Top Five Metrics for Revenue Generation Marketers

Marketing measurement is a perennially popular topic. I myself have just completed a white paper on Top Five Metrics for Revenue Generation Marketers, sponsored by LeadMD, and touched on it in a separate Gleanster study, Revenue Performance Management - The Evolution of Marketing Automation. With both of these on my mind, I also paid new attention to Eloqua’s list of five key revenue performance indicators (listed in the ‘Take a tour’ graphic on this page). The obvious question was whether these three sources agreed about what’s important.

The answer is: not exactly. The following table compares the top metrics from each paper, with analogous items on the same row:

The only item that’s clearly shared across all three lists is the number of leads generated, and even that takes a bit of squinting to include Eloqua’s measure of “reach”, which is really the number of leads currently at different stages. You could also argue that close rate and conversion rates are pretty much the same thing, and therefore also present on all three lists. (Again, a bit of squinting is required). Three of the other items appear just twice (return on investment, revenue, and time to close). The remaining three occur just once.

What accounts for the inconsistencies? I’d say mostly it’s the nature of the lists. The Gleanster list is from a survey of what marketers actually do: it’s no accident that nearly all items are quite easy to calculate. (Return on investment is a glaring exception, and I very much doubt that 73% of marketers actually calculate it today. So let’s just assume that figure is aspirational.)

The other two lists are prescriptive: that is, they show what an expert feels should be done, not what marketers actually do. Look closely, and you'll see that the lists are quite similar.  Four of the five measures are shared.  Even the two non-matching items are related: my fifth item is cost and Eloqua's is return, which is a combination of cost and revenue. 

The apparent difference between the lists is that mine looks more simplistic. It starts with a three-part formula for calculating revenue: (number of leads) x (close rate) x (revenue per closed lead). A fourth factor, cost, combines with revenue to create return on investment. The fifth factor, time, is needed to forecast revenue by period.

Eloqua’s list breaks those same factors down by stages. That is, instead of a single close rate is has a set of conversion rates from one stage to the next. It similarly breaks number of leads into reach (number of leads at each stage), revenue into value (expected revenue from leads at each stage), and time into velocity (number of days spent at each stage). This makes total sense, and if you read my paper, you’ll see that I recommend breaking the measures into stages in almost exactly the same way.* The reason is that reporting on stages gives much greater insight into what’s working well or poorly, and thus helps marketers to see where they should make changes. Providing this sort of actionable information is probably the most important purpose for any measurement system.

In short, Eloqua and I pretty much agree on what marketers should measure. Now if the marketers themselves would join the consensus.

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* The paper also gives plenty of sage advice on how to actually build a system based on these measures.

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What Really Creates Marketing Automation Success: Data from Gleanster

Research firm Gleanster released its free “Gleansight” report on marketing automation late last month. I was principal author for much of the document and drafted the survey questions, although I didn’t write the individual vendor profiles or administer the survey. (I’ll also share in the revenues, which come when vendors pay for the names of people who download the report.) Although I’m obviously biased, I do think it’s an excellent product and highly recommend it.

Many readers of the report will be most interested in the vendor profiles and survey-based rankings. But I have my own data on those, so I’m more interested in the answers to the general survey questions. These asked why people implement marketing automation, what challenges they face, and what contributes to success. The survey compared answers from top-performing marketing automation users with answers from everyone else. This gives especially interesting insights (or should I say...GLEANSIGHTS) into the differences between sophisticated users and their less experienced brethren.

Here are some highlights – please look in the report itself for details.

Reasons to Implement: top performing companies listed higher revenue, better leads, and more leads as the most important reasons to use marketing automation. All other companies also listed revenue and more leads in their top three, but their most common answer was decreased marketing costs. By contrast, marketing efficiency ranked fourth among top performers and much lower among all others.

My interpretation is that top performers have learned that marketing automation might make you more efficient but won’t actually reduce your total marketing budget.  Other, less experienced companies that expect cost savings will probably be disappointed.  If anything, marketing automation will probably increase their marketing spend – hopefully with an even larger improvement in the revenues and leads to compensate.

Value Drivers: Both groups agreed that the two most important predictors of marketing automation success are cooperation with sales and ability to measure return on investment. But the top performers' third item was easy-to-use system while the others chose adequate staff training. This is a really interesting divergence.

The Gleanster people tell me that many of the top performers are now using their second marketing automation system because their first was too hard to use.  In other words, this is the voice of experience, and that voice is saying that training by itself can’t overcome a difficult system. Less-experienced companies who are relying so heavily on training may find it's not enough. But let’s not overstate this point: training is certainly necessary for success even if it’s not sufficient. Perhaps the top performers discounted training in part because they already had a lot of it.

Back to the data.  Both groups rated having an analytical culture as the fourth-most important value driver, while listing outside help, change management, and organizational resources as the least important.

These answers shed important light on the on-going debates about how marketers should approach their automation deployments. The message is clear: success depends on a marketing department’s own skill level, not on outside resources. A marketing department that pays attention to sales needs, carefully measures ROI, and makes fact-based decisions will succeed with marketing automation just as it probably succeeded without it.. A group that hasn’t mastered those basics won’t succeed at marketing automation no matter how much training, change management, and external assistance it brings to bear.

Challenges: Gleanster didn’t send me responses to this question broken out by top performers vs. all others. For the combined group, the top two challenges cited were data quality and creating content; the next three were poor marketing processes, lack of staff, and selecting metrics. These are all factors controlled by the marketing group. The lowest ranking factors were all the external ones: organizational culture, senior management mindset, lack of IT support, and lack of funding.

This is consistent with the previous answer: marketers create their own success. Marketing automation is just a tool.  It will help a well-run department work better but can’t save a poorly-run department from itself. Massive re-engineering isn't necessarily required to deploy marketing automation, but departments that lack a solid foundation need to build one before automation can do them any good.

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