SEO Reinvented: Responding To Algorithm Shifts via @sejournal, @pageonepower

Recent developments at Google, including data leaks and the March 2024 core update, have changed how some practitioners think about SEO.

With engagement metrics taking center stage, SEO professionals must adapt their strategies to prioritize user interaction and satisfaction.

This article will examine how engagement metrics are reshaping key areas of SEO, with a focus on Conversion Rate Optimization (CRO), technical SEO, and strategic link building.

As we explore these topics, we’ll highlight how Page One Power’s expertise can help you navigate these changes.

Google’s Shift Towards Engagement Metrics

Recent leaks and updates suggest that Google is placing increased importance on user engagement signals.

Metrics such as click-through rates (CTR), time on site, bounce rates, and pages per session are likely more significant factors than previously thought.

The March 2024 core update further reinforced this trend, with sites providing exceptional user experiences seeing gains.

This shift calls for a more holistic approach to SEO.

Conversion Rate Optimization: The New Frontier Of SEO

With engagement metrics gaining prominence, CRO has become integral to SEO strategies. CRO directly impacts engagement signals that Google appears to be prioritizing.

How CRO Impacts Engagement Metrics

CRO Techniques to Boost Engagement

Focusing on these CRO techniques can improve your conversion rates and send strong engagement signals to Google. Page One Power’s team of experts can help you develop and implement a CRO strategy that aligns with your goals.

Technical SEO: The Foundation of User Engagement

Technical SEO is vital to user engagement by ensuring that your website provides a seamless, fast, and accessible experience.

How Technical SEO Impacts Engagement Metrics

Technical SEO Issues That Hurt Conversions

Addressing these technical issues improves your site’s performance in search engines and enhances the user experience, leading to better engagement metrics and potentially higher conversions.

Page One Power’s technical SEO audits can help identify and resolve these issues, ensuring your site provides the best possible foundation for user engagement.

The Evolving Link Graph & Strategic Link Building

Google’s approach to evaluating links is evolving. There’s a greater emphasis on the quality and relevance of links rather than sheer quantity.

This shift aligns with the focus on engagement, as high-quality, relevant links are more likely to drive engaged traffic to your site.

How the Link Graph is Changing

Strategic Link Building for Engagement

Page One Power specializes in strategic link building that aligns with these evolving best practices, helping you acquire high-quality, relevant links that boost your search rankings and user engagement.

Conclusion: Adapting to the Engagement-Focused Future

As Google continues to refine its algorithms, websites, and businesses must adapt their strategies accordingly.

By focusing on CRO, addressing technical SEO issues that impact user experience, and evolving your link-building approach, you can create a comprehensive SEO strategy that aligns with Google’s engagement-focused direction.

Remember, the goal is to rank well and provide genuine value to users.

Real-World Results: Case Studies

Consider these case studies:

Kreo, a SaaS company: After implementing keyword-focused content and strategic link-building strategies, Kreo saw impressive results:

Domotz, a network monitoring software provider: Following a 12-month campaign focused on targeted keyword content and high-quality link acquisition, Domotz experienced:

These case studies show how our three-pillar approach can improve search visibility and user engagement.

If you need assistance navigating these changes, consider partnering with Page One Power. Our team of experts can provide comprehensive solutions, from technical audits and CRO strategies to high-quality link building, helping you drive sustainable growth.

A lot has been said about the remarkable opportunities of Generative AI (GenAI), and some of us have also been extremely vocal about the risks associated with using this transformative technology.

The rise of GenAI presents significant challenges to the quality of information, public discourse, and the general open web. GenAI’s power to predict and personalize content can be easily misused to manipulate what we see and engage with.

How The SEO Industry Can Leverage And Improve Generative AI

Generative AI search engines are contributing to the overall noise, and rather than helping people find the truth and forge unbiased opinions, they tend (at least in their present implementation) to promote efficiency over accuracy, as highlighted by a recent study by Jigsaw, a unit inside Google.

Despite the hype surrounding SEO alligator parties and content goblins, our generation of marketers and SEO professionals has spent years working towards a more positive web environment.

We’ve shifted the marketing focus from manipulating audiences to empowering them with knowledge, ultimately aiding stakeholders in making informed decisions.

Creating an ontology for SEO is a community-led effort that aligns perfectly with our ongoing mission to shape, improve, and provide directions that truly advance human-GenAI interaction while preserving content creators and the Web as a shared resource for knowledge and prosperity.

