How analytics can inform WIP II?

Owned by Paul Seiler (Unlicensed)
Last updated: Apr 08, 2015
2 min read

An early WIP II task was to investigate the statistics and data that is currently available that could usefully inform our work. The following notes are based on my conversation with Keith, but include some thoughts I have had since and from looking around GA (as I now have access). I believe that we have enough data to act on (in ways described below) if there is a willingness to do so.

And this is a first draft and as such would benefit from updates and comments by others. Go for it.

 

Information obtained from the analytics could be used in WIP II in:

  • Making high level IA decisions: Allow accurate drafting of templates and content, with the minimum chance that rework is required.
  • Locating content: How far down is the most frequently used content located? Should we cull content if it is seldom/never accessed?
  • Selecting tags/search terms: How to improve the hit rate for searches.
  • Validating the key drivers for WIP II: Importance of mobiles, extent of duplication, etc
  • Measuring success: With KPIs the analytic data might help measure the impact of WIP II. This might lead to 
  • Prioritising new functionality: We are unlikely to be able to measure success using the current tools/functionality. 

 

Key points my talk with Keith include

  • GA is the main source/tool for analytics: GA contain a whole heap of data about most of the sites in question, but little analysis is currently done on the data beyond what Google provide in the reports. 
  • This means the team is "data rich but information poor"
  • Reasons for the lack of analysis time pressures, a lack of reason or a work driver (i.e. ability to out-work insights gained from the analysis).
  • Maybe in the University there is little willingness to have empirical data guide decisions (need to validate this with team).
  • There are very few KPI's at present, so even if the technology was not a limiting factor it is impossible to determine if we are succeeding.
  • And where people have attempted to set some they are qualitative and therefore less amendable to measuring by analytics
  • Need to be honest on how challenging this will be and start by going back to user needs. Listen to our users.
  • Although difficult to do the following is probably essential if (good) use of analytics is to In order to make better use of this data there would have to be:
    • Define a reason: For example "analyticity will inform IA and content placement decisions"
    • Draft a proxy for a web strategy: To provide higher level direction. For example "The purpose of the web channel is to ..." or "Our three main products are student recruitment, service delivery and public engagement." 
    • Agree some KPI or success measure: Reduced number of clicks for users to get to what they want, or Lower the bounce rate to X, or Start using goal conversion (or even completion) rates?
  • Too many site exits that are not linked to a goal/conversion.