Yet despite the considerable evidence against its use, many SEOs still rely on rank reporting to communicate campaign successes. Unlike other communication mediums, organic search has a certain intangible quality to it. It can seem like witchcraft and voodoo to the lay observer (or lay consumer of services), and so SEOs point to the rank report as the key piece of evidence that their counsel is having a demonstrable impact.
The fact that this is often the normal mode of operation is the result of two truths: 1) SEO is a service that is sold as one that will improve a business’ position (i.e. rank) across the search results pages; 2) a viable alternative has yet to present itself.
What is needed is a replacement measure that would better equip the SEO to understand, and report on, the quantitative impact of their efforts. What is needed is a new KPI. I call it return on rank (ROR).
Similar to KPIs return on investment (ROI) and return on ad spend (ROAS), used to quantify returns from SEM campaigns, ROR would help SEOs quantify the return from their efforts and enable them to prioritize future rounds of optimization. Revenue generated from organic search is tracked just as it would be from other channels; the “cost” of SEO is unique to each company, and involves human capital + technology investments + other miscellaneous costs. The return then becomes a fairly straightforward calculation, with some known caveats:
Beyond the core ROR calculation, a powerful predictive model can be introduced that identifies anticipated incremental revenue based on average rank improvements on a per-keyword basis. SEOs would then be able to best prioritize their immediate next step actions. Couple that knowledge with insights from Moz’s Keyword Difficulty tool, and a formidable intelligence mix emerges. SEOs would know with relative certainty which keyword fights were worth picking before expending any effort.
We believe that with enough observations over time, this can become both a legitimate SEO ROI calculation and a smart predictive analytics resource.
This is my first contribution for the esteemed readers of Metric Insider. I’m eager to read your thoughts and field any questions.