A Brief Overview Of Traditional SEO Practices And Their Evolution

Traditional SEO practices in the early 2010s focused heavily on keyword optimization. This included tactics like keyword stuffing, link schemes, and creating low-quality content primarily intended for search engines.

Since then, SEO has shifted towards a more user-centric approach. The Hummingbird update (2013) marked Google’s transition towards semantic search, which aims to understand the context and intent behind search queries rather than just the keywords.

This evolution has led SEO pros to focus more on topic clusters and entities than individual keywords, improving content’s ability to answer multiple user queries.

Entities are distinct items like people, places, or things that search engines recognize and understand as individual concepts.

By building content that clearly defines and relates to these entities, organizations can enhance their visibility across various platforms, not just traditional web searches.

This approach ties into the broader concept of entity-based SEO, which ensures that the entity associated with a business is well-defined across the web.

From Static Content To Semantic Data

Fast-forward to today, static content that aims to rank well in search engines is constantly transformed and enriched by semantic data.

This involves structuring information so that it is understandable not only by humans but also by machines.

This transition is crucial for powering Knowledge Graphs and AI-generated responses like those offered by Google’s AIO or Bing Copilot, which provide users with direct answers and links to relevant websites.

Screenshot from Bing Copilot, August 2024

As we move forward, the importance of aligning content with semantic search and entity understanding is growing.

Businesses are encouraged to structure their content in ways that are easily understood and indexed by search engines, thus improving visibility across multiple digital surfaces, such as voice and visual searches.

The use of AI and automation in these processes is increasing, enabling more dynamic interactions with content and personalized user experiences.

Whether we like it or not, AI will help us compare options faster, run deep searches effortlessly, and make transactions without passing through a website.

Automation And AI In SEO

The future of SEO is promising. The SEO service market size is expected to grow from $75.13 billion in 2023 to $88.91 billion in 2024 – a staggering CAGR of 18.3% (according to The Business Research Company) – as it adapts to incorporate reliable AI and semantic technologies.

These innovations support the creation of more dynamic and responsive web environments that adeptly cater to user needs and behaviors.

However, the journey hasn’t been without challenges, especially in large enterprise settings. Implementing AI solutions that are both explainable and strategically aligned with organizational goals has been a complex task.

Building effective AI involves aggregating relevant data and transforming it into actionable knowledge.

This differentiates an organization from competitors using similar language models or development patterns, such as conversational agents or retrieval-augmented generation copilots and enhances its unique value proposition.

Introduction To SEOntology And Its Significance In The Current Digital Landscape

What Is An Ontology For SEO In Layman’s Terms?

Imagine an ontology as a giant instruction manual for describing specific concepts. In the world of SEO, we deal with a lot of jargon, right? Topicality, backlinks, E-E-A-T, structured data – it can get confusing!

An ontology for SEO is a giant agreement on what all those terms mean. It’s like a shared dictionary, but even better. This dictionary doesn’t just define each word. It also shows how they all connect and work together. So, “queries” might be linked to “search intent” and “web pages,” explaining how they all play a role in a successful SEO strategy.

Imagine it as untangling a big knot of SEO practices and terms and turning them into a clear, organized map – that’s the power of ontology!

While Schema.org is a fantastic example of a linked vocabulary, it focuses on defining specific attributes of a web page, like content type or author. It excels at helping search engines understand our content. But what about how we craft links between web pages?

What about the query a web page is most often searched for? These are crucial elements in our day-to-day work, and an ontology can be a shared framework for them as well. Think of it as a playground where everyone is welcome to contribute on GitHub similar to how the Schema.org vocabulary evolves.

The idea of an ontology for SEO is to augment Schema.org with an extension similar to what GS1 did by creating its vocabulary. So, is it a database? A collaboration framework or what? It is all of these things together. SEO ontology operates like a collaborative knowledge base.

It acts as a central hub where everyone can contribute their expertise to define key SEO concepts and how they interrelate. By establishing a shared understanding of these concepts, the SEO community plays a crucial role in shaping the future of human-centered AI experiences.

When working on sites with traffic, there is as much to lose as there is to gain from implementing SEO recommendations.

The downside risk of an SEO implementation gone wrong can be mitigated using machine learning models to pre-test search engine rank factors.

Pre-testing aside, split testing is the most reliable way to validate SEO theories before making the call to roll out the implementation sitewide or not.

We will go through the steps required on how you would use Python to test your SEO theories.

