In the course of our work, we see many implementations of many different Web analytics systems. Although most companies now have an analytics system, or are on their third or fourth system, the poor quality of the data that is still being reported is surprising.
If you're only using the system to report very top line numbers, then it's easier not to worry too much about quality, as the data is hidden from view. Once you work with the system and use the data for diagnostics and analysis, data quality becomes more important and any data issues become more apparent.
Here's a quick checklist to understand how well your Web analytics system has been configured and how good your data quality is.
Is the site being tracked properly? Do you have a page tag on all the pages you want to track, including your error pages?
IP Address Exclusion
Have you determined which IP addresses you need to exclude or filter out from the data? Do you need to exclude your own organization's IP addresses? How about any site monitoring tools you use?
Good page naming is the bedrock of easier content analysis and user path analysis. Does the page name make sense? Do you have page URLs or document titles that are self-explanatory and make sense to ordinary users?
If you have complex or dynamically generated URLs, can you change them to something that is easier to understand? If you have a lot of extraneous parameters in the page URLs, such as session IDs, have they been stripped from the data collection so they don't appear in the reports as different pages?
Is your content organized into logical and coherent groupings? Do you have neat folder structures on your site that allow you to group content easily, or do you need to create a customized approach to grouping pages into content sections? How will this work be maintained?
Are conversion events well defined? Are all the important conversion events on the site being tracked? Do you need to customize parts of the system to recognize certain key events that aren't being captured automatically?
If you run an e-commerce site, are the orders being tracked properly? How does the data compare against your order processing systems? Are there any obvious differences and, if so, why?
Do you need to track campaigns in the system? If so, what structure and detail do you need for your campaign reports? How will you add the relevant tracking parameters to your landing page URLs and keep them up to date?
Do you have an internal search engine on your site? If so, is it being tracked? Are you capturing the keywords that are being typed in and analyzing the results?
This isn't an exhaustive list. Depending on your business, you may need to focus on some other issues. However, these are some of the common problems that we encounter in our work with clients. If you're focused on getting these things right, you're well on the way to having good quality data to work with.
Neil is off today. This column was originally published December 22, 2009 on ClickZ.
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Neil Mason is SVP, Customer Engagement at iJento. He is responsible for providing iJento clients with the most valuable customer insights and business benefits from iJento's digital and multichannel customer intelligence solutions.
Neil has been at the forefront of marketing analytics for over 25 years. Prior to joining iJento, Neil was Consultancy Director at Foviance, the UK's leading user experience and analytics consultancy, heading up the user experience design, research, and digital analytics practices. For the last 12 years Neil has worked predominantly in digital channels both as a marketer and as a consultant, combining a strong blend of commercial and technical understanding in the application of consumer insight to help major brands improve digital marketing performance. During this time he also served as a Director of the Web Analytics Association (DAA) for two years and currently serves as a Director Emeritus of the DAA. Neil is also a frequent speaker at conferences and events.
Neil's expertise ranges from advanced analytical techniques such as segmentation, predictive analytics, and modelling through to quantitative and qualitative customer research. Neil has a BA in Engineering from Cambridge University and an MBA and a postgraduate diploma in business and economic forecasting.
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