Both Web Analytics and Business Intelligence attempt to tell part of a story, one in which a visitor becomes a customer and everything in between; however, these two disciplines have always presented information separately due to various reasons, leaving out the holistic view so many companies need to gauge efficiency and business value.
Since the emergence of Web Analytics, the actions of a visitor—such as how they arrived, how long they stayed, and what actions they took—have become more and more transparent. A slightly older discipline, Business Intelligence (BI) has roots in financial reporting, where analysts aggregate transactional level data into robust reporting structures with multiple levels of dimensions. Both types of analysis are necessary to understand a visitor from landing to purchase, but neither contains all of the data teams need to truly understand how digital marketing endeavors are performing.
In situations with an ecommerce site, analysts are able assess data like “basket size” and revenue, providing a solid foundation for return on investment and cost of goods sold along with a complete view of the visitor’s level of engagement. The less perfect and more common case does not allow the site to capture the entire path to purchase because the final purchase is often made offline, such as with hotel, auto, and insurance sales. In these situations, the Web Analytics package is limited to showing only engagement; however, just because the tool is limited, the reporting shouldn’t be. Thanks to key pieces of information like a loyalty ID, user ID or email address, it’s possible to maintain a link between what takes place on the site and what happens at the moment of transcation. Used in conjunction with Web Analytics, BI completes the whole story, bridging the gap between visitor engagement and storefront purchase.
As much as I would like to say this is new, it’s not. Unfortunately, Web Analytics and BI tools and analysis are seldom implemented correctly. The breakdown occurs when the technologies are not aligned from the start, and the planning needed for obtaining optimal insights doesn’t happen. For example, if the goal of the digital channel–be it a website or social property—is to be sales driven and have visitor engagement available to provide additional insights into purchase motivators then a robust tagging plan must be considered during the early stages of the project. Also, backend data models should be designed with the expectation of injecting online metrics into the aggregated transactional data.
As these two technologies continue to improve, we will see them converge in ways that we will all celebrate. Improvements, for example, will allow the analytics-driven marketer to determine success from an email, banner, and search, all the way through to a purchase or lead-generating action, all in the same reporting system. An end-to-end analytics solution: Won’t that be nice? Until then, you can benefit from a holistic view with the right blend of Web Analytics and BI, and by allotting enough time up front for strategic planning.
The Integration of Web Analytics and Business Intelligence
by Perficient Author on September 6th, 2011 | ~ minute read
