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Where Web Analytics Fails

Current Web Analytics products don’t really do what they say on the label. A more accurate term would be 'Web Reporting'. And that's because the aggregated information which these firstgeneration solutions provide can at best be used for two- or three-layered reporting, rather than in-depth analyses about which customer did what, when and how. Worse still, current web analytics products don't even enable accurate reporting due to inconsistent data storage and collection strategies.

An article by David Smith. VP Sales. Site Intelligence Australia.

Web Analytics hints at what happened. Multi-Channel Customer Intelligence shows you what could be. Whilst Web Analytics can offer some simple insight into online processes, campaigns, etc., it doesn’t provide complete transparency but instead a series of independent and difficult-to-integrate stand alone reports. In real-life scenarios, analytics requires the ability to break knowledge down into constituent parts (which is the opposite of synthesis or aggregation), as well as being a method of studying the knowledge elements and their inter-relationships (which analysts might call an ad hoc, unlimited multi-dimensional analysis). Web Analytics - as its name suggests - is a ‘web only’ understanding of activity, often at a ‘universe’ level or at a basic segment level. It rarely provides individual long-term visitor behaviour or identification of the individual, and therefore lacks the ability to link to offline.

Are the basics of web analytics fundamentally flawed? Having worked with great channel marketeers from top corporations in Europe and Asia, I see a general consensus that the basics of Web Analytics are often inaccurate due to flawed technology/schema structures and collection rules. For example:

1. Most Web Analytics solutions aggregate data and provide reporting over a

predetermined period. Anything outside that period is aggregated from standard reporting periods. For example, new unique visitors over 3 days = day 1, plus day 2, plus day 3. This method double counts any repeat visitors during the 3 days. 2. In addition, many solutions will have a set way of counting certain metrics. One

‘leading’ solution will under certain common circumstances count the visitor but not the visit. This leads to a higher numbr of visitors to the website recorded than visits, which is plainly impossible. 3. There is no reliable method for cookie management in nearly all solutions, which

means that little or no repeat visitor history is achieved, or at best the history is disjointed due to cookie deletion, etc. Only solutions that are based on a true

relational database that is capable of retaining multiple attributes and cookies at an individual visitor level can provide as full a picture as possible. If a cookie is deleted then the visitor history is curtailed until next time the visitor identifies themselves, and everything in between is lost. Worse still, most solutions will have unrelated duplicate records for the old profile and the new profile, and see them as separate visitors.

You can't dive deep in a shallow pool. Another problem is that many or all solutions limit the number of filters that can be applied to a report to around three atributes. This severely limits the business in its analysis. Whilst two- or three-layer reports can technically be argued as ‘analysis’, really the analysis requires unlimited attributes and filters to be applied. If a three-layer report shows a potentially interesting trend, then a separate unrelated report has to be written substituting different fields with new ones, thereby losing the drill down capability from the original interesting report. A true analytics solution should provide unlimited and ad-hoc drill down across any number and mix of attributes and filters.

Web Analytics lives in a world of its own. First-generation Web Analytics offers little or no integration with offline data, whether campaign data or customer data. Those systems are also marked by limited outputs or integration with industry standard tools and protocols, such as full integration with Excel. In today's organizations, web reporting is often stand-alone silo-based reporting. Being focused on the single online channel, it is also difficult to export or analyse using industry standard tools. So without a true relational database and ability to directly link relations across an ad-hoc mix of tables and data, it is very difficult to perform predictive modelling and analysis on anything but the simplest of reports.


About Site Intelligence Site Intelligence creates, installs and supports multichannel customer intelligence systems. The company has worked with leading retail and financial institutions. Our mission is to help organizations gain commercial advantage from a 3D view of their customers. By recording customer behavior online, ondevice and instore, we help companies to anticipate customer needs and provide outstanding service. Site Intelligence supports customers with consulting, installation and training.

We have three differentiating technologies: VBIS multi-channel database. VBIS uses standard SQL technology and comes with sophisticated APIs that enable it to be used with any other EPOS, call center, ecommerce or other platforms for a truly multichannel solution. And with multi-server processing capabilities, VBIS is enterprise-class and completely scalable.

Specialized reporting tools. Site-Intellegence has developed a range of report generators and specialized modules which enable detailed customer intelligence to be extracted from the VBIS multi-channel database. These range from executive dashboards and Excel plugins through to detailed ecommerce, sales correlation and visitor location analysis tools.

Server-driven Infinity Tags. Our Infinity Tags are easily installed by webmasters, typically reducing the web tagging overhead to days instead of months. Once installed, our Infinity Tags collect a wealth of data about customer behavior. And because they are server controlled, future changes to your anlytic requirements need not involve the web design team. Europe Americas Asia Pacific

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Where Web Analytics Fails  

Multi-Channel customer intelligence.

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