I quickly learned that it was a real challenge to integrate Web analytics data in heterogeneous data environments. Most of the tools weren't able to load transformed data from other systems and expose it in the interface, nor were many tools able to feed data to other systems. Those that could dealt strictly with aggregate, visit-level data, and could typically provide an export in only very common formats.

Time marches on as relentlessly as companies innovate. Now when I ask the same question to analytics tool vendors, I get a different response -- more along the lines of "yes, we can do that, we understand why you would want to that, and we have a various options for doing so." The Web analytics industry has come a long way for sure. Vendors have actual products or services that enable integration (such as Omniture Genesis, Coremetrics Connect, WebTrends Open Exchange, and Unica's Partner Ecosystem) or they offer technology solutions that support direct data integration using off-the-shelf extract-transfer-load tools into their open relational backend databases. Still, the question remains "what do I integrate," which brings me to the point of this article. Several integration points are now possible to consider when integrating data to and from your Web analytics system:

Ad Serving Systems. By feeding data from Atlas, Dart, Burst, and a whole host of other ad servers directly into your Web analytics tool, you can begin to understand and analyze the behavior of customers and visitors at the campaign level or at the specific ad level. You can look at impressions, clicks, click-through rates from your ad servers and directly compare them against conversion rates and other custom measures in your analytics tool. The purpose here is to improve, optimize, or eliminate the lowest performing campaigns/ads based on your goals and increase your best performers.

CRM Systems. Sending data out of your analytics tool to your CRM mart can enable a corporation to deliver on some very powerful and profitable activities, such as being able to identify and record specific events that customers perform on a site (such as a white paper download or shopping cart abandonment) and automatically trigger actions that promote value (such as following up with an email to the customer offering other white papers in the same or related topic areas or a discount on products abandoned in the shopping cart.) You can also record key activities that the customer engages in on the site to enhance their customer record. The purpose is to improve your knowledge of customer activities on the site to enhance the value chain and inform the sales process (among other things).

Financial Systems. It's not uncommon to see proxy values loaded into a Web analytics system to provide an indicator of the financial value of a transaction or other value-generating event on a site. But why use a soon-to-be obsolete proxy value, when you can directly connect your analytics data to real monetary value? For example, you can join actual product price or margin data with transactional data on the site to let stakeholders view the revenue or profit impact on the business today, not when the next report comes from finance. The purpose is to better understand how the site is monetized and what products outperform others. Or you can even use these data at a visitor level to identify lifetime value.

Custom Data Warehouses and Marts. One of the more exciting integrations you can do with Web analytics tools these days is join view or visit data (and thus visitor data -- as long as you use the right tool) with dimensions in your data warehouse or custom data mart. For example, online publishers may not want to field values in tags or parse urls, so instead they join a dimension from their data warehouse with a view or visit table in their Web analytics tool to bring to light how a specific customer attribute or segment is performing against one another.

Third-Party Systems. Certain vendors have concentrated their efforts on integration with third-party systems, such as qualitative survey tools, email service provides, multivariate testing tools, and paid search tools. The benefit of deploying these integrations can be enormous when placed in the proper business context. By enabling these integrations, you can associate things like site satisfaction scores with the specific urls people viewed on a site and thus approximate the impact of user experience on satisfaction. You can compare various KPIs for different email campaigns or better understand the search terms that drive your specific site KPIs.

Without a doubt, integration of various data with Web analytics tool is no longer a white whale. Instead integration has turned into the holy grail for companies that want to automate business processes based on Web behavioral data, enhance their competitive advantage, maximize their sales opportunities, and understand how to best optimize the site against goals to improve the user experience. How have you integrated data from or with your Web analytics tools?

This commentary is insightful. I recommend it to others.