How to Measure SEO’s Impact on B2B Revenue

By the time you finish this webinar, you’ll have the tools to calculate SEO’s impact on your business.

How to Measure SEO’s Impact on B2B Revenue

Here’s the problem with SEO: Most people have no idea if it’s working.

Especially the people who need to know, like founders and CEOs!

How about you? Ask yourself, “Does SEO make my company money?”

And I’m not talking about “organic traffic growth,” or “blog readership,” or any other response that doesn’t involve a dollar figure.

I’m asking “Can you accurately quantify the revenue SEO is generating for your business?”

If you can, congratulations. You’re ahead of the game. If you can’t, this article is for you (or your SEO vendor).

Here is what I’m going to teach you in this article

I’m going to make you one firm promise: By the time you finish this article, you’ll have the tools to calculate SEO’s impact on your business. You’ll also be able to benchmark and forecast the revenue your SEO work will bring you in the future.

Here’s the nitty-gritty of what we’re going to get into:

Commercial SEO clicks: What they mean and how to calculate them. Making sense of your data: Commercial-intent clicks are behind everything we will calculate, therefore we need to know how to manipulate the data in a way that makes sense. Marketing- and sales-qualified leads: Calculating the number of clicks required to generate each on-site conversion and sales-qualified lead. Extrapolating dollar values: Converting SEO efforts into a dollar value. Projecting future results: Using all of our data to determine what will happen in the future.

Are you ready to strap up? We’re going to get deep into the weeds, but don’t worry, I’ll hold your hand all the way to the end. Oh, and for the C-Suites in the audience, feel free to skip past the recipe and skip straight to the plated dinner.

Part 1. Commercial Intent Clicks

You’re probably a little more familiar with the term “transactional intent” than “commercial intent,” which refers to keywords searchers use when they are actively shopping for products or services. These are your money-making keywords.

For example, keywords like “Project management software” are used by searchers who are actively shopping for a product. In perfect contrast, someone searching “History of project management software” has zero shopping intent, and pure information-seeking intent.

But I’m not crazy about the “transactional” terminology because I feel that it resonates more with B2C and e-commerce business models than their B2B counterparts.

Most B2B products and services involve much longer sales cycles, and often result in a relationship rather than a one-off transaction.

So for our purposes, we are going to refer to the money-making keywords for B2B organizations as “Commercial intent.”

Cool? Alright, let’s dig into exactly what a commercial-intent click looks like.

What Is a Commercial-Intent Click?

Any searcher who finds your site using a keyword that is clearly for the purpose of shopping for your product or service is considered a “commercial-intent click.”

If you’re thinking that’s a little too subjective, here are some quick rules you can use to help judge whether or not a keyword has commercial intent:

It has to be clear that the searcher is actively shopping for products or services. The keyword has to be directly relevant to a product or service you provide. The ranking page they click on needs to be sales-oriented.

Pro tip: You can get a good sense of the true intent of a keyword by Googling it and eyeballing the Search Engine Results Page (SERP). Generally, keywords with commercial intent will result in product and/or service pages in SERPs, whereas keywords with informational intent will generate blog results.

Let me explain by using a few examples of our commercial-intent keywords and their corresponding pages.

Example 1

Keyword used: “Content marketing services”
Intent of search: To find a company that can provide content writing services.
Ranking page clicked on: Product landing page for content marketing services

In the above scenario, our product page ranks for the keyword “content marketing services” and many highly similar variant keywords.

Anyone searching these types of keywords are actively shopping for (and likely comparing) vendors that can satisfy their content needs. They have a high demand for our services and are the most likely visitors to convert and take a meeting with a sales rep.

Because of the highly targeted nature of this page, we can assume that people who find it through search have used a commercial-intent keyword. (It would be extremely rare for someone to find this page using an informational-intent keyword.)

So, by measuring the search traffic to this page, and all similar product/service pages, you will have an idea of the number of marketing-qualified visitors your site is getting on a daily, weekly and monthly basis.

Cool, so you can see that this page generated about 1,200 commercial-intent clicks over 6 months.

Pro tip: If you don’t have product or commercial landing pages with commercial-intent keyword targets, you’re falling behind. I can promise you that your competitors are taking this traffic.

Actionable steps:

Our goal is to quantify the number of qualified visitors coming to the site via commercial-intent keywords. These visitors are where the vast majority of your revenue comes from, so if we are able to establish a baseline and trends, we’ll have a better understanding of our SEO performance.

Create Segments for Your Commercial Pages

There are a few ways to do this depending on a number of individual factors. In general, you want to be able to quantify the number of search visitors to your product/service landing pages. I’ll walk you through 3 different methods for grabbing this data.

Google Analytics

The easiest way to measure your commercial-intent traffic in GA is to build advanced segments that isolate your product/service landing pages.

1. Create a new advanced segment

2. Name the segment and start adding conditions

3. Add the pages you want to measure

Change the first dropdown to “Landing page” and “exactly matches.” Enter multiple URLs by clicking “or” for each additional URL. Add all of your product/service page URLs.

4. Add an organic search filter

You want to make sure you’re looking at only organic search results. Add a new filter by clicking “And,” then click the dropdown and search for “Default channel grouping.” Change the filter to “exactly matches” and type “Organic search.”

5. Observe your traffic trends

Visually I can see that these two pages generate a consistent number of visitors on a monthly basis. I’ll benchmark the average of 247 per month.

Pro Tip: Segments only allow you to add up to 30 conditions, which can be very limiting and messy for certain domains like ours, which has more than 60 product/service pages. This method can get messy in ways that will take me another blog post to explain.

Google Search Console

Search Console is a more time-consuming solution, but comes with the benefit of not having to create special filters for organic traffic. If you have a lot of landing pages, this might not be a viable option as you will have to enter each URL individually.

1. Add a new page

2. Change the filter to “exact URL” and enter your URL

3. Adjust the time frame

Generally I like to take the average of 6-12 months. Anything less than 6 months is subject to seasonality and any number of factors that can throw anomalies into your analysis.

4. Observe your traffic trends

This page generates an average of 6.5 visitors per month. Unfortunately, this is just 1 of over 60 landing pages on our site, and I would have to run the same report for each, logging the data in a massive spreadsheet.

The method works, but can be prohibitively time consuming.

Ryte

Ryte is a Germany-based SEO company that has built an incredibly powerful tool that pulls all Search Console data (using Search Console alone limits your data to 999 rows) into a single platform.

Search Console is incredibly limited in its total available data, and its ability to manipulate and segment data. Ryte solves this problem by pulling in all your data, and giving you the ability to create massive segments based on pages, keywords, geographies, etc., and aggregating the data into individual reports. ler

And this “bulk upload” of URLs results in a beautifully aggregated data visualization and spreadsheet.

Congrats, once you’ve made it to this part, you’re 50% done. Because of how easily scalable and efficient it is to get this data using Ryte, it is our recommended method.

Part 2. Make Sense of Your Data

Pulling the data was the hard part. Now we need to make sense of it and present it in a way that your colleagues (and most importantly, the C-Suite) can understand.

The best way to do this is to aggregate your click data into a chart.

Actionable steps:

1. Pull monthly data from 1 of your 3 tools.

Similar to what I showed in the previous step, I’m going to use Ryte to pull some raw data.

Next, I’m going to put the raw data into a pretty looking chart. You can use any of the 3 methods listed above to do this.

2. Put the data into a spreadsheet and create a chart

Using either Google Sheets or an Excel spreadsheet, add your monthly data. Feel free to use this template.

This chart may not look like much now, but it’s going to serve as the groundwork for everything else we’re going to work on.

3. Get a sense of what’s happening in the chart

The whole point is to get a sense of trends and benchmarks.

In this example, you can see that our commercial-intent clicks range between about 1,000 clicks per month at the lowest, and just over 1,400 clicks at the peak.

It’s important to note the average number of monthly clicks, because you will find there is always some level of elasticity (meaning some months spike high and others come up short), which always regresses toward the mean.

Outside of seasonality, we can expect a given month will generate around 1,250 clicks, with a range of 200 clicks in either direction.

We’ll use this information later on to help interpret our other reports.

Part 3. SEO and Lead Generation

None of our previous observations and metrics matter without putting them in the context of conversions.

In other words, SEO and website traffic don’t matter unless taken in the context of money.

In this next step we are going to aggregate our conversion data, then compare it to our click data, giving us our very first glimpse into what SEO does for the bottom line.

Exciting stuff.

Actionable steps:

1. Aggregate your conversions

You should be tracking the commercially relevant conversions on your site. This is usually done through your customer relationship management system (CRM) like Salesforce, or through Google Analytics.

Some examples of commercially relevant conversions are:

Request a demo form. Contact us form. Request a quote form.

People who fill out these forms have the highest level of commercial intent.

Some examples of conversions that are NOT commercially relevant are:

White paper downloads. Newsletter subscriptions. eBook downloads.

People who take these types of actions have very little buyer intent and should not be counted. They certainly have value, but they tend to need a lot more nurturing before they are even ready to consider buying something.

Brafton has three different forms on the site in which visitors can express commercial interest in our products and services. We count these conversions in Salesforce automatically so that we can easily observe trends and pull data:

I’ll then take that data and put it into the second tab of our sheet.

Next we need to see how often clicks turn into conversions, with 1 simple formula.

2. Calculate conversion rates

Like I mentioned before, traffic means nothing unless it converts. We need to know how often this happens so we can set some benchmarks.

The easiest way to do this is to simply divide your conversions into your clicks. Referring back to our sheet, you’ll see I have already set up a formula and chart to observe this data.

Look at this beautiful chart! Now let’s work on turning this data into a story.

Stories you can take away from this chart:

1. Months with unusually high numbers of required clicks per conversion can be the result of a few things that should be examined.

Poor market confidence (trend): As you can see clearly in this chart, the months of March through August demonstrated unusually inefficient conversion activity. This was primarily due to the pandemic, leaving organizations less likely to commit to a sales demo with the intent of taking on more financial burden during uncertain times. A problem with the site (trend): If your forms are broken, or your pages are loading too slowly, you’re not going to get your forms filled. You’ll see a sudden dip in conversions per click. You might also see this happen if your keyword targeting is poor, or rather, getting worse as you add pages. Bad luck (anomaly): Outside of predictable seasonality, anomalies happen all the time. Some months are just plain bad. Chalk it up to a cold streak that you will probably sink back down to Earth from.

2. Months with unusually low numbers of required clicks per conversion can be the result of a few things that should be examined:

Strong market confidence (trend): Months that dip significantly below the monthly average can be the result of very high buyer confidence. This can be the result of a renewed interest in your product or service, seasonality or simply strong economic signals. A lucky month (anomaly): The flipside of an unlucky month is a lucky month. If you observe an anomalous month or two that are unrelated to any clear economic factors or seasonality, you’re probably just on a hot streak. And you should assume that you’ll eventually regress toward the mean.

After reviewing your data and trends, you should start to get an idea of roughly how many commercial-intent clicks you need to generate a conversion. In our case, we should be around 3 ½ clicks per conversion.

Also, you should be able to narrow down the likely reasons for ongoing positive or negative trends and monthly anomalies.

Pro tip: Just remember, absent any major external factors or changes to your site, unusually strong or weak months are very likely to pull back to the average. Remember this when doing your monthly reporting and forecasting!

Part 4. SEO and Sales-Qualified Leads

We get a ton of junk coming through our forms. There’s no way around it. People try to pitch us products, request guest blogging, ask to be on our podcast and inquire about job applications. And as your search visibility grows, this problem will only get worse.

To exemplify this, three years ago Brafton used to qualify about 45% of form fills to sales. As of 2021, we now qualify less than 15% of form fills to sales. More visibility = more junk.

All day we get this…

Sales-Qualified Leads (SQLs)

While form fills are a great indicator of market sentiment, SQLa are the Holy Grail of inbound marketing. And, SQLs are also the Holy Grail of evaluating the efficacy of an SEO program.

Every organization qualifies leads differently, so this data will look very different for you than for me. Here’s how we qualify our leads:

Organization of over 10 employees. Expresses interest in a product or service we currently offer. Not a competitor or reseller of our product/service offerings.

If a form fill satisfies these criteria, we will qualify the lead and assign it to a sales representative.

Getting back to SEO, we want to know how often this happens in relation to commercial-intent clicks and form fills. Leads assigned to sales (SQLs) directly result in revenue. Therefore, SEO that results in SQLs can easily be quantified in terms of ROI.

Actionable steps:

1. Aggregate your SQLs

Similar to form fills, we track all Sales Qualified Leads that result from SEO activities.

Let’s take this data and add it to the third tab of our sheet.

2. See how often commercial-intent clicks turn into an SQL

This is the ultimate metric we want to solve for and benchmark against. Clicks per SQL metrics provide a treasure trove of insights into:

How much traffic you need to drive to commercial pages to generate sales demos. Using benchmarks to determine unusually high/low performing months. Understanding market sentiment (buyer confidence). Forecasting future performance. Projecting the impact of future content creation efforts.

To get clicks-to-SQL metrics, we will simply divide the number of SQLs by the number of commercial-intent clicks.

This looks like a wide range of fluctuation from the beginning of the year to the end, to be certain. However, remember that these metrics are telling a story. The story of this table tells us that the beginning of the year showed very high efficiency, followed immediately by a period of extreme inefficiency due to the pandemic.

As buyer confidence eroded during the height of the pandemic, fewer qualified visitors were apt to fill out a form with the intent of talking to a sales rep.

Towards the end of the year, you can see our efficiency started to normalize between 19-22 clicks required per assigned lead.

What you should take away from this data:

1. The number of clicks you need per qualified lead

On average, we require 23.1 clicks to generate 1 SQL (4.3% of all clicks). That’s extremely valuable, as it draws a clear line between SEO efforts and the commercial results they generate. Further, you’ll be able to justify future SEO initiatives, like building more commercial landing pages, by using these numbers to project the number of leads you will create.

2. Benchmarking

On any given non-anomalous or pandemic month, we will generate an SQL every 20ish clicks to the site. Without this benchmark we would have no idea what an anomalous month would look like, or how much you would bounce back from one.

3. Forecasting

As you’re hopefully starting to see, recognizing bad months is as important as recognizing strong months. By benchmarking your range of clicks required per SQL, you’ll be able to predict a pullback in either direction.

For example, take a look at the number of SQLs generated in the month of May:

Now take a look at the number of clicks required to generate those leads:

Now that we have a full year’s worth of data, we know that January outperformed by a very large margin. If this were to happen again, we would expect a strong pullback for the month of February. And as our clicks per SQL came back down to earth, our total SQLs followed suit.

In conclusion, you should see an inverse relationship between clicks/SQL and total SQL assigned. And when your metrics fall outside the normal range, you can predict a regression toward the mean.

Part 5: Extrapolating Dollar Values

So far we have drawn a line from SEO to marketing-qualified leads, leaving us in pretty good shape to make a short hop to actual revenue generated from SQLs.

Someone on your sales team should have this data handy. If not, I’m going to run you through a few quick equations you need handy, along with a few SEO values based on clicks, form fills, SQLs and met meetings.

With this data, you’ll be able to get a firm understanding of the underlying numbers driving your inbound. And you’ll start to get a sense of if something is out of a whack on any given month. Let’s go through some important calculations:

Useful Sales Metrics

1. Percentage leads assigned as SQL: Like I mentioned earlier, not all your form fills are going to convert to SQLs. By dividing the number of total form fills by SQLs, you will get the percentage SQL assigned number.

SQLs/ Website form fills = Percentage leads assigned SQL

Example:

90 SQL average/ 450 form fill average =20% Percentage leads assigned SQL

Pro tip: If you start seeing your percentage of form fills assigned as SQLs increase or decrease way off the baseline, you should dig in. Using what we’ve learned previously, it will be a trend, or an anomaly.

Example: As you can see above, there were a couple instances where the numbers fell well outside the normal range. You would report these as false positive and false negative months, assuming that the upcoming corrections will be felt downstream with SQLs and ultimately, revenue.

2. SQL to met meetings: Not all Sales Qualified Leads turn into a met meeting, unfortunately. You need to determine the rate at which SQLs turn into met meetings.

Met meetings / Sales Qualified Leads = Percentage SQL to met meetings

Example:

45 met meeting average / 90 SQL Average =50% SQL to met meeting

Pro tip: If you start seeing your numbers fall, then there is likely something wrong with your prospect follow-up workflow. Either your sales reps are overworked and don’t have the resources to chase leads. There can also be a deeper economic problem that’s preventing leads from taking meetings.

Example: Take a look at May through August. The above hypothetical scenario shows that 60% of SQLs should result in a meeting, far missing the mark for over a quarter. The resulted in numerous missed sales demos. In August, we hired another sales rep to shoulder some of the burden, and our conversion rates went right back up to normal.

3. Met meetings close/win rates: Simply put, what percentage of meetings result in a closed deal? This should be the easiest metric to calculate.

Closed Deals / Met Meetings= Closed win rate

Example:

8 Closed deals average / 45 met meeting average =17.8% closed win rate

Pro tip: If you’re noticing any sort of theme happening here, it’s that you should be aware of anomalies, and the certainty that your numbers will always come back to their baseline. In this instance, it’s highly unlikely that your sales team is going to get significantly better or worse at selling, so the most likely scenario for rising and dropping rates is either the quality of the leads being sent, or moments of luck/lack of luck.

Example: In the above example we can see two clear things happening: an anomaly and a negative trend. The month of May showed extremely high close rates against our benchmark of 18%. It immediately fell back to Earth thereafter, as expected. Then, starting in September we saw a three month slide well below our benchmark. This was due to very poor lead quality, resulting in a lack of demand.

4. Average deal size: You can calculate this in 2 ways, one is easier and the other is more accurate. The easy way is to divide the number of closed deals by new business revenue to determine the average deal size. The more accurate way is to calculate the lifetime revenue of a closed won deal, which takes a lot more digging. We will stick with the simple version for now.

Closed won deals / New business revenue=Average deal size

Example:

8 closed deals / $100,000 new business =$12,500 average deal size

Pro tip: Your average deal sizes are probably going to vary significantly from month to month. I would highly recommend taking your yearly, or even two-yearly average.

Useful SEO – Tying it all together

Each of the previous metrics are useful in that they help us get a little closer to calculating the value of what’s happening from an SEO standpoint. We are going to be taking a look at:

Value per click. Value per form fill. Value per assigned SQL.

Let’s take a look at each of these.

1. Value per click: This is a very simple, but extremely important metric that will tell you what each commercial SEO click is worth. It may seem crude, but from a reporting standpoint, it’s easy for you C-suite audience to connect with a dollar figure. It also helps contextualize what it would cost to replace organic clicks with PPC.

Average monthly revenue from SEO / Commercial clicks= Value per click

Example:

$100,000 average monthly revenue from SEO / 1,225 clicks =$81.63 per click

Given a value of $81.63 per click, with an average monthly number of clicks at 1,225, our monthly SEO value is $99,225. Yearly that number is $1,190,700

Pro tip: This is the number you would report to the C-Suite or your client. Assuming you have recorded everything properly, you can accurately state the value of each click. You can also use this information to argue for additional SEO efforts.

2. Value per form fill: How much is a form fill worth? We know that only a certain percentage of form fills result in Sales Qualified Leads, so the value will be discounted accordingly.

Form fills / Revenue= Value per form fill

Example:

$100,000 average monthly revenue from SEO / 340 form fills =$294 per form fill

Pro tip: This may seem high given the low percentage of leads assigned SQL, but you need to remember that this is all accounted for in our calculations. Consider that you will get the same number if you calculate: % form fills qualified to sales * % leads met * close rate * average deal size divided by the number of form fills.

3. Value per assigned SQL: How much a Sales Qualified Lead from SEO worth? This is the ultimate test

Average monthly revenue from SEO / Average monthly SQLs= Value per SQL

Example:

$100,000 average monthly revenue from SEO / 54.7 average SQL =$1,828 per SQL

Pro tip: Again, remember that this takes into account all of our previous calculations. This number tells you the true value of what SEO is doing for your organization.

So if you were to try to push for more budget, you would explain that each SQL from SEO activities requires 23.1 commercial intent clicks, worth $1,828 per SQL. If you were to forecast an additional 100 clicks per month, you could project an added value of around $7,312 monthly, and $87,744 yearly.

Conclusion

This entire exercise was meant to give you the tools and insight to benchmark, forecast, and report on what’s happening from a commercial SEO standpoint. At this point you should be able to benchmark normal ranges of activities and identify anomalous or positive or negative trending behavior.

Further, you should also be able to explain why the scenario happened.

Lastly, you should feel comfortable reporting to the C-Suite or your client in a way they will truly understand: Dollars and cents. And as always, if you have any trouble calculating these numbers for yourself, send me a note, I’m always happy to help!