A while back when I started consulting with clients in the area of digital analytics, a common engagement would be to help an organization figure out which Web analytics technology would be best for its needs. The typical process would involve working with the organization to define its business needs by understanding what its goals and objectives were. The next stage would be to translate that into a set of requirements in terms of what the technology needed to be able to deliver and then using those requirements as a basis for assessing proposals and pitch presentations and choosing a particular vendor.
Cost was inevitably an issue, as there were no "free tools" at the time, but the focus on costs tended to be on the cost of acquiring, implementing, and running the particular technology. Generally this would focus on the price of the technology itself and perhaps what kinds of professional services might be required to implement it. Little consideration was really given to how much it would cost to actually run the technology and extract the return on investment. There was little consideration given to the true total cost of ownership as, on the whole, the decision was often viewed as a technology decision and not as a business decision. Has much changed?
Back in 2006, in one of his first ever blog posts, Avinash Kaushik proposed his "10/90" rule for Web analytics success. In his inimitable eloquent style, Kaushik suggested that for every $10 an organization spent on Web analytics technologies it should spend $90 on "intelligent resources and analysts." Effectively, his favored strategy was for organizations to sign up for a free technology, invest the savings in analysts to extract value from the data, and then invest in more sophisticated technology when the organization reached the limits of what the free technology can do from it. It was a bold statement at the time and one that has stuck in the psyche of the Web analytics world. You can argue on the proportions but the point is fundamentally right. It's not the software that delivers value or the hardware that it sits on, it's the "humanware" that sits on top of it.
Since 2006, the digital analytics technology landscape has evolved. At the core remain Web analytics technologies, but around that core has evolved a whole ecosystem of additional analytically-based technologies such as testing and experimentation systems, customer experience measurement systems, voice of the customer systems, social listening systems, and a myriad more. The landscape has become more complex, but the fundamental tenet remains the same. Implement these technologies at your peril unless you invest in the appropriate resources to drive and extract the value.
These days, the costs of good analytics or optimization technologies are not just in the technology but in the processes and people around the technology. Implementing a sophisticated Web analytics technology requires a structural rethink around how that technology will be supported. In a recent engagement with a major online player in the U.K., we advised that they needed to treble the size of their analytics team in order to realize the benefits of an investment decision they had already made on a particular technology. In another case, we identified that the costs of implementing a technology roadmap would be largely in the additional headcount required to implement and run the program as opposed to the technology license costs. As the technology landscape becomes more complex, so does the need to define the real costs of good analytics.
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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.
March 19, 2014