Making Web Analytics More Analytical

In my last column, I reflected on 10 years in digital analytics, examining how far the industry had developed in a decade in some ways and how there is still room to grow in others. One point: the online marketing world had been “data rich and analytically poor.” This week, let’s explore some areas where there is work to be done to enhance the quality of insight that digital marketers get from their investments in data capture and reporting technologies.

For example, considering that Web analytics and campaign data is often used to make resource and budget allocation decisions around online marketing spend, the continued use of the “last click attribution model” seems to be bizarre.

Many businesses and their agencies know that this model is a sub-optimal solution to managing marketing spend but have limited options given the systems available to them. Some work has been done by some agencies to improve the way that campaigns are measured and optimized, but it’s seemingly required large investments to get to the position where the influence and impact of different channels can be tracked and measured properly. I’d like to see Web analytics providers offer greater analytical horsepower for campaign analysis by providing more flexibility in defining attribution models and windows, and the ability to look at the impact on multiple campaigns on conversion.

Also, it would be great to see some more progress in the development and application of econometric and predictive techniques to better understand online marketing effectiveness and particularly the interaction between online and offline activity. These types of techniques have been used for years in brand and consumer marketing to assess the impact of TV campaigns and the like. There’s probably a role for these types of analytical approaches to supplement or enhance the direct measurement techniques that are most often used in the digital space. Again, work is being done in this area by some businesses or their agencies, but we need to see more debate about the use of these techniques in the online space and the type of problems they can help to solve.

This leads me on to my final point. I would hope to see the development of more “science” in online marketing. For me, marketing is a blend of art and science; a combination of left-brained and right-brained approaches. That’s not to say that there isn’t any science in online marketing analytics. Multivariate testing is a good example where analytical and statistical techniques are used to predict the best probable outcome from a series of experiments. There are other examples as well in behavioral targeting. However, there are many other areas where there are opportunities for a more scientific and statistical approach to understanding online behavior in the same way that marketing scientists developed theories and models to explain such things as TV viewing behavior, shopping behavior, and purchasing behavior 20 to 30 years ago.

One of the earliest marketing texts sitting on my bookshelf is called “Marketing Decision Making: A Model-Building Approach” and it was published in 1983! For this to happen, there must be greater interaction between academia and commerce. I’m sure there is some really interesting research in this area being done in instructions around the world, but am not sure about how much of it is reaching the commercial world in a consumable format. One good initiative in this area is that the Web Analytics Association has secured access to five leading marketing journals for its members, to provide an opportunity for industry analysts to connect with the work being done by marketing academics and researchers. It’s one step along what I hope will be the road to making Web analytics more analytical.

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