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Going Beyond the Implementation: 3 Steps for Optimizing Your Digital Analytics Installation

  |  May 26, 2014   |  Comments

Report creation, education, and iteration can help you optimize and better your organization.

For digital analysts and implementation specialists, we're in one hell of a sweet spot. The tools we use and data we can collect are more robust than ever. The way in which we collect data (as long as you're using a good tag management system) is easier than ever. This leads to more projects and demand for our services, and really helps elevate what we have to offer within organizations.

But with the ability to blast through tagging projects and requirements documents at light speed, we can't forget about our true end goal of helping our clients and organizations to optimize and better their organizations. And for this, here is a high-level, three-step process to work through once those tracking requirements are satisfied:

1. Report Creation.

For most in the digital analytics industry, reporting is a very dirty word. If you've been in the industry for a few years, no doubt you have some stories to tell. But don't worry, when I say "Report Creation," I don't intend for you to create and deliver every report your client consumes. But what should happen is a transition period between fulfilling reporting requirements and your client becoming self-sufficient with their data.

The reason for this is simple. Most digital analytics practitioners remain ahead of their clients in terms of new analytics tool features, evolving measurement philosophies and methodologies, etc., and the majority of the requirements you receive will be unique in some aspect. As a result, the strategy you employ to fulfill the reporting requirements and how the data is structured will likely be unique for each tagging project, even if it's with the same client. By creating the first set of report templates and reports, you are able to teach your client how the data is related, and provide a framework to build on for the future.

2. Education.

As I mentioned above, it's part of our job to stay ahead of the curve in knowing what data we can report on, how to collect it, and then what to actually do with it. Having this breadth of knowledge, it is also our duty to disseminate it to the rest of our organizations and clients. And we should do this by meeting with all members that will analyze, consume, and make decisions off of this data.

If we can educate all involved parties in how the data is collected, and how to analyze the data, something magical will happen. The data will actually get used. I have been a part of dozens of implementation projects from small businesses to Fortune 50 brands, and what holds true every time is that unless someone knows how to find the data and what to do with it, nothing gets done. This education phase is vital to leading your clients to making data-driven decisions, and growing their organization's analytics maturity over time.

3. Iteration.

During the Education phase when you meet with all the involved parties, you'll notice two things happen with every project: the data doesn't quite match the business question they were looking to answer, and they have additional data they want collected.

Even if it means you have to perform a major rework of the data collection and reporting process, these are both great items to have happen. And it's because you have your clients thinking. You have them engaged. They are actively visualizing how they will use the data to optimize and make things better. And in the digital analytics world, that truly is the ultimate outcome.

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ABOUT THE AUTHOR

Robert Miller

Robert Miller is a Senior Analyst at Search Discovery. He is actively involved in industry organizations, such as the Analysis Exchange and the Digital Analytics Association.

With the Analysis Exchange, he helps non-profits capitalize on their website data, and educates aspiring digital analysts about the foundation of digital analytics, from the implementation of a digital analytics tool to performing analysis and making data-driven recommendations for organizations.

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