In the first part of this series (measuring data related to users - Part 1), I started out by outlining more details about what analytics is, what is the kind of information that can be captured from users, some kind of information that should not be captured based on privacy guidelines, and what you can do with this kind of information. In this post, I will continue more on this line and provide some more examples of what can be done (the purpose of this series of posts is to describe more about what can be done with analytics through some real life examples that lets you know what to do through analytics).
Let us take the example that we use a lot, a greeting card application that allows the user to use their own photos or images in addition to standard greeting card background photos, allows them to use their own audio and videos, or lets them record them same from the camera and microphone on their computers, and also allows them to add their own text of greetings. The final collected greeting can be sent via email, or through social networks.
Now, the application designers are trying to figure out tweak related to the videos that users upload from their own machines. There are numerous video formats that users can be having with them, since there are many different capture devices. You could be shooting the small video clip using a mobile phone (that too can have a different video format depending on the manufactures of the mobile phone), you could have shot the video using a tablet, could have shot it using a still camera, or have shot it using a video camera.
The size of the video that has been shot depends on the shooting device, depends on whether the user has reduced the resolution of the video in order to reduce the size, or in some cases, decided to use the same video that would have been uploaded to Youtube (which means that the video would have been converted to a FLV video format). Now, for most of these formats, these videos cannot be used just like that. Coders / decoders need to be user for this purpose, and even though there are some open source solutions, there would be commercial software that could be used for this purpose.
The decision about whether to use open source or commercial software could depend on the number of users who would be using such a software. So, as part of the data gathering in previous versions of the software, it could be determined as to which are the video formats that users are using, and then based on this data collection, the proportion of video formats used can be determined. If it turns out that the number of users using a particular format is more than a certain proportion, the product team would determine that it would make sense to use the commercial video encoder/decoder rather than use the open source one. The advantage that users get out of a better software component be greater than the cost advantage of using an open source software. But unless you are getting such information through analytics, any decision you take would be flawed, based on a hunch rather than information.
Read the next post in this series (Measuring data related to user information - Part 3)
Let us take the example that we use a lot, a greeting card application that allows the user to use their own photos or images in addition to standard greeting card background photos, allows them to use their own audio and videos, or lets them record them same from the camera and microphone on their computers, and also allows them to add their own text of greetings. The final collected greeting can be sent via email, or through social networks.
Now, the application designers are trying to figure out tweak related to the videos that users upload from their own machines. There are numerous video formats that users can be having with them, since there are many different capture devices. You could be shooting the small video clip using a mobile phone (that too can have a different video format depending on the manufactures of the mobile phone), you could have shot the video using a tablet, could have shot it using a still camera, or have shot it using a video camera.
The size of the video that has been shot depends on the shooting device, depends on whether the user has reduced the resolution of the video in order to reduce the size, or in some cases, decided to use the same video that would have been uploaded to Youtube (which means that the video would have been converted to a FLV video format). Now, for most of these formats, these videos cannot be used just like that. Coders / decoders need to be user for this purpose, and even though there are some open source solutions, there would be commercial software that could be used for this purpose.
The decision about whether to use open source or commercial software could depend on the number of users who would be using such a software. So, as part of the data gathering in previous versions of the software, it could be determined as to which are the video formats that users are using, and then based on this data collection, the proportion of video formats used can be determined. If it turns out that the number of users using a particular format is more than a certain proportion, the product team would determine that it would make sense to use the commercial video encoder/decoder rather than use the open source one. The advantage that users get out of a better software component be greater than the cost advantage of using an open source software. But unless you are getting such information through analytics, any decision you take would be flawed, based on a hunch rather than information.
Read the next post in this series (Measuring data related to user information - Part 3)
No comments:
Post a Comment