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Wednesday, May 22, 2013

Analytics - Measuring data relating to user information - Part 3

This is a series dealing with analytics, and the advantages that it brings to the product team. However, any movement into the area of analytics requires a lot of careful thought, and needs time to be spent on the design of a strategy around Analytics. You cannot just say that you want analytics, and put in place some strategy. But when you do get your Analytics strategy right, there are a lot of benefits that are possible. In the previous post (Analytics - measuring data related to user information - Part 2), I talked about a scenario where a team wants to find out the video formats being used by its users in the application, and the benefits of making decisions based on this knowledge rather than making guesses (which may be right, or could be wrong). In this post, I will write more about the usage of analytics while making decisions.
Consider the previous post, which talked about which video formats are popular. However, consider that you need to make decisions about the future, which means that you need to do much more analysis about the data you are getting. So, if you have been in the game for many versions now, don't just look at data for the previous release. Instead, if you have been gathering such data for the past many releases, you need to make an effort to review this data for the past many releases in order to figure out the best possible method ahead. So, even though in the last post, we only reviewed the proportion of video formats that were in use, a better analysis would have looked at the proportion of video formats that the users have been using in the past few versions.
Over a period of time, such analysis would, in most cases, reveal trends that would be useful for the designers of the product, as well as the product managers to know. Till you would have done such an analysis, the way for you to learn such data would be by looking at industry data as well as research done in the forms of surveys and information from users through other means, but all of this is indirect data. Analysis of analytics data allows you to get confirmation on such information, and can help you make decisions that is also backed by hard data. 
A possible example that shows the value of such analysis would be about the usage of mobile devices. So, the product manager would have seen that there is a higher trend worldwide about using mobile devices for capturing such data, and then look at analysis of the data from the past many versions that talks about the source of the videos being used by the consumers of the applications. Consider a case where the trend shows a movement towards mobile devices being the source, but the trend is slow, only going up from around 12 % to 16% in the past 2 years. The question in front of the product manager was about diverting resources to producing a mobile version of the application, but that would require a large amount of code change and architectural and design work, and hence would have an impact on the current release. The other option would be to plan for a mobile version only in the next release, which would have a lower impact on the current release. Based on this data, the Product Manager might decide that although there is a lot of attractiveness in terms of having a mobile version, the data does not suggest that there is an emergent need to create a mobile version in the current release, especially with the costs of doing so. Instead, one can wait for such a release.
Taking such a decision is critical for the application, but being able to take such a decision without having data on consumer usage would mean a decision that is more like a guess-estimate, where some information tells that you can you take a decision, but the amount of data that you have is not adequate to produce a high level of comfort in taking such a decision.

We will continue this series on the usage of Analytics in the next post on this series.

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