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Calculating the ROI on B2B Content Marketing

Posted January 15th, 2014

“If you can’t measure it, you can’t manage it,” the management guru Peter Drucker famously observed. Much has been written about the need to measure the ROI on content marketing. The challenge of doing so can be significant, however, particularly in the world of B2B.

One problem is that content marketing campaigns can take many different forms, have very different objectives, and be measured using many different performance metrics. Another problem is that the results of the campaigns are often spread out over time, and not all performance outcomes (e.g., brand awareness) are easily quantifiable.

Last week, Forbes ran an interesting article entitled Will the Content Marketing Trends Continue? It Depends On Proving Your ROI. The author offers a useful example of a ROI calculation on — in this case — a white paper. The example (reproduced here) shows some of the costs a B2B marketer is likely to incur as part of the investment on the front end as well as the expected ROI in terms of the metrics that would seem to matter most.

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The calculation seems reasonably accurate. Having created and run inbound marketing campaigns around dozens of white papers, we know that $23,500 is a good ballpark estimate on the cost side of the equation. For smaller companies, that represents a hefty investment, however, and it’s more money than many of them are willing to gamble. And while a 2.3x ROI is certainly nothing to sneeze at, we think B2B marketers can do a lot better.

At Starfleet Media, we take great pride in our ability to deliver positive ROI to our clients without fail, and in a highly cost-effective manner. We’re able to do this, in part, by creating economies of scale around content production, distribution and promotion (combining a proven multi-sponsor underwriting model with a state-of-the-art inbound marketing campaign infrastructure and extensive partner network) and by delivering a guaranteed number of qualified leads, allowing our clients to predict conversion rates with a high degree of accuracy.


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