When we talk about analyzing Web data, we tend to focus on the use of so-called Web analytics tools, such as Google Analytics, Omniture and Coremetrics. These analysis tools were developed specifically to help manage the reporting and analysis of Web site data, but they aren’t necessarily the only tools available on our workbenches.
There are a variety of other reporting and analysis tools we might use on site data to better understand online business performance and customer behavior. Fair to say, Web analytic systems have significantly improved their analytic capabilities over the past few years and will continue to do so. These days, a number of systems are much better at filtering and segmenting data on the fly to look at particular groups’ behavior or characteristics.
But as an organization’s needs develop, so too does the need for specialist reporting or analysis tools. Other systems for reporting and analyzing Web and customer data can be grouped into three broad categories:
BI, or OLAP, tools are often found in the corporate reporting environment and include systems such as Business Objects, MicroStrategy, and Cognos. Databases such as Oracle and SQL Server also either come with BI functionality or can have it 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, for example, I might start looking at total sales for a year, then drill down to product categories, then subcategories, and then the product level.
Some Web analytics systems can drill through data in this way, but a feature of the BI tools family is the ability to handle multiple hierarchies across multiple dimensions. In addition to drilling down on the product dimension, you may also be able to drill through the data in terms of geography and time. BI tools could be used to report on Web data in the context of other channels as well, such as comparing the profile of leads or enquiries generated online against those generated in the call center.
As the saying goes, a picture is worth a thousand words. Visualization tools can be a valuable weapon in your analytical arsenal. Again, some Web analytics tools, such as Visual Sciences and Site Intelligence, have powerful visualization capabilities. But though many Web analytics systems have improved the visual reporting of Web data through tools like click overlays, a visualization tool might add another dimension for the analyst.
Visualization tools can range from Excel add-ons to complex applications that are commonly integrated with data mining tools. At the desktop level, Excel add-ons such as MM4XL extend the scope of Excel’s charting abilities, allowing the analyst to present data in different ways. More sophisticated tools can produce rotating three-dimensional 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!
Statistical Analysis and Data Mining Tools
Finally, statistical analysis and data mining tools can be useful for analyzing Web and customer data. What’s the difference between statistical analysis and data mining? Statistical analysis is predominantly about exploration, and data mining is about discovery. With statistical analysis, you are often testing an assumption or a hypothesis, such as proving 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 cover a wide variety of approaches, methodologies, and techniques that can be useful for the Web analyst. They can be broadly classified as follows:
- Statistical analysis
- Classification techniques
- Clustering and segmentation methodologies
- Text analysis
Increasingly, many of these techniques are used for making predictions, so “predictive analytics” is also often used to describe these various methodologies.
Some of this stuff may seem 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 optimization 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’ll look at some of these techniques in more detail and how they can be used in the context of analyzing online visitor and customer behavior.
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