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Biggest Challenges Facing SEO In 2023

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When you’re preparing your strategy for next year, it’s vital to plan for potential upsets and challenges ahead.

This year, SEO practitioners overcame challenges posed by a lack of resources, issues with strategy, and the ability to scale processes.

Looking ahead to 2023 and beyond, our State of SEO report finds practitioners anticipate machine learning and AI, Google updates, and the deprecation of third-party cookies to lead the way as the greatest shifts in SEO.

In this article, we’ll summarize key data points from our report, highlight three major challenges in particular, and look at relevant SEO trends that can aid in your strategy development.

Lastly, we’ll discuss the implications advancements in machine learning and AI has on search marketing. Will this new search technology pose a challenge for you and your business? Continue reading to learn what our experts say.

All of the insights here are driven by our first-party survey data in the annual State Of SEO Report.

Summary Of Report Findings

When asked what were the biggest SEO challenges over the last 12 months, respondents stated:

  1. Lack of resources (14.9%).
  2. Strategy issues (12.3%).
  3. Scaling processes (11.9%).
  4. Pandemic-related issues (11.2%).
  5. Alignment with other departments (10.7%).

Budget cuts fell from the number one challenge SEO professionals faced in 2021 to number six this year.

However, the fact that lack of resources and scaling processes were top challenges in 2022 suggests that 2021’s budget cuts had a lasting impact.

Looking ahead to potential threats in 2023, we asked respondents to select up to three “biggest shifts” and industry changes in SEO. Here are their top responses:

  • Machine learning and AI (18.7%).
  • Google updates (18.0%).
  • Third-party cookie deprecation (13.9%).
  • Google zero-click pages (12.9%).
  • Competition for talent (11.5%).

Factors SEO professionals are watching as emergent factors are:

  • Machine learning and AI (11.3%).
  • Core Web Vitals (10.8%).
  • EAT & trusted sources (10.2%).
  • Mobile SEO (9.8%).
  • SERP features (8.3%).

SEO Pros Often Work With Limited Resources

Lack of resources came in as the top challenge faced by SEOs in 2022.

There’s little doubt that the industry is feeling the effects of budget cuts incurred in 2021, though another reason for the limited resources is that many SEOs aren’t working with large teams.

Over 40% of respondents report working with a team of 10 or fewer members, while roughly 5% said they work by themselves.

Adding new team members may prove difficult in the next year or two.

The State Of SEO Report goes into deeper detail about the challenges facing SEO professionals and what they’re worried about next year.

Recent And Continuing Growth May Prove Challenging

Several of the SEO shifts predicted for 2023 and beyond are potential impediments to growth.

Recent and continuing growth may prove challenging without the ability to scale as a team, and competition for talent is expected to be a major cause for concern over the next two years.

Deprecation of third-party cookies makes it difficult for SEO pros and marketers to sustain recent growth, as they’ll be expected to deliver the same or better results with fewer data.

Strategy Is A Concern For Many SEO Pros

SEOs listed strategy issues as one of their greatest challenges over the last 12 months.

Strategy issues may indicate that SEO professionals are struggling to prove their ROI (return on investment).

While over half of SEO practitioners (58.0%) we surveyed reported an increase in the ROI for their work, many struggled to prove ROI, and 29% of SEO professionals reported feeling ambivalent about their ROI.

In our chapter on Winning Strategies And Measuring SEO Success, we discuss how ROI problems are often the result of a disconnect between a brand’s target goals and the data being tracked.

SEO Pros Expect Machine Learning And AI To Have A Big Impact

Topping the list of biggest shifts over the next two years, as anticipated by SEO pros, is machine learning and AI.

Additionally, machine learning and AI were the top responses when SEO pros were asked to rank what they think will be the most important emergent factors in 2023.

To understand better why machine learning and AI are at the top of everyone’s minds, we turned to our in-house experts to get more context.

Shelley Walsh, the SEO content strategist at SEJ, doesn’t see AI and machine learning being able to replace human decision-making any time soon. Further, she doesn’t advise relying too heavily on AI-powered tools for creating content:

“As a disruptor, I can’t yet see AI being able to replace critical decisions and choices where there are several routes to take, and you have to make a choice based on expertise. The tool is only as good as the person driving it. At the moment, there is a flood of tools powered by GPT-3.

These are great for low-end volume content, such as product descriptions, but they widen the divide and elevate well-researched thought leadership quality content. As niches online become saturated by AI-spun content, the quality will be the only way to stand out. Ultimately, overuse will only have a detrimental effect.”

To see all of the first-party survey data and read more insights, download the State Of SEO Report.


Featured Image: Paulo Bobita/Search Engine Journal



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Google Updates Search Console Video Indexing Report

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Google Updates Search Console Video Indexing Report

Google’s updated Search Console Video indexing report now includes daily video impressions and a sitemap filter feature.

  • Google has updated the Search Console Video indexing report to provide more comprehensive insights into video performance in search results.
  • The updated report includes daily video impressions, which are grouped by page, and a new sitemap filter feature to focus on the most important video pages.
  • These updates are part of Google’s ongoing efforts to help website owners and content creators understand and improve the visibility of their videos in search results.



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Bing Revamps Crawl System To Enhance Efficiency

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Bing Revamps Crawl System To Enhance Efficiency

According to a recent study by Bing, most websites have XML sitemaps, with the “lastmod” tag being the most critical component of these sitemaps.

The “lastmod” tag indicates the last time the webpages linked by the sitemap were modified and is used by search engines to determine how often to crawl a site and which pages to index.

However, the study also revealed that a significant number of “lastmod” values in XML sitemaps were set incorrectly, with the most prevalent issue being identical dates on all sitemaps.

Upon consulting with web admins, Microsoft discovered that the dates were set to the date of sitemap generation rather than content modification.

To address this issue, Bing is revamping its crawl scheduling stack to better utilize the information provided by the “lastmod” tag in sitemaps.

This will improve crawl efficiency by reducing unnecessary crawling of unchanged content and prioritizing recently updated content.

The improvements have already begun on a limited scale and are expected to roll out by June fully.

Additionally, Microsoft has updated sitemap.org for improved clarity by adding the following line:

“Note that the date must be set to the date the linked page was last modified, not when the sitemap is generated.”

How To Use The Lastmod Tag Correctly

To correctly set the “lastmod” tag in a sitemap, you should include it in the <url> tag for each page in the sitemap.

The date should be in W3C Datetime format, with the most commonly used formats being YYYY-MM-DD or YYYY-MM-DDThh:mm:ssTZD.

The date should reflect the last time the page was modified and should be updated regularly to ensure that search engines understand the relevance and frequency of updates.

Here’s an example code snippet:

<?xml version=”1.0″ encoding=”UTF-8″?>

<urlset xmlns=”http://www.sitemaps.org/schemas/sitemap/0.9″>

   <url>

      <loc>http://www.example.com/</loc>

      <lastmod>2023-01-23</lastmod>      

   </url>

Google’s Advice: Use Lastmod Tag After Significant Changes Only

Google’s crawlers also utilize the “lastmod” tag, and the suggestions on using it by both major search engines are similar.

Google Search Advocate John Mueller recently discussed the lastmod tag in the January edition of Google’s office-hours Q&A sessions.

It’s worth noting that Google recommends only using the “lastmod” tag for substantial modifications, which was not mentioned in Microsoft’s blog post.

Changing the date in the lastmod tag after minor edits can be viewed as an attempt to manipulate search snippets.

In Summary

Microsoft’s recent study and efforts to improve the utilization of the “lastmod” tag in sitemaps will result in more efficient and effective webpage crawling.

Publishers are encouraged to regularly update their sitemaps and lastmod tags to ensure that their pages are correctly indexed and easily accessible by search engines.


Featured Image: mundissima/Shutterstock

Source: Microsoft



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Everything You Need To Know

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Everything You Need To Know

Now more than ever, marketing and sales leaders are taking a critical look at where to allocate their resources and how to staff their teams.

Attribution modeling is one of the best tools for providing clear guidance on what’s working, and what isn’t.

What Is Marketing Attribution?

Marketing attribution is the approach to understanding how various marketing and sales touchpoints influence the prospects’ move from visitor, to lead, to customer.

By implementing attribution in your organization, you’ll have a better idea of:

  • Which channels are most influential during different phases of the sales cycle.
  • Which content formats are more or less impactful in your marketing or sales enablement efforts.
  • Which campaigns drove the most revenue and return on investment (ROI).
  • The most common sequence of online or offline events that prospects interact with before becoming a customer.

Why Is Attribution Important In Marketing?

Analyzing attribution data provides you with an understanding of which marketing, sales, and customer success efforts are contributing most effectively and efficiently toward revenue generation.

Attribution modeling helps you identify opportunities for growth and improvement, while also informing budget allocation decisions.

With accurate attribution models, marketers are able to make more informed decisions about their campaigns, which has allowed them to increase ROI and reduce wasted budgets on ineffective strategies.

What Are The Challenges Of Marketing Attribution?

Developing a perfect attribution model that guides all of your decisions is a pipedream for most marketers.

Here are five challenges that result in inconclusive data models or total project abandonment:

Cross-Channel Management

This is a common challenge for enterprise marketers who have web assets across multiple websites, channels, and teams.

Without proper analytics tagging and system settings configuration, your web activities may not be tracked accurately as a visitor goes from one campaign micro-site to the main domain.

Or, the prospect may not be tracked as they go from your website to get directions to then go to your physical storefront to transact.

Making Decisions Based On Small Sample Sizes

For smaller trafficked websites, marketers using attribution data may not have statistically significant data sets to draw accurate correlations for future campaigns.

This results in faulty assumptions and the inability to repeat prior success.

Lack Of Tracking Compliance

If your attribution models rely on offline activities, then you may require manual imports of data or proper logging of sales