Marketing has always been about understanding and designing behaviors.

To achieve this goal, we need to communicate effectively with our audience.

The content is the necessary means of delivering these messages.

It has the power to convince, entertain, inspire and inform people.

With the ability to attract Google traffic through images, videos, and even podcasts, the potential for content marketing continues to grow.

But how do we know that our content works?

The logic can drop here.

The American Marketing Association and Deloitte's 2020 CMO survey found that:

  • Only 35% of experienced marketers can quantify the impact of their marketing.
  • A further 44% say they have a qualitative feeling for whether marketing works, but cannot prove this quantitatively.
  • The remaining 21% still cannot demonstrate the direct impact of their marketing efforts.

According to a BrightEdge study, only 20% of consumers are concerned with B2C content.

Brands instinctively know that it must be worth investing in content marketing.

However, there is a noticeable separation between this qualitative feeling that content “works” and the quantitative evidence that it works for them.



Without this insight, it is impossible to use the full range of possibilities of content marketing.

With attention to data and a focus on what drives customers, attribution can help us shed light on the true value of content for meaningful business metrics.

The value of content for SEO & Beyond

Consumers always have the world's information at hand and usually go to a search engine if they have a scratch.

Simply put, without content there would be no SEO. After all, there is nothing for us to optimize for the search engines.

The search engine results pages (SERPs) are much more dynamic than ever and contain a number of multimedia resources.

However, numerous factors have made it much easier to quantify the value of content in the past:

Users would enter standardized key phrases. For example "Thailand vacation" or "Buy sneakers online".

  • The search was primarily done on the desktop, reducing the potential for hyperlocal results.
  • The content was almost entirely text-based.
  • Although the click rates were discussed a lot, they were relatively stable.
  • Paid ads and organic entries took up predictable storage space in the SERPs.



These static and reliable patterns are great for aligning landing pages with keywords, keywords with rankings, and rankings with clicks.

Based on these simple factors, it was then possible to calculate a rough projection of the market value.

More importantly, you could look back and see if the content had achieved those goals.

As a result, marketers could isolate SEO as a separate content channel and report on the effectiveness of their new pages.

This sense of certainty has always been illusory, and the veil has been lifted by Google's updates over the past decade.

For example, Google today has a much more sophisticated understanding of the written language.

The Hummingbird and BERT updates are just two examples that have significantly improved this field.

In the SERPs, paid and organic offerings increasingly mix in shared spaces, and individual consumers see different results depending on their preferences, their device, and their location.

The old certainties crumble.

Searches are much more diverse because customers get better results when they're more specific.

Why search for “Thailand Holidays” when you can determine the exact type of vacation you want?

Mobile devices offer Google a range of rich intent signals that can also be used to personalize search results.

2010 Serps vs 2020 Serps

In short, Google can understand its users better and therefore show them better results.

For marketers, the causal chain of events from the landing page to rankings and clicks on leads has been broken.

Rankings are unstable, search volumes are unreliable, the data are not convincing.

We can offer a much better experience through personalized multimedia results – but can we measure the impact?

Shift in focus – from rank to sales

By focusing too strongly on rankings, marketers have missed the true value of content marketing.

This also led to a separation between marketing and CFO using metrics that had no clear value outside of their original context.



The standard last-click approach to attribution (where the full value of the conversion is assigned to the final referral channel) was often the best we had, and it at least focused sales on a marketing channel.

However, this approach is not in line with the consumer journey.

If we report the data incorrectly, the opportunities for improvement will immediately be lost to our access.

In reality, customers don't make decisions by searching, clicking, and buying in one session.

They rummage around, they change devices, they check social media, they talk to their friends.

The customer journey is increasingly fragmented across channels and devices, which further removes us from the last-click attribution model.

The last click is important, but we know that the dozens of clicks before it were important too.

Moving away from this flawed but reliable model has led some to the nebulous areas of "awareness" and "engagement" in search of new metrics.

Simon Bell, professor of marketing at the University of Melbourne, told Deloitte:



"Accurate mapping is the biggest problem we have as a marketer. Digital metrics were seen as our savior, but instead they only created more confusion."

There is another way

This is not an inevitable condition.

Large publishers such as the New York Times and Condé Nast have created their own attribution models using machine learning.

These companies want to know which items are most likely to induce free users to upgrade to a paid subscription.

Using a simple, rule-based model, they could easily see which article was the last one the user had read before logging in.

However, this would only tell part of the story.

If you look at the user's full interactions and combine them with engagement metrics such as time on page and scrolling depth, you can determine which articles have the greatest impact on the defined goal.

These trips can then be compared to other users' paths to isolate the key factors that contribute to a decision.



This insight is invaluable.

Publishers can learn a lot about their audience base, which helps them design marketing messaging and targeting.

For example, they now know which articles to use in their paid social media or paid search campaigns for different audience segments.

The data can also be fed into the editorial team and the product team.

In addition, marketing efforts can be measured by their contribution to this goal and not by channel-specific indicators such as likes, shares or rankings.

Define the content value with attribution

There are numerous lessons here, but the most universal principle is that successful attribution models are based on one goal.

As marketers, we have made it our business to influence behavior.

Without knowing what changes we want to make, it is difficult to develop a reliable value assignment model.

The publishers discussed above have a clear metric (new subscribers) that is important for all departments.

For other companies, this could instead be a forward-looking metric like Customer Lifetime Value (CLV).



Not every company can or wants to develop its own models to answer such questions.

Fortunately, most companies don't have to go that far.

As Google continues to provide access to its data-driven attribution services, free Google Analytics users will soon be able to create their own version of the New York Times model mentioned above.


There is currently a lot of room for experimenting and adapting the rule-based models in analysis platforms.

For companies whose customers usually have a long purchase path, e.g. For example, in the automotive or luxury watch marketers, marketers may find that early interactions are important.

Attracting the consumer’s attention and interest is what triggers the rest of the interactions.

Therefore, these brands can use a position-based, first-click or linear attribution model.



Position-based, the first and the last interaction (40% each) would be taken into account equally and the remaining 20% ​​would be divided equally between the intermediate levels.

This would reflect the value of the content that has drawn the consumer's attention and the latest content that has completed the sale.

A first click would give 100% of the credit to the first interaction, while the linear option simply distributes the credit evenly across all interactions on the trip.

Today, marketers can immerse themselves in their data on their analysis platform to see what the typical customer journey looks like.

The Google Analytics Delay Report (Conversions> Multi-Channel Funnels> Delay) shows the time between the first interaction and a conversion.

Conversions & Conversion Value

The Top Conversion Paths tab shows the channels that users typically interact with and in what order.



In the meantime, the path length report shows how many interactions there are on a typical buying trip.

The goal here is not only to look back at how customers have dealt with your content, but also to shape the future strategy by understanding where the value of content lies.

Decide what types of content to produce

Using this analysis, you can uncover social media posts, press releases, or YouTube videos that have more impact on conversions than the last click would ever do.

For example, if marketers find that the early phases of the customer journey are more important than previously thought, they can strengthen the business model for a stronger investment in this future.

This can mean that you create more video content to rate based on the universal results from Google or directly on YouTube.

Brands can then track that content performance to see how much it is contributing to the company's business goals.

You can also consider numerous other ways to monetize your content.



As a rule, customer journey models consist of a few phases, e.g. B. Awareness, interest, decision and action.

There is then a dotted line between these phases and the assumption that the customer moves into the interest phase when content does its job in the awareness phase, and so on.

This can lead to vague "awareness campaigns" targeting reach, likes and clicks.

These metrics can serve a purpose, but mean very little in themselves.

Being aware of this does not guarantee future action.

This language also separates marketing from executives at a time when the two should work closely together.

Instead, start with the behaviors you want to shape and work back to determine the key moments that tend to precede this conversion.

If marketers can plot these potential sequences, they can create a customer journey that matches meaningful metrics.

For example, if a brand recognizes from their data that customers who display three or more products in their first session are more likely to make a purchase later, they can apply monetary value to content that controls these interactions.



They can then use the new attribution model to report just as meaningfully, to show how well the content performs compared to expectations, and to assign a value to each content.

In summary: assign value to content

The relationship between SEO and content has always been symbiotic, but this proximity has also been restrictive.

Just as there is no type of content, there is no more search engine marketing.

To take advantage of this, marketers need to look at the value their content offers to their audience from a broader perspective.

Instead of just looking at content performance with SEO metrics, it's important to start from a customer-centric perspective.

The customer can find the content via PPC, social media or PR channels, all of which are of value to the company.

The new multimedia SERPs also give SEOs the opportunity to experiment with video, image and audio content.

Many companies create blog posts as the primary form of content marketing, but it is now possible to get searches from a much wider range of sources.



Those who use customer insights to map and measure the buying journey can not only create better content, but also demonstrate its value to the company.

Here are some tips to help you get started with this new approach to content rating:

  • Start with the behaviors that you want to promote in your audience. Without knowing what to do, getting people to act is very difficult.
  • Analyze the customer journey to determine which steps tend to make a greater contribution to conversions.
  • Then evaluate the attribution models to determine which ones best reflect your customers ’journey. You can test data-driven mapping or customize your own rule-based model.
  • Create new content to capture these key phases on the way to buying. Look for the path of least resistance in the SERPs: this can mean, for example, using a paid search to promote content or using images to rank in universal search.
  • Report on key figures that are important for the company and are not classified for just one term. With this approach, you can show a causal relationship between interactions that lead to a conversion that can then be shared with the rest of the company.

The assignment in digital marketing is difficult, but also very important.

For content marketers, it’s important that a variety of metrics are not just SEO.

Value must be attributed to touches, brand value and language share (SOV), sales, customer service, product, positioning, PR and more.

And remember, if the search works, it means the content is working.

But its value just doesn't end there.

More resources:



Image credits

Screenshot from the author, June 2020
In-post images: Google


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