Choose Rank Positions

One of the challenges of testing SEO theories is the large sample sizes required to make the test conclusions statistically valid.

Split tests – popularized by Will Critchlow of SearchPilot – favor traffic-based metrics such as clicks, which is fine if your company is enterprise-level or has copious traffic.

If your site doesn’t have that envious luxury, then traffic as an outcome metric is likely to be a relatively rare event, which means your experiments will take too long to run and test.

Instead, consider rank positions. Quite often, for small- to mid-size companies looking to grow, their pages will often rank for target keywords that don’t yet rank high enough to get traffic.

Over the timeframe of your test, for each data point of time, for example day, week or month, there are likely to be multiple rank position data points for multiple keywords. In comparison to using a metric of traffic (which is likely to have much less data per page per date), which reduces the time period required to reach a minimum sample size if using rank position.

Thus, rank position is great for non-enterprise-sized clients looking to conduct SEO split tests who can attain insights much faster.

Google Search Console Is Your Friend

Deciding to use rank positions in Google makes using the data source a straightforward (and conveniently a low-cost) decision in Google Search Console (GSC), assuming it’s set up.

GSC is a good fit here because it has an API that allows you to extract thousands of data points over time and filter for URL strings.

While the data may not be the gospel truth, it will at least be consistent, which is good enough.

Filling In Missing Data

GSC only reports data for URLs that have pages, so you’ll need to create rows for dates and fill in the missing data.

The Python functions used would be a combination of merge() (think VLOOKUP function in Excel) used to add missing data rows per URL and filling the data you want to be inputed for those missing dates on those URLs.

For traffic metrics, that’ll be zero, whereas for rank positions, that’ll be either the median (if you’re going to assume the URL was ranking when no impressions were generated) or 100 (to assume it wasn’t ranking).

The code is given here.

Check The Distribution And Select Model

The distribution of any data represents its nature, in terms of where the most popular value (mode) for a given metric, say rank position (in our case the chosen metric) is for a given sample population.

The distribution will also tell us how close the rest of the data points are to the middle (mean or median), i.e., how spread out (or distributed) the rank positions are in the dataset.

This is critical as it will affect the choice of model when evaluating your SEO theory test.

Using Python, this can be done both visually and analytically; visually by executing this code:

Image from author, July 2024

The chart above shows that the distribution is positively skewed (think skewer pointing right), meaning most of the keywords rank in the higher-ranked positions (shown towards the left of the red median line). To run this code please make sure to install required libraries via command pip install pandas plotnine:

Now, we know which test statistic to use to discern whether the SEO theory is worth pursuing. In this case, there is a selection of models appropriate for this type of distribution.

Minimum Sample Size

The selected model can also be used to determine the minimum sample size required.

The required minimum sample size ensures that any observed differences between groups (if any) are real and not random luck.

That is, the difference as a result of your SEO experiment or hypothesis is statistically significant, and the probability of the test correctly reporting the difference is high (known as power).

This would be achieved by simulating a number of random distributions fitting the above pattern for both test and control and taking tests.

The code is given here.

When running the code, we see the following:

To break it down, the numbers represent the following using the example below:

(39.333,: proportion of simulation runs or experiments in which significance will be reached, i.e., consistency of reaching significance and robustness.

1.0) : statistical power, the probability the test correctly rejects the null hypothesis, i.e., the experiment is designed in such a way that a difference will be correctly detected at this sample size level.

60000: sample size

The above is interesting and potentially confusing to non-statisticians. On the one hand, it suggests that we’ll need 230,000 data points (made of rank data points during a time period) to have a 92% chance of observing SEO experiments that reach statistical significance. Yet, on the other hand with 10,000 data points, we’ll reach statistical significance – so, what should we do?

Experience has taught me that you can reach significance prematurely, so you’ll want to aim for a sample size that’s likely to hold at least 90% of the time – 220,000 data points are what we’ll need.

This is a really important point because having trained a few enterprise SEO teams, all of them complained of conducting conclusive tests that didn’t produce the desired results when rolling out the winning test changes.

Hence, the above process will avoid all that heartache, wasted time, resources and injured credibility from not knowing the minimum sample size and stopping tests too early.

Assign And Implement

With that in mind, we can now start assigning URLs between test and control to test our SEO theory.

In Python, we’d use the np.where() function (think advanced IF function in Excel), where we have several options to partition our subjects, either on string URL pattern, content type, keywords in title, or other depending on the SEO theory you’re looking to validate.

Use the Python code given here.

