotline support and user documentation”

  1. It would be really cool if someday someone could distill it all into a plug-and-play algorithm. Go to the website and plug in your data to get a nice neat recommendation! Don’t we wish!

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