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“Big Data.” Most marketers would agree that discussion over this term has exploded over the past few years. And due to an abundance of cheap storage, most marketers would also agree it’s easier than ever to collect and store. But that’s not enough. Just having the data won’t make an impact on your marketing campaigns. In fact, very few marketers can effectively extract the value out of such large amounts of information.
Much of the power behind “Big Data” requires a human touch. It’s fueled by interaction and analysis, which isn’t immediately clear with a cursory look or basic statistical modeling packages. Classic regression techniques only allow for a limited number of attributes or data elements to be used for marketing decisions. “Big Data” flips this upside down with its breadth and volume of available information. And limiting the scope of what information can be used severely hampers the viability of “Big Data” to the point where ROI may not justify its use.
That’s why direct marketers today need to put the systems and procedures in place to effectively use the information available. This means investment in research and analysis to determine which pieces of newly collected data can be used to improve results.
The days of simply taking a core set of demographic or behavioral attributes and pushing them through SAS to see what pops up are most likely behind us. Data analysts today have to invest significant time to fully understand what information is available to an organization under the “Big Data” umbrella, and most importantly, dive into the relationships that exist beneath the surface of large data sets.
Again, these data relationships are not evident at a cursory glance; they require a deep understanding of the contents of the data as well as the business principles driving their collection. A top notch data analyst in today’s environment will have the ability to not just churn out predictive models, but also speak intelligently about what data exists and why it’s important to an organization’s overall goals.
It can be difficult to measure the effectiveness of “Big Data” for direct mail campaigns. There are often additional costs that may not show up when looking at the components of a direct mail piece. These costs often include additional storage and processing power, software licenses as well as additional staff hired (whether internal or outsourced) to analyze the newly created data sets.
That said, at the very least the results for a direct mail campaign must improve to justify the introduction of “Big Data”, otherwise the data added nothing to the ultimate metrics. Whether it’s an increase in response rates, better conversion rates or better overall ROI, the inclusion of “Big Data” in a direct marketer’s toolkit should contribute positively to the bottom line in order for it to be considered a success. Including “Big Data” just because everyone else is doing it won’t benefit an organization in the long run if results don’t justify its use.
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