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Content StrategySEO

Forecasting Organic Growth: All Models Are Wrong, But Some Are Useful

omniscient digital all organic traffic growth models are wrong

“What can we expect from investing in content and SEO?”

That’s the question every content marketer gets and any good marketer will start their answer with “It depends…”

It depends on how much content has already been produced.

It depends on how old the website is.

It depends on how much organic traffic the website already gets.

It depends on how many backlinks it has.

It depends on whether Google decides to change their search algorithm this Tuesday.

And on and on.

Regardless, of all the variables, it makes sense that a company prefers to invest their money in things they understand and believe they can get a return from.

That’s why we do traffic growth forecasts for our prospective clients.

Though, we make it clear that there’s a caveat: it’s going to be wrong.

One more time, all models are wrong.

We’re transparent that the model will be fragile, a rough estimate that only gives a sense of what’s possible, not what will happen.

Here’s how we build an organic traffic model and how it helps businesses like yours decide whether or not to invest in content now.

Follow along with our template:

What’s an organic traffic growth model?

A model is a prediction of future performance. To be more specific, according to Wikipedia

Quantitative forecasting models are used to forecast future data as a function of past data. They are appropriate to use when past numerical data is available and when it is reasonable to assume that some of the patterns in the data are expected to continue into the future.

In this case, we look at historical organic traffic growth and forecast what it would look like should it continue on that same growth trajectory considering any previous trends.

Then we layer on how it could potentially grow if implemented a new SEO and content strategy.

How we build our traffic growth models

We build our traffic growth model by first looking at the current organic traffic a website has and understanding the traffic trend over at least the last six months, ideally the last twelve months.

Then we work with our client to understand what topics and ideas they want to be known for and who their competitors are. These topics and competitors act as starting points for our keyword research to create a product-led content strategy.

Once we come up with a list of target keywords we prioritize the keywords based on relevancy, user purchase intent, keyword difficulty, and monthly search volume (MSV). 

When we have a shortlist of target keywords we take the keywords and put them into our forecasting spreadsheet.

(Get a template of this spreadsheet.)

The columns on the right in the image above show the ceiling for traffic opportunity based on the assumptions below.

Average SERP Rank1234
Estimated Clickthrough Rate30%15%10%6%

The assumption above are based on ranking positions we assume we can achieve for our clients and the average clickthrough rates on the search engine results page (SERPs) for that ranking.

Some clients are in very competitive spaces so we have to be clear that aiming for position 1 may not be possible in the short-term. (We’re not in the business of promising the moon.)

We then take those traffic ceilings and assume we can reach that traffic ceiling in the next 12 months based on linear growth giving us the table below.

In this example, we assume the client is currently at 10,000 monthly organic traffic views.

You can see that we’ve also added rows for conversion rates (CVR) for clients to understand what kind of leads or product signups they can expect.

In this example, we assume they’re currently at a 1% visitor-to-lead conversion rate and aim to improve that by 5% (relative improvement) every month.

The improvement in conversion rate happens in two ways:

  1. The content should be created to target mid-to-high purchase intent keywords and naturally include references to your product. This means people who read the content will be encouraged to sign up for your product. (Get in touch if you’d like our help with this.)
  2. The website itself has multiple conversion points or landing pages to turn viewers into leads. We assume you will continue to optimize those landing pages over time as the content directs people to those assets.

When we turn that table into a clean chart, we get the following visuals that show the potential organic traffic and lead growth trajectory if the client decided to commit to the content strategy for a year.

What do we actually do with this model?

A model can be useful in providing rough math to help you understand the opportunity of investing in content marketing and decide whether you want to invest in it.

It gives you a data-informed perspective of making a yes/no decision to content marketing.

If you decide yes, the model helps you understand when you’ll breakeven on your investment.

It’s one thing to make assumptions that a small investment will generate small returns and similarly a big investment will generate big returns. It’s another to have some rough calculations to back up those assumptions.

Depending on the conversion rate of a lead to a customer and the value of a lead, you can create an additional model to understand when you’d get a return on your content marketing investment.

For example, when looking at the model below for ranking in position 1, the first three months will not generate positive returns. We’d see a breakeven point in month 4 and from there the return on investment continues to improve over time as the organic traffic and conversion rate improve.

If we look at a scenario for an average ranking in position 2, we see a different story where the breakeven point happens at month 6.

Get the organic traffic growth model template and create predictable traffic and lead growth.

David Khim

David is co-founder and CEO of Omniscient Digital. He previously served as head of growth at and Fishtown Analytics, and before that was growth product manager at HubSpot where he worked on new user acquisition initiatives to scale the product-led go-to-market.