Strictly speaking, you would run this to collect data going forward as part of a new experiment. But you could test your theory retrospectively, assuming that there were no other changes that could interact with the hypothesis and change the validity of the test.

Something to keep in mind, as that’s a bit of an assumption!

Test

Once the data has been collected, or you’re confident you have the historical data, then you’re ready to run the test.

In our rank position case, we will likely use a model like the Mann-Whitney test due to its distributive properties.

However, if you’re using another metric, such as clicks, which is poisson-distributed, for example, then you’ll need another statistical model entirely.

The code to run the test is given here.

Once run, you can print the output of the test results:

The above is the output of an experiment I ran, which showed the impact of commercial landing pages with supporting blog guides internally linking to the former versus unsupported landing pages.

In this case, we showed that offer pages supported by content marketing enjoy a higher Google rank by 17 positions (22.58 – 5.87) on average. The difference is significant, too, at 98%!

However, we need more time to get more data – in this case, another 210,000 data points. As with the current sample size, we can only be sure that <10% of the time, the SEO theory is reproducible.

Split Testing Can Demonstrate Skills, Knowledge And Experience

In this article, we walked through the process of testing your SEO hypotheses, covering the thinking and data requirements to conduct a valid SEO test.

By now, you may come to appreciate there is much to unpack and consider when designing, running and evaluating SEO tests. My Data Science for SEO video course goes much deeper (with more code) on the science of SEO tests, including split A/A and split A/B.

As SEO professionals, we may take certain knowledge for granted, such as the impact content marketing has on SEO performance.

Clients, on the other hand, will often challenge our knowledge, so split test methods can be most handy in demonstrating your SEO skills, knowledge, and experience!

More resources: 

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Aligning your content with media coverage can drive high conversion rates because it influences search behavior and creates a funnel from media to search and through the website.

I’ve seen the best conversion rates and most qualified leads from SEO and content marketing when I coupled it with a digital PR strategy, creating these funnels.

Are SEO and content marketing part of your marketing mix? Do you see low conversion rates? Is your SEO program just failing to achieve business results or simple conversions?

Then, these three ways to optimize funnels can change the success trajectory of your entire program by creating a PR & SEO funnel.

Article overview:

Before diving into case studies, it’s important to understand why and how digital PR & SEO work together.

Why Create A PR & SEO Funnel

Marq’s Brand Consistency report found that 68% of businesses said brand message consistency contributed to revenue growth by 10% to 20% or more.

Image created by author, May 2024

Optimizing content for digital PR creates consistent messaging to nudge the audience from the media, through search engines, to specific web pages, and finally to conversion.

The media is at the top of the funnel, and you use media to drive demand and interest in an idea.

For example, create an industry trend report that identifies an opportunity or a major pain point for an audience. Media loves data, so use these reports to gain media coverage.

The coverage then drives searches for keywords your website owns. These can be the company or product brand name, a new concept, or just keywords you dominate for.

Keywords you own create a much higher click-through rate (CTR) than other keywords.

For brand keywords, a site can get up to a 60% CTR. An audience searching for these keywords tends to be more willing to submit an email to get a document or contact.

Image from Backlinko, May 2024

Instead of telling you how to optimize content for these funnels, let’s look at some examples.

These are three ways to optimize content that will change the way you do SEO.

1. Branded Reports For Data Shares

Create a funnel from your data by optimizing the report’s name and using it in a digital PR data campaign.

This approach creates a funnel from media through search engines to website conversion by owning your “state of industry” report or creating a study with its own brand name.

The audience will often read about the report in the media, on a blog, or hear about it referenced on a podcast, then search for it in Google or click the report link.

HubSpot and Conductor create two reports that garner some attention.

HubSpot’s State Of Marketing

HubSpot creates a “State of Marketing” report each year that gains a significant number of links and brand mentions.

The report covers a wide range of topics and trends in marketing that can impact marketers’ decisions, ideally affecting everything from how to use AI to which social channels produce the best ROI.

Screenshot by author, May 2024

According to Semrush, the report URL generated links from 7,400 referring domains. A quick Google search for the search operator “hubspot” and “state of marketing” –hubspot.com uncovered a significant number of brand mentions in the report.

Brand mentions of the report names can drive the audience to search for the report on Google and then download it from the website. This is a simple but effective funnel.

Screenshot by author, May 2024

Conductor’s State Of SEO

Conductor, an enterprise SEO software, has a “State of SEO” report that surveyed over 400 digital marketing, SEO, and content marketing experts to identify actionable insights.

