One of the more inter­est­ing con­cepts pre­sented at last week’s SES show in San Jose detailed the use of pre­dic­tive rev­enue mod­el­ing for SEO and paid search. A for­mer col­league of mine, Dave Roth of Yahoo!, runs search pro­grams on behalf of Yahoo!‘s prop­er­ties. He dis­cussed their use of pre­dic­tive data mod­els to gauge how they were per­form­ing against their competition.

In SEO it works like this:

For each key­word, cre­ate an “Oppor­tu­nity report” that looks at your cur­rent organic rank and click vol­ume ver­sus your top com­peti­tors, and mea­sure the gaps. Then map those gaps to Life­time Value (LTV) to quan­tify their value. If, for exam­ple, you know how much rev­enue you made in posi­tion #1 ver­sus posi­tion #2, you can quan­tify your rev­enue losses or gains based on rank. In other words, how much rev­enue are you los­ing by not rank­ing higher than your com­peti­tors? Noth­ing gets an executive’s atten­tion faster than lost rev­enue, espe­cially if you can see (at least roughly) how much rev­enue your com­peti­tors are earn­ing by out­rank­ing you.

In paid search it works sim­i­larly, but is a lit­tle more complex:

For each key­word, cre­ate an LTV model based on past rev­enue per­for­mance, click vol­ume, rank, CPC and click costs. By fac­tor­ing in click costs you are cap­tur­ing your effi­ciency over time because click costs account for your qual­ity score. At Yahoo! Dave explained that they refore­cast every month to stay on top of rapidly chang­ing search mar­kets, then com­mu­ni­cate the updated num­bers across all rel­e­vant inter­nal depart­ments so every­body knows what rev­enue gains or losses to expect for that month. At Yahoo! they use a “score­card” to mea­sure each Yahoo! prop­erty vs. another, all based on ROI (both imme­di­ate for that month and LTV). My favorite line in Dave’s pre­sen­ta­tion was a Yahoo! mantra: “If you can’t quan­tify it, it doesn’t exist.”

In a world where every­thing is quan­ti­fied, all of your actions can be based on data and rev­enue mod­els; and per­haps even more impor­tantly, you can com­mu­ni­cate poten­tial fluc­tu­a­tions in rev­enue BEFORE they happen.

Make sure your toolset can read your search per­for­mance data across both paid and organic so you don’t have to marry two dif­fer­ent reports together. Hav­ing that data in two sep­a­rate sys­tems leaves room for human error when com­bin­ing the data sets. Don’t set­tle for any­thing less than a holis­tic approach to ana­lyz­ing the per­for­mance of your search pro­grams, and don’t rule out the impor­tance of pre­dic­tive rev­enue mod­els for your organization.


Hello Bill, I have always done this when i pitch to my clients. I call this the "Lost Opportunity Report". But then when the revenue losses or gains is dependant on rank, then there acceptance seems to be positive, else it has been on the negative... Nice article... Palani