Web 2.0 for Product Decisions – Using Google Analytics

Increased community participation on the internet (Web2.0) has significantly reduced the cost of reaching out to customers. User forums, blogs, mailing lists, LinkedIn/Facebook/Yahoo/Google Groups etc enable customers to seek information and help. As discussed in the previous post, Blog is a powerful medium for product companies. Blog audience may or may not be logged in. Comments are posted by handful of audience, while most viewers tend to be passive listeners. While feedback from comments is the most valuable, Google Analytics also provides rich amount of data for a product company to make intelligent decisions. Here are a few examples –

1. Information about viewers operating system, browser, screen resolutions, Flash versions – There are several websites or industry surveys which provide the system information for consumers or enterprises at large. However, if you have a blog focused on a particular product and expectedly, the viewers of the blog can be considered as potential or existing customers of the products. The data pertaining to the blog is hence more relevant when making decisions to support operating systems or browsers. For example, we used the declining share of Windows 2000 among the viewers to confirm the decision to drop support for this operating system. In addition, screen resolution enabled a choice of User Interface toolkit which required a higher resolution to reduce screen clutter.

2. Search keywords which lead viewers to the blog – Based on the ranking of keywords which viewers are searching for and the viewers that reach the blog by clicking on the results for these keywords provide a lot of data on positioning of the product. For example, we discovered “XML” and “XML authoring” as important keywords which led to more traffic for posts on FrameMaker. A blog is an important tool in positioning of the product – more important for a new product where perceptions can be changed with relative ease. Search keyword data provides an important indicator if there is a correction required in the positioning.

3. Leading referrers to the blog – There are several ecosystem partners we used to work with and they had their own websites. A large number of them posted a link to our blog. This data provides important information when combined with timing of peak traffic from a particular website and the context.

4. Top content viewed – This is very simple statistic and provides an important source of information. For example, we found that a post of Windows Vista support was most viewed (even after 1 year of posting). This also enables you as a blogger to understand what topics you should focus in your future posts.

There are several other statistics which can be looked for specific interpretation – for example, bounce rate, geographical distribution of viewers and so on. In forthcoming posts, I will discuss other Web 2.0 mediums and how they can be used for making product decisions.