Web Analytics is Dead: Long Live Web Analytics!
The Web analytics market is undergoing a significant transformation, marked by vendor consolidation, the expansion of vendor solutions and the entrance of competitors from adjacent markets. Web analytics has been around for more than a decade, so what has happened to drive this recent activity?
Simply put, businesses' use of the online channel is maturing, which is changing what they need from the software that supports it. Designed to help companies understand the technical and business performance of their Web sites, Web analytics initially grew up in relative isolation from the rest of the business. However, most customer relationships are not siloed but rather shift across online and offline channels. Web analytics is evolving to better support companies' abilities to understand and grow multichannel customer relationships by integrating with the significant IT investments that businesses already have made.
The evolution of Web analytics can be characterized by three cumulative waves: reporting, analysis and action. The evolution is only midway. When Web analytics emerged as a market in the 1990s, tools from early vendors processed server logs into reports for monitoring Web performance. As businesses realized the potential for e-commerce, reporting became marketing oriented. The focus changed from quantifying "How many hits to our Web site?" to "How many visitors did we turn to buyers?"
But marketers then needed more than static reports. They wanted to understand why behaviors occurred, to test hypotheses and assess patterns. The entrance of Visual Sciences in 2004 marked a shift in emphasis from static reporting of "what happened" to dynamic analysis to uncover "why it happened."
Of course, marketers don't analyze things just to get smarter; they do it in order to take actions that improve results. For online marketers, this initially meant using insights from Web analytics to refine the Web site experience so as to eliminate barriers to conversion. Now, tools are beginning to automate the connection between analysis and action, attempting, for example, to refine pay-per-clicks bidding for more effective customer acquisition. Already companies are hungry for more sophisticated solutions that will enable more personalized customer interactions to capitalize on specific, individual customer behaviors and trends.
Where is this evolution likely to take Web analytics?
In order to support a holistic view of customer relationships, Web analytics will become more deeply integrated with the billions of dollars businesses already have made in enabling the "360-degree view of the customer," such as customer data warehouses and business intelligence systems. To help businesses leverage the valuable insight Web analytics provides in a more automated fashion, we also will see the integration of Web analytics with the business systems that are already in place for customer support, cross-channel marketing, and other functions that utilize the Web. These developments will marginalize the market for standalone Web analytics software, though the broader services and support ecosystem will continue to thrive.
Vendors already have recognized this direction. They are either combining or expanding beyond the rapidly commoditizing market of pure reporting for more sustainable market footprints. For example, WebTrends has now taken a position to deliver "marketing performance management." Visual Sciences, a division of WebSideStory, now markets its technology for analyzing voice response as well as the Web channel. Unica acquired Sane Solutions to integrate Web analytics into its cross-channel marketing suite. First among business intelligence vendors, SAS has a Web analytics offering.
Change for the Web analytics industry will continue. As companies invest in technology to support the growing Web channel, they should look at how insight from online data can be leveraged broadly across the organization through integration with business intelligence, marketing automation and other systems to derive optimal long-term value. n