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Marketing Analytics: Beware the Black Box

April 23, 2013 | By Ross Graber

One of the most positive developments in b-to-b marketing is an increased emphasis on data-driven decisionmaking. Growing numbers of marketing organizations are relying more on data and facts to make their decisions. While many organizations are not as far along as they’d like to be, I’m finding that most marketing leaders’ intentions are in the right place.

One of the most positive developments in b-to-b marketing is an increased emphasis on data-driven decisionmaking. Growing numbers of marketing organizations are relying more on data and facts to make their decisions. While many organizations are not as far along as they’d like to be, I’m finding that most marketing leaders’ intentions are in the right place.

With the desire to get really good at this really fast, marketing organizations are turning to advanced analytical techniques. Marketing leaders are hopeful that easier analytical approaches are right around the corner – if only they could apply the right algorithms to the data they have, they could create more of the insights they need. Advanced analytics offer great potential for b-to-b marketers, but I suggest you watch for three things when considering them:

  • Analytics don’t replace understanding. Don’t expect the machines and data scientists to come back with immaculately produced findings. Work to understand the what, how and why of the conclusions being drawn. Favor healthy skepticism vs. complete deference to smart people and complex technology. Blind acceptance based on a lack of understanding can only lead to poor decisionmaking.
  • GIGO still applies. Analytics are not immune from the old principle of garbage in, garbage out. Be sure to understand the process used to create the data, as well as how the data is being used. I’ve worked with several clients who were able to identify inconsistencies in the way humans entered subjective information into business systems, which led to flawed analysis and conclusions. Another thing to keep in mind: Just because data quality is sufficient for one use case doesn’t mean it can support all analytical purposes.
  • Keep it actionable. Watch for conclusions that are too high-level to be sensibly acted upon. Marketing-mix analysis findings like “White papers perform better” tend to isolate single elements of the buying process. Look for conclusions that appropriately address context. A more useful finding might be “White papers tend to have the greatest impact on technology executives when used during the education phase of the buying process.”Don’t allow narrow conclusions to stand in the way of making well-informed corrections.

There’s great promise in applying advanced analytical techniques to b-to-b marketing. As you consider this path, know that pursuing it requires your organization to develop a greater understanding of how analytics work and what can reasonably be concluded. Drop the hope of taking your existing data, feeding it into a black box and having incredible insights come out the other side. Instead, prepare to invest in understanding how findings can be produced and what they mean.

Ross Graber

Ross Graber is a Senior Research Director of Marketing Operations Strategies at SiriusDecisions. He brings over 15 years of b-to-b marketing experience with focus spanning marketing measurement, demonstrating ROI, data management, process development, marketing technology, customer marketing and sales enablement. Follow Ross on Twitter @rossgraber.

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