For about 10 hours of effort, you should be analyzing valuable data you previously didn't have, and be poised to make serious optimizations to your campaigns.
It should come as no surprise that the digital analytics and marketing industries are talking about big data. After all, big data is a very exciting and popular topic, with many industry experts writing and speaking about big data, and why it's important. All of this content and popularity is good for the big data movement; however, the conversation could still be improved.
By its definition, the collection and analysis of big data is supposed to be large, complex, and difficult to work with. From the outset, this already seems like a conversation about to get extremely technical, costly, and time consuming (I can see the "non-data geek" executives in the room checking out right about now). In my experience, none of these attributes correlate well with successful projects. This is why we should shift the conversation away from big data, and talk more about smart data.
So What Is Smart Data?
Smart data is a subset of big data and should be a part of every big data project. The goal of smart data is to quickly show a return on investment and lay the foundation for ongoing big data projects. More specifically, smart data has three attributes:
How About an Example?
A great smart data project to start with is to incorporate marketing campaign data from a digital analytics tool, such as Google Analytics, within a CRM system, such as Salesforce.com.
For our example, let's assume I run a B2B company that uses Google Analytics to track my marketing campaign performance. Because I don't sell directly on my website, I'm unable to tie sales back to specific marketing campaigns. The goal of my example is to connect marketing campaign data with my CRM so I can compare the value of the leads I get from different campaigns. Once this information is available, I can make better data-driven decisions on where I should invest more, and where I should better focus my optimization efforts. The process I will follow is:
For about 10 hours of effort, you should be analyzing valuable data you previously didn't have, and be poised to make serious optimizations to your campaigns. But don't stop there; take your smart data project further by building on what you learn. Do you get a large amount of qualified leads from paid search, but little sales? Maybe your sales staff needs some training on the differences between paid search prospects and cold calling prospects. Maybe certain sales reps respond to certain campaigns or traffic mediums better than others. What else can you learn from the data?
Once you're done with one smart data project, reassess where you can make the biggest impact with the least resources and start again. Chances are your organization will have a big data culture before you know it.
Big Data image on home page via Shutterstock.
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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December 2, 2015
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