I joined PBwiki last month as the first web analyst on the team. One of my key roles here is to analyze how people interact with their wikis so that we can craft our products and services to best meet your needs.
We can monitor PBwiki to see what’s working and what’s not
One of the greatest facets of having a “software as a service” (SaaS) model is that we have an on-going relationship with our users and can observe how they interact with our product. Compare that to the standard shrink-wrapped software model, where the vendor sells the product and then disappears from the customer’s sight until they want to sell an upgrade. The benefit, of course, of us knowing how you are using our product, is that we can enhance the product to better suit your needs.
Case study: How many users use Document Management functionality?
Before the new features
As a concrete example of how PBWiki analyzes user behavior to improve the value of our product, let’s look at our new Document Management capabilities. On any given day earlier in October, roughly 35% of active wikis were uploading files (see table 1). This adoption rate indicates that you find document management useful and that we need to focus product development effort on it. But at the end of the day, this number doesn’t give us much guidance in terms of what direction to take this feature. To make any decisions regarding the product, we also had to look closely at qualitative data. The quantitative data (i.e. the 35% adoption rate) lets our product team know what you’re doing, but the qualitative data lets us know why you’re doing what you’re doing.
Table 1 – Pre-feature enhancement adoption rate
After we released Document Management
After analyzing the qualitative data (e.g. user feedback), we realized the need for several new features (including access control), implemented them, and pushed them live near the end of October. So, how do we know if these new features were useful? We monitored the adoption rate and saw it jump over 10% (see table 2)! The upshot of this example is that at PBwiki, we listen to our users so that we can build the best products to solve their needs.
Table 2 – Post-feature enhancement adoption rate
Web analytics and your privacy
- If you mark your wiki private, we’ll keep it private.
- We don’t share personally identifiable information with others.
- We hate spammers, too. We’ll try not to bug you with email.
During any analysis, I will be sifting through the 1 billion events that our users have generated over the past few years. Because of the immense size of our data set, I work with anonymous and aggregate data. In the analysis of Document Management above, I included over 100,000 wikis and at no point did I need to drill into the specific details of any one wiki or user.
What kind of data would you like to see?