Screenshot by author, May 2024

Conductor’s report received a brand mention and a direct link on entrepreneur.com, which can create a click or brand search.

Screenshot by author May 2024

Branded studies are one of my favorite funnels, as the reports can be named and focused on a specific persona or audience segment.

2. Guides In A New Category

This is by far my favorite approach to integrated digital PR & SEO because it can make competition irrelevant.

“Category design” is a strategy that involves creating and leading your own new market category or strategy. This approach involves creating foundational training content to help your audience see and capitalize on the value of your approach.

The foundational content should rank in search engines for the category name, then use digital PR to drive demand and interest in the category.

Drift is a wildly successful example of this strategy.

Conversational Marketing By Drift

I use the Drift example of category design whenever I’m involved in a planning session to identify or design a new category.

Drift is essentially a chatbot. However, the product is marketed as a “conversational marketing” platform. Functionally, it tailors a chat experience on a website based on the stage of the buyer’s journey and qualifies the leads.

Drift created the category “conversational marketing” and positioned the company as a platform for a conversational marketing platform, as you can see from its main navigation.

Screenshot by author, May 2024

Drift was founded in 2015 and, shortly after, started developing the “conversational marketing” category. A few years after the company’s launch, the audience started searching Google for the concept.

Screenshot from Google Trends, May 2024

Drift is still highly visible in Google for the term, ranking in the “What is Conversational Marketing” guide.

The launch of conversational marketing was so well executed that even HubSpot followed along and created a beginner’s guide, ranking just below Drift.

Screenshot from search for [conversational marketing], May 2024

If you’re creating a new category or trying to drive brand search for your company, create customer story funnels.

3. Homepage For Feature Articles

Feature articles can drive brand search spikes, and optimizing the homepage messaging can move the audience further down the funnel to take action on a website.

Sweetgreen, the fast-casual restaurant chain that serves salads, is a great example.

In early May, the New York Times and other publications published articles about Sweetgreen starting to offer steak. The angle was that Sweetgreen’s mission “committed to becoming carbon neutral by 2027” was at “steak” because the meat industry is a major producer of carbon.

Screenshot by author, May 2024

The articles about Sweetgreen’s addition of steak to the menu caused Google Trends to show a significant 36% spike in search for the brand name around May 7th.

Screenshot from Google Trends, May 2024

When a given audience searches for a brand name, most will end up on the homepage. Have you ever noticed a spike in traffic and higher conversions from the homepage? Very often, this is a brand search.

Sweetgreen updated its homepage banner to show steak as the first image. This creates a cohesive messaging funnel for the audience, so they will either convert when they enter the site or improve their recollection of the new steak option.

Screenshot by author,May 2024

Securing feature articles that align with the homepage messaging creates a powerful funnel as the audience searches the brand in search engines.

Start Optimizing

Digital PR with SEO can transform content marketing strategies, leading to enhanced visibility, higher traffic, and increased conversion rates.

A website can significantly influence audience behavior by aligning content with media coverage through branded reports, innovative category guides, and strategically placed feature articles.

The provided case studies underline the potential of these strategies to attract attention and drive meaningful engagement and growth.

More resources:

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Google’s recent leak highlighted engagement as part of the ranking system, alluding to the importance of influencing audience behavior to drive SEO-specific metrics, like ranking or organic visibility.

That said, I’ve been using simple variations of these measures for a while to evaluate the impact of integrated PR and SEO campaigns. I don’t think the idea of measuring search behavior is new, but the Google leaks shed some light on its importance.

For sustainable growth in organic visibility and rankings, SEO strategies need to pivot to include measures that reflect how strongly owned assets, marketing assets, and messaging influence an audience’s search behavior.

Google’s broader objective to rank content that is genuinely helpful to specific audience segments is an important context for considering this shift.

So, SEO pros should evaluate website performance based on engagement-driven metrics like asset NPS, idea adoption rate, and time to activation, which will be important for directly and indirectly maximizing organic search visibility.

Why Measure Influence

The recent Google leaks highlight the growing importance of audience engagement measures in ranking pages.

This highlights the importance of integrating SEO, content creation, and PR, where influencing audience behavior becomes a key measure.

I see it like this:

But to drive audience engagement, we have to think beyond simple SEO activities like link building, creating keyword-focused content, or technical SEO.

The future of search marketing is designing scenarios that influence an audience’s search behavior.

Ideal Search Behavior Scenario

The audience’s journey is more complex today than ever because they use many different sources to learn about their problems, the solutions, and the opportunities they create. However, this scenario simplifies how to think about your search strategy.

Scenario: You create an asset, you get PR coverage, and the audience searches the asset in Google (maybe they don’t find it based on keywords, then search your brand name). Then, they keep returning to your site for new assets or resources to solve their problems or create an opportunity (the original one as a resource or for your offering).

Simple Search Behavior Scenario Statement:

I need to create a content asset about [a problem or opportunity], to get coverage about [an asset of the asset] that the audience will prompt because [audience interest], which will drive my audience to search for [category or terms you own], and they will immediately or return to the site to take action because [solve a problem or create an opportunity].

You’ll have to modify this based on your specific website event goals, but the statement’s essence will guide you in the right direction.

This direction will allow you to focus on the much more significant but more difficult-to-impact measures below.

I have a foundation in product management and marketing, so I adopted these measures from product marketing concepts since they directly relate to audience actions.

Measure 1: Asset NPS

How likely is your audience to promote your content assets or ideas?

NPS score is used to gauge an audience’s loyalty and satisfaction using a survey question: “How likely are you to recommend our content to a friend or colleague?”

Respondents can provide a rating from 0-10.

High NPS indicates strong audience engagement, boosting engagement, and can indirectly influence organic visibility.

Typically, you’d have to survey an audience to gather the data. Use Google Forms, Survey Monkey, or any survey tool with a rating scale to collect questions.

Pro tip: Survey the audience on your site, the following you have on social media, or the email list you’re building as a result of the audience submitting contact info on the site or even through a newsletter.

Measure 2: Idea Adoption Rate

Does your audience adopt your ideas?

The adoption rate of an idea refers to the percentage of the audience segment that starts using the idea after you launch the asset.

This is a key measure to understand if your audience is accepting a particular idea, providing insights into engagement and market fit. This could directly influence engagement signals that can influence ranking.

Here’s How To Calculate

Metrics

You can collect this data in a lot of different ways, but shares alone are not a great metric since I don’t believe they reflect actual influence.

Find discussions or actions taken as a result of your ideas or content.

Pro tip: I see some creators concerned about people “stealing” their original idea. I don’t think this is a bad thing. This is a signal of adoption due to the idea of solving a significant problem or opportunity.

Measure 3: Time To Activation

How long does it take your audience to take action on your site?

Time to activation measures how long it takes for your audience to take action by searching a topic or taking action on your site after engaging with your messaging.

These can include brand searches, search keywords you own, document downloads, contacting for a quote, or requesting a demo.

This measure can show how well your content is being adopted or if the messaging aligns with your audience’s journey. Shorter activation times suggest strong alignment with audience needs and higher content efficacy.

How To Measure

Pro tip: Some marketers will say you shouldn’t measure your program because attribution modeling doesn’t work or SEO takes time. However, time to activation highlights the importance of evaluating the actions on the site that the campaign should drive. Design campaigns for time to activation of less than 3 months for each event, 6 months for large goals, and 12 months for larger business impacts like creating a new market category.

As you activate your audience, brand search will likely have an impact, as your audience will likely search Google for more information on your topic.

Measure 4: Brand Search Volume

Does the audience search for your brand in search engines?

Brand measures refer to the number of times users search for a specific term you branded or own in search engines.

You can measure this in Google Search Console, searching for your brand name or a term you own.

Pro tip: Brand keywords are reported in Google Analytics under the general search engine (e.g. Google) with non-brand keywords. Look for short-term spikes or sustainable trends in Google Search Console, segmenting it in any way possible (e.g. page, query, date, brand modified term) to find the impact. Design your strategy with the idea of being able to measure brand search impact.

Impact On Your Strategy

Integrating SEO and PR strategies to influence audience behavior and engagement is important for maximizing organic visibility and search rankings.

Google’s recent leaks emphasize the importance of audience engagement, highlighting the need to integrate content creation, SEO, and PR to drive meaningful interactions.

Measures such as asset NPS, idea adoption rate, and time to activation provide valuable insights into audience loyalty, idea adoption, and action times.

These seem to be important for driving engagement and influencing search engine rankings but critical for audience engagement.

These engagement-driven measures will help ensure you don’t have to keep chasing Google’s evolving algorithms and that content genuinely resonates with your audience segment.

Start designing integrated PR and SEO strategies.

More resources:

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