Well, actually, the topic of this post should be more like, When to drop support for the previous versions of the software. In the previous post (Supporting previous versions of the software - Part 1), we did a top level summary of the problem, and what are some of the approaches one should be following.
In this post, we will talk about the usage of data analytics to determine the number and percentages of users who are using previous versions of the software, and how to use these data analytics to work out whether to continue support or not. Say, if the data is able to determine that there are only 1% of users who are on a version that was released about 5 years back, then it would really help in the decision making. One of course needs to keep in mind that the data cannot be the only factor in determining the dropping of a support, but it is very useful to have such a data rather than trying to make a decision without this kind of data.
How does one get this kind of data ? Well, it is fairly easy to make the application to be phoning back whenever the user launches the application. The data phoning can be incorporated in various different ways - it can be incorporated to repeat the first time that the user launches the application, it can also track how many times each feature was launched in the application, for how much time, what were the workflows involved, and so on. This data coming into the applications data gathering mechanism can be tweaked to do whatever kind of analysis is required.
This data provides a great amount of input into the decision making process. For an application that has around 10,000 active users across versions, if there are only say 100 users working on an application version that was released 5 years back (and during the year of release of this version, there were around 900 users); it is possible to make a decision that support for this software version could be dropped. In many cases, the product team could try to entice these users on previous software versions by offering them a discounted upgrade path to the newest version.
However, using data analytics comes with its own challenges. There are many cases where there are challenges in data collection, or in data analysis. It needs to be very very sure that there are no errors during this process of data collection and analysis. And there are legal issues that need to be settled. This concept of sending data from the application needs to ensure that there is no violation of the privacy of the user (there can be heavy penalties in case it is found that privacy of the user has been violated). Hence, the functionality of this kind of data collection and data analysis would need to be cleared by somebody in the organization who has the authority to clear such kind of privacy potential issues (can be the legal advisor in the company).
Read the next post in this series here.
In this post, we will talk about the usage of data analytics to determine the number and percentages of users who are using previous versions of the software, and how to use these data analytics to work out whether to continue support or not. Say, if the data is able to determine that there are only 1% of users who are on a version that was released about 5 years back, then it would really help in the decision making. One of course needs to keep in mind that the data cannot be the only factor in determining the dropping of a support, but it is very useful to have such a data rather than trying to make a decision without this kind of data.
How does one get this kind of data ? Well, it is fairly easy to make the application to be phoning back whenever the user launches the application. The data phoning can be incorporated in various different ways - it can be incorporated to repeat the first time that the user launches the application, it can also track how many times each feature was launched in the application, for how much time, what were the workflows involved, and so on. This data coming into the applications data gathering mechanism can be tweaked to do whatever kind of analysis is required.
This data provides a great amount of input into the decision making process. For an application that has around 10,000 active users across versions, if there are only say 100 users working on an application version that was released 5 years back (and during the year of release of this version, there were around 900 users); it is possible to make a decision that support for this software version could be dropped. In many cases, the product team could try to entice these users on previous software versions by offering them a discounted upgrade path to the newest version.
However, using data analytics comes with its own challenges. There are many cases where there are challenges in data collection, or in data analysis. It needs to be very very sure that there are no errors during this process of data collection and analysis. And there are legal issues that need to be settled. This concept of sending data from the application needs to ensure that there is no violation of the privacy of the user (there can be heavy penalties in case it is found that privacy of the user has been violated). Hence, the functionality of this kind of data collection and data analysis would need to be cleared by somebody in the organization who has the authority to clear such kind of privacy potential issues (can be the legal advisor in the company).
Read the next post in this series here.
