RICE Model Forecasting for SEO

RICE Model Forecasting For SEO

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Heidi Klum Gif

Investing in anything at the moment is not an easy decision. Now imagine you have to invest in something as perceptually flaky as SEO. Now imagine you have to convince someone else to invest in SEO. You’d better have your shit together.

With that in mind, I’d like to offer you Local SEO Guide’s version of the RICE Model for SEO. The RICE Model provides a data-driven way to forecast potential results from SEO projects. While it’s by no means perfect, it provides managers with a way to prioritize SEO activities against other priorities and against each other.

“RICE” stands for Reach, Impact, Confidence and Effort, which are defined as follows:

Reach: The % of pages affected by the project. For example, if you are recommending updating a template with 10,000 URLs out of a 100,000 URL site, the Reach would be 10 (we don’t use % for Reach to make the final score larger and easier for management types to grok).

Impact: The % traffic lift expected by the project. This will typically be a guess based on your experience with similar projects.

Confidence: A score that reflects how confident you are that the project will succeed. We use the following scale for Confidence:

90 Google explicit statement or we have conducted our own tests
80 Google implied (Looking at you @JohnMu & @Methode)
70 We have seen this work in the past, but not certain it still works
60 Reputable pros have stated but we don’t know
50 We need to test

Of course, you can tweak these based on your preferences. For example, a Google explicit statement about an SEO technique might equal a Confidence score of 0.01 to you 😉

Effort: The estimated # of Dev days or hours the project will take.

You then put these data in the following formula to get the RICE Score of the project:

(Reach x Impact x Confidence)/Effort = RICE Score

Once you do this for all projects then you can compare the RICE Scores of each to prioritize. You can also add estimated revenue impact based on these scores to have another metric to work with. Once you have the basic formulas, the model can become pretty flexible to accomodate variables specific to your projects.

Anyhow, here’s a free version for you to use.

Happy forecasting!

h/t to Traian Neacsu for the inspiration!


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