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Monday, February 23, 2015

Tracking platform usage and making decisions based on that

Even in today's data based world, if you are an analytics expert, you can't expect to be totally popular, or that people will welcome you with hands outstretched. There are many aspects of a product development cycle that would benefit from integration with a data analytics cycle - generating the questions, collecting the data, and the extremely important task of generating the output information and conclusions (and a wrong conclusion at this point can have negative implications for the product - putting in effort in wrong or unimportant areas). However, consider the case where there is support for using analytics and there are resources dedicated for ensuring that requisite changes to the product can be made (based on the analytics output).
One major area where the team needs to gather information is on the area of platform support. Platform could mean the Operating System (MacOS, Windows, others), Versions (Windows XP, Windows 7/8, etc) as well as browsers support (versions of Chrome, Internet Explorer, Firefox, Opera, etc). These can make a lot of different in terms of the amount of effort required for product development. For example, on different versions of Windows, the versions of systems files are different and it is a pain to support these different versions. There can be small changes to the functionality of the system based on these files, and in many cases, the product installer would actually detect the Windows version and install the required versions of these systems files. If you could find out that the number of people using these different versions, and find out that one of these versions is being used by a small number of consumers, then the decision to drop that particular operating system may be taken.
So, this is one of the areas in which analytics can work. The simplest way to do that is to make it part of the functionality of the product, where the product, on the user's computer, dials home and provides information such as the platform on which the product has been installed. Once this kind of data has been collected and a proper analysis is done, then the product team can look at this information and factor that into the feature list for the next version of the product. The team will need to decide on the time period for which the data would be captured, and also some of the benchmarks which will decide whether data from the analytics effort needed to be used for making decision (for example, the data seems to be such that people feel that the data is widely different from public perception).
However, this is also dependent on how much the team can depend on this data and the analysis; after all, even a small variation during the analysis can result in information that has levels of inaccuracies in it. But, it is necessary that the team spends effort in the analytics effort, since the payoff from using accurate data analysis and interpretation is very high.

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