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The web analytics toolbox

When we talk about analysing website data we always tend to think about doing that in a web analytics tool. Last time I described my perfect web analytics system would be one that would allow me to have fast, reliable access to my core data on the site and also allow me to easily and cheaply access and extract the data in different formats. Then I would be able to analyse the data in different tools if I wanted to.

I’ve always had this concept of the “analyst toolbox” in my head. This is a suite of tools and techniques that can be used and deployed on the right data at the right time. All analytical software have different strengths and weaknesses and so if it’s possible it’s good to have more than one tool in your toolbox. You don’t want to be trying to bang in a nail with a screwdriver (metaphorically speaking)!

I had an example of this recently with a client who is a global media company. They want to be able measure and report on hundreds of sites they manage across the world. Typically in a situation like this there are potential tensions between a need for a high level view of key metrics across all sites versus the local need for depth and detail on a small number of sites.

As a rule (and no doubt I will get emails from vendors challenging me on this) I find that web analytics systems are better at handling detail on a relatively small number of sites and less good at reporting small number of metrics across large numbers of sites in a digestible way. For example, having the ability to drill through from a high level view to a low level view in a way that is found in corporate reporting systems using Business Intelligence tools.

My recommendation to the client in this instance was that they should look to extract summarised data from their web analytics tool on each of the sites on a regular basis and put it into a separate database. The data could then be reported using a Business Intelligence tool such as Business Objects, Cognos or similar. This would also enable the client to add additional data (such as marketing expenditure data or cost data) on the sites into the database and report that along side the site data.

This was an example where there was a need to be able to aggregate and consolidate the data so that higher level trends could be observed and sites benchmarked against each other. At the other end of the spectrum there may be times when you want to delve deep into the data and look for patterns or trends that are not obvious in regular reports. This is where you are likely to be analysing data at the visitor or customer level rather than the site level may involve the use of more advanced analytical or data mining techniques.

This type of analysis tends to be driven by particular issues or problems that you want to understand in more detail. Often these problems may have several factors that you need to analyse to understand what may be causing them. For example, a client has a problem with a high bounce rate on a home page. What’s causing this and is and is it specific to certain types of traffic? Potential factors that I might want to analyse might include the type of referral or campaign that the visit originated from, whether this was the first visit or not or even potentially the time and day of the visit.

Whilst it is possible to filter data in web analytic systems, often the challenge can be to iterate through the various hypotheses quickly and easily enough to get at the nub of the matter. Maybe the problem is also not dependent on just one of the factors but is dependent on a combination of them. Maybe, for example, the home page bounce rate problem is particularly bad amongst first time visitors arriving from Google who are coming at the weekends.

As we look to continually optimise the site and the visitor experience, we will need to delve deeper and deeper into the data to understand the nuances and subtleties of visitor behaviour. Our web analytics tools can take us a long way but from time to time it may be necessary to look at the data a different way using different techniques. Next time I’ll take a look at some specialist data analysis tools and techniques and how they can be deployed on web data.

Till then…

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