The Analyst’s Toolbox: Introduction

There is a tendency when we talk about analysing web data that we focus on the use of so-called web analytics tools such as Google Analytics, Omniture, Coremetrics and the like. These analysis tools were developed specifically to manage the challenges of managing the reporting and analysis of data collected from web sites but they aren’t necessarily the only tools we might have in our toolbox.

There are a variety of other reporting and analysis tools that we might want to use on the data from our web sites to get a better understanding of online business performance and customer behaviour. It is fair to say that web analytic systems have significantly improved their analytic capabilities over the past few years and will no doubt continue to do so. These days there is a far greater ability in a number of the systems to be able to filter and segment data on the fly to look at the behaviour or characteristics of particular groups.

However, as the needs of the organisation continue to develop so too might the need for different or specialist reporting or analysis tools. Other systems for reporting and analysing web and customer data can be grouped into three broad categories:

  • Business Intelligence (BI) or OLAP tools
  • Visualisation tools
  • Statistical analysis and data mining tools

BI or OLAP tools are often found in the corporate reporting environment and this class of tools includes systems such as Business Objects, Microstrategy and Cognos. Databases such as Oracle and SQL Server also either come with BI functionality or it can be bolted on. Underpinning many of these tools is the concept of a data cube that allows the analysts to drill through the data in a hierarchical manner. In a commerce environment I might start looking at say at total sales for a year and then drill down into product categories, then into sub-categories and then down to the product level.

Some web analytics systems do have the ability to drill through data in this way but a feature of the family of BI tools is the ability to handle multiple hierarchies across multiple dimensions. So, in addition to being able to drill through on the product dimension, you can also drill through the data say in terms of geography and also time. BI tools could also be used to report on web data in the context of other channels, for example comparing the profile of leads or enquiries generated online against those generated in the call centre.

As the saying goes, a picture tells a thousand words and visualisation tools can be a valuable weapon in your analytical arsenal. Again some web analytical tools such as Visual Sciences and Site Intelligence have some powerful visualisation capabilities. Whilst many web analytics systems have improved the visual reporting of web data through developments of click overlays for example, for the analyst a visualisation tool might add another dimension.

Visualisation tools can range from add-ins or add-ons for Excel through to complex applications that are commonly integrated in with data mining tools. At the desktop level, Excel add-ons such as MM4XL extend the scope of the charting abilities of Excel and allow the analyst to present data in different ways. More sophisticated tools can produce three dimensional rotating images that allow the analyst to explore and look for patterns in the data. The human brain is still one of the most powerful tools available for spotting patterns and trends in data when presented in the right way!

The final set of tools that might be useful for analysing web and customer data are statistical analysis and data mining tools. What’s the difference between statistical analysis and data mining? The way that I tend to view it is that statistical analysis is predominantly about exploration and data mining is about discovery. With statistical analysis you are often looking to test an assumption or a hypothesis. For example, you may be looking to prove that one group of customers rate your product or service more highly than others. With data mining, you are looking for patterns or relationships in the data that you may not know about.

Statistical analysis and data mining covers a wide variety of approaches, methodologies and techniques that might be useful for the web analyst. The can be broadly be classified as follows:

  • Statistical analysis
  • Classification techniques
  • Clustering and segmentation methodologies
  • Forecasting
  • Text analysis

Increasingly many of these techniques are being used for making predictions and so the phrase “predictive analytics” is a term that is often used as well to describe these various methodologies.

Some of this stuff may seem like a long way from the current day to day analysis of conversion funnels and the like. But as the market continues to mature and growth comes from optimisation and improvements in marketing efficiencies, some of these techniques will have a place on the analyst’s workbench. Over the next couple of weeks, I will take a look at some these techniques in more detail and how they be used in the context of analysing online visitor and customer behaviour.

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