I'm excited to have one of my Silverpop colleagues, Bryan Brown, once again contributing to our Demand Generation blog.  Bryan directs product strategy for Silverpop.  He was a co-founder of Vtrenz, which became Engage B2B after Silverpop's acquisition and which today is the basis for the marketing automation sophstication underlying our new converged email and marketing automation platform, Engage 8.  Bryan has deep insight at the crossroads of marketing strategy and technology, and he spends a lot of time thinking about how to improve revenue attribution in a closed-loop B2B demand generation environment.  This is where his piece today focuses -- digging into the state of revenue attribution for B2B marketing and where it is headed.  Welcome back, Bryan.  ~ABN

I recently chatted with a few retail marketing customers of Silverpop.  My discussion reminded me of what is a strong revenue analytics focus among many digital marketers in the mass consumer arena.  With statistically-sound precision -- and often without true 'closed-loop' systems in place -- these consumer marketers can measure and forecast the revenue generated by specific marketing programs. It’s so thorough that they can tell you precisely the attribution of revenue down to a specific email message and within that message down to the various links, offers and content, itself.  Of course, they can then 'bubble up' the attribution to types of email campaigns, product lines and database segments, and all of this can be reported with trending by any period.

This conversation helped me realize how much of the revenue attribution efforts by consumer marketers parallel where revenue attribution within B2B demand generation is heading these days.  Yet at the same time, I was reminded of how difficult it still remains for B2B marketers to report on multi-point attribution of marketing programs -- i.e., identifying the sequence of touches and content offers that are most likely to result in a positive revenue outcome.  This is particularly difficult, given the complex, lengthy sales cycles in B2B marketing.  Digital consumer marketers frequently are able to see the direct impact between a specific marketing program and a revenue outcome merely because they deal in terms of simple, lower-price-point, short-run decisions, which are often direct cause-and-effect scenarios.  This type of buying decision simplifies revenue attribution and overall marketing-program analysis.  B2B marketing -- on the flip side -- may have dozens of touch points with a prospect over many months before (s)he makes a purchase.  In fact,