Competing on Analytics

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I recently read a research report from Babson Executive Education entitled "Competing on Analytics." While this isn't a new report and it doesn't focus specifically on Web analytics, I found a number of the points in it very interesting. They position analytics as a competitive strength across all industries.

Some of the points translate directly to Web analytics. In fact, many elements such as the need for executive support and the importance of data accuracy have been touched on in a number of these columns.

The research revolves around "fact-based decision making," using data to make smarter, more informed decisions. It touches on all business organizations, from financial services to professional sports teams. No matter the industry, it's clear that smarter decisions can be made when people are more educated through correct, accurate data.

I've often written about data driven organizations. My new book, Actionable Web Analytics: Using Data to Make Smarter Business Decisions has a few chapters dedicated to this topic. It is a common struggling point - how do you shift the way your organization makes decisions? Where do you start? Who needs to be involved? How do you do it? And what does it mean?

They identify several key attributes of companies successful in using and leveraging analytics to become more competitive in their industries. I found them interesting and on point, and wanted to add a few details to make them more specific to Web analytics specifically.

Attributes they identify include:

• One or more senior executives who strongly advocate analytics and fact-based decision making. This is key to success because it will require changing the way in which decision are made and will impact the overall process of improving the Web channel. Since most people fear change it is imperative to have the executive support to help push people in the right direction and break down the barriers.



• Widespread use of not only descriptive statistics, but predictive modeling and complex optimization techniques. This is key! Don't waste time on analytics if you're not going to take action and optimize site performance. While predictive modeling can be very helpful, you can tune things on the fly with live, on-site testing. An ongoing optimization plan ensures there's a process to take advantage of opportunities you identified through analytics. Don't be afraid of the "complex" optimization techniques reference. You can start small and basic. When you get right down to it, on-site testing and optimization are pretty straightforward.



• Substantial use of analytics approaches across multiple business functions or processes. I took this a bit differently from what's meant in the research for the purpose of how it specifically relates to Web analytics. We don't want data silos, we want attitudinal, behavioral, competitive and customer data all shared in a way different people can leverage it. Typically, the people who run the analyses aren't the same people who put data to work. It's imperative the business people have direct access to the recommendations and data are consistently shared.



• Movement toward an enterprise-level approach to managing analytical tools, data, and organizational skills and capabilities. More than anything, it's important to have an enterprise-level process to analyze, recommend, and act on the data. Analytics tools are making it easier to pull additional data in (almost always other Web data), as well as export it to other systems. Start small with these efforts. Make sure to focus on the initiative that will return the biggest impact. We've all heard horror stories of other enterprise software that takes two years to set up and configure, then isn't really used.

Take a look at the full report if you're interested, but also consider what it will take to shift your company's thinking and become a data driven organization where you can truly take advantage of the data.

Meet Jason at the ClickZ Specifics: Analytics seminar on May 2 at the Hilton New York in New York City.