One of the greatest challenges for marketers in the business-to-business (B2B) sector today is defending the value of their online marketing programs. This is because it’s difficult to justify spending money to generate clicks, downloads, page views, visits, impressions and links if they can’t be directly connected to the revenue generation.

Traditionally, B2B marketing consisted of programs such as trade advertising, trade shows, cold calling and direct mail. Each type of program had standardized metrics, such as circulation, traffic, leads and responses that you could reliably use to forecast or measure your return on investment (ROI). The reason this worked was because you could easily identify the prospect who was involved, and track whether they eventually bought the product or service.

For example, many trade magazines included “bingo cards,” or magazine inserts that readers could fill out, and mail in to request more information from the advertiser. (This often even applied to published articles that resulted from PR efforts.) A response was considered to be a qualified sales lead whose conversion rate was fairly predictable. And so calculating an ROI – using the revenue resulting directly from the program – was straightforward. You simply divided the revenue that was generated from the program (less the cost of the marketing program) by the cost of the marketing program, to get the ROI.

Likewise, trade shows generated sales leads that could be tracked forward to revenue production, such as swipes, business cards, attendance at lectures and, of course, personal contacts. For habitual exhibitors, conversion rates were well known, and often correlated with the attendance profile. And so you could, again, easily calculate your ROI.

Even direct mail companies included business reply cards (BRCs) in their mailers, where interested prospects would identify themselves, and their interests. These direct mail/direct response (DM/DR) programs had conversion rates that produced a predictable and repeatable return on investment that, again, could be easily calculated.

But with the rise of online marketing, the ability to attribute revenue production to a particular marketing initiative has been weakened. For instance, in many cases the adoption of consumer marketing techniques (such as search engine optimization, social media marketing, promotion and blogging), along with an emphasis on content (or inbound) marketing, has now turned accountability into a significant issue.

If you can’t identify the prospect, then you generally can’t tie the revenue to the program that produced it nd therefore you can’t calculate the true ROI for the program. At best you can only estimate it, or allocate the costs over a pool of revenues – a situation with some serious consequences.

This is best illustrated by the flow diagram below, which describes what happens to the output of a typical marketing program.

What Happens to Marketing Output?

As you can see, most marketing initiatives produce some type of output, whether it’s in the form of impressions, clicks, BRCs, appointments, downloads, or something else. In calculating your ROI, therefore, the first thing to consider is whether that output can be identified as being a specific individual or company.

If you can identify the respondent (noted by following path “A”), such as in the case of a direct mail or telemarketing program, or a download where the visitor enters their email address, you can post their information to your contact management system. You can then try to contact them, determine if they’re worth your time, probe for needs, and attempt to close them – either immediately, or after nurturing them for some time. And if you sum up all the revenues from all the closes that result from the initiative (subtracting the cost of the program), and divide that result by the cost of the program, you can get a reasonably good measure of your ROI.

On the other hand, if all you have is a set of IP addresses, clicks or impressions, then you have a problem (noted by following path “B”) – even if you truly believe that the program produced revenue. To be sure, you can attempt to identify the prospects by researching their IP addresses. (Any research cost would need to be added to the original program cost. And, of course, many IP addresses are shared which makes attribution problematic.) But if you can’t identify the prospect then the ability to attribute revenue to a given program, and therefore calculate its ROI, are limited.

As a way out of this problem, you could ask: How much revenue credit should be given to those programs where direct attribution is impossible (i.e. type B programs where research can’t provide you with the name of the prospect)? A rational method would make defending the program considerably easier.

One option is to calculate the ROIs for each type “A” program, the ones where you can identify the prospect. You can then subjectively estimate the degree to which each type “B” program influenced or enhanced the results of each type “A” program in order to estimate the ROIs for your type “B” programs.

For example, let’s say you ran two type “A” programs: a direct mail program that returned $20,000 in gross revenue for a cost of $500, and a telemarketing program that returned $40,000 for a cost of $500. This would give you ROIs of 3,900% and 7,900% respectively, as shown below.

Basic ROI Calculation for Type A Programs

Now, let’s say you also ran an SEO program and a blog, both of which are type “B” programs, and each of which also cost you $500. You can’t give them credit for revenues that you’ve already given to your type “A” programs, otherwise you’d be double counting – and that would invalidate the investment theory underlying the ROI concept. But you could take some revenue credit away from each of the type “A” programs, and allocate it to your type “B” programs, as shown below, as an alternative.

Adjustment for Type B Programs

As you can see in this example, we made some assumptions (hopefully collaboratively) that the revenues generated by the direct mail program were enhanced by 5 percent due to the SEO program, and 10 due to the blog. And we assumed that telemarketing results were 10 percent better because of the SEO program, and 15 percent better because of the blog. But this now gives us revenue numbers we can use to calculate each program’s ROI.

All that’s left to do now is subtract the revenues from the type A programs that have been credited to the type B programs, and you can get an adjusted estimate of ROI for each program that avoids double counting.

Adjusted ROIs

Obviously, the percent of the revenue credit that’s moved from one program to another is critical. But if your team can agree on this methodology, then it will put you in a far better position to make rational business decisions about how to allocate your scarce marketing dollars to get the best return on your investment.