Last edited:
February 1, 2024

Calculating Link Building ROI Using a Monte Carlo Simulation (with Template)

Anything can be measured. If something can be observed in a way at all, it lends itself to some type of measurement method. No matter how fuzzy the measurement is, it’s still a measurement if it tells you more than you knew before”
— Douglas Hubbard

If you’re a management-level SEO or marketing professional, you’re likely making MANY decisions on a daily basis with imperfect information.

That inevitability is part of the game you’re playing.

To make better and more informed decisions, your goal should be to reduce uncertainty in the decision-making process. What’s the best way to do that?

Measurement.

Measurement is simply a reduction in uncertainty that is conveyed quantitatively and based on at least one observation.

When it comes to digital PR (i.e., link building), there’s no exact science for measuring ROI.

From an ROI reporting perspective (i.e., calculating ROI after the fact), it’s impossible to hold all other variables constant except the links you build.

From an ROI forecasting perspective (i.e., predicting what the ROI might be), it’s impossible to know exactly what links to build and what the resulting revenue gains from those links will be.

Does that mean you should abandon any attempt at calculating a return?

No.

By failing to calculate a return on link building, you’re ignoring the fact that there are techniques available that reduce the uncertainty in determining if link building can provide your organization with a positive return.

In this post, I’ll cover the two mistakes I see organizations making when determining link building investment, what a Monte Carlo simulation is, what Fermi decomposition is, and how to use a Monte Carlo simulation to model the return on link building.

Use the form below to get your copy of the template. You'll get it via email. I don't spam people who download templates with emails.

A Simple Assumption about Organic Traffic

Before you begin calculating the return on link building, it’s important to understand an assumption you’ll be making.

When you invest in organic search traffic acquisition, your ROI will usually increase over time.

Why?

Once you are ranking, the resources you need to invest to keep ranking are usually MUCH less than the resources it took to begin ranking in the first place.

When you first begin link building, you’ll need to close the link gap between your site and your competitor’s. The link gap is the difference in the number of backlinks between your site and the competitors ranking for the keywords you want to rank for.

The bulk of your investment will be in closing this link gap.

Once that gap is closed, you just need to ensure your link velocity is greater than or equal to your competition’s, which is usually much cheaper to maintain.

When you calculate the ROI on link building, I recommend calculating it over at least twice the amount of time you think it will take to close the link gap.

For example, let’s say you think you can close the link gap in 6 months. You’d want to measure ROI over 12 months or more.

The assumption we’ll be using when calculating ROI on link building is that ONLY once you close the link gap will you begin generating revenue attributed to organic traffic.

In reality, you’ll likely see benefits from link building way before you close the link gap, but for the purposes of forecasting ROI, it’s best to use this assumption so you know you’re underestimating the return.

If the ROI is still positive AND you know you’re underestimating it, you should feel good about your link building investment. I’ll show you where this assumption comes into play when we start building the model below.

What is a Monte Carlo Simulation?

"Monte Carlo simulations are used to model the probability of different outcomes in a process that cannot easily be predicted due to the intervention of random variables. It is a technique used to understand the impact of risk and uncertainty in prediction and forecasting models."
— Investopedia

A Monte Carlo simulation is a powerful statistical method you can use when you need to make an estimate or forecast where there is significant uncertainty (i.e., calculating the ROI on link building). The simulation creates tens of thousands (or millions) of different random scenarios and then provides you with the probability of various outcomes.

In the Google Sheets template I provided, I’m running 10,000 different scenarios in the simulation, but if you want to run 1,000,000 or even 10,000,000, you can easily rebuild the logic into a Python script.

What is a Fermi Problem?

According to Wikipedia, a Fermi problem “is an estimations problem designed to teach dimensional analysis or approximation of extreme scientific calculations, and such a problem is usually a back-of-the-envelope calculation.”

Fermi decomposition is the term used to describe the approach to solving Fermi problems. To teach Fermi decomposition, Enrico Fermi (the originator of the Fermi problem) used to ask his students to estimate the number of piano tuners (i.e. people that tune pianos) in Chicago. The only information he’d give them was the population size of Chicago.

Thanks to Nasa, here’s the explanation of the problem:

As a lecturer, Enrico Fermi used to challenge his classes with problems that, at first glance, seemed impossible. One such problem was that of estimating the number of piano tuners in Chicago given only the population of the city. When the class returned a blank stare at their esteemed professor, he would proceed along these lines:

From the almanac, we know that Chicago has a population of about 3 million people.

Now, assume that an average family contains four members so that the number of families in Chicago must be about 750,000.

If the average piano tuner serviced four pianos every day of the week for five days, rested on weekends, and had a two-week vacation during the summer, then in one year (52 weeks) he would service 1,000 pianos. 150,000/(4 × 50) = 150, so that there must be about 150 piano tuners in Chicago.

This method does not guarantee correct results; but it does establish a first estimate which might be off by no more than a factor of 2 or 3--certainly well within a factor of, say, 10. We know, for example, that we should not expect 15 piano tuners or 1,500 piano tuners (a factor of 10 error, by the way, is referred to as being 'to within cosmological accuracy.' Cosmologists are a somewhat different breed from physicists, evidently)!

We can apply the concept of Fermi decomposition to link building and use it to help us estimate ROI.

For example, let’s say you are estimating what your revenue per visit (RPV) is from organic traffic.

If you’re tracking everything appropriately, you should know your current RPV (let’s say it’s $0.20).

Now, we know that the RPV could either increase, decrease, or stay the same, but by how much? Ask yourself, could the RPV drop to $.10? Maybe. What about $0.01. Definitely not. If you looked at your RPV historically, maybe the lowest it's ever been is $0.12. Armed with this information, you’d be VERY surprised if RPV went any lower than $0.05, so you’ll use that as your lower bound.

Next, to find the upper bound, could you see RPV reach $0.50? Possibly, what about $5? No, that would be too high based on the products you sell and the traffic potential you know you have available. Historically, the highest your RPV has ever been was $0.45. Using $0.60 as your upper bound wouldn’t be unreasonable.

Now we know the range for RPV is $0.05 to $0.60. By deconstructing the problem using the data you have available, some common sense, and Fermi decomposition, you can, with reasonable accuracy, estimate a range for your inputs.

You will want to use this approach to estimating ranges when you build out your Monte Carlo simulation below.

Final Thoughts

When it comes to acquiring organic traffic and SEO, there are no guarantees.

At the end of the day, you’re dependent on a search engine that you have zero control over.

That doesn’t mean you shouldn’t invest. You’d be missing out on a massive upside.

It also doesn’t mean you should be blindly investing just because you know there is a massive upside. If you do that, you’re exposing yourself to a ton of risk and uncertainty.

The best way to reduce risk and uncertainty is to use business intelligence, analytics, and measurement.

The Monte Carlo simulation paired with Fermi decomposition is a perfect example of this.

If you want a business intelligence partner for organic search acquisition, you can schedule a consultation with us.

Author:
Skyler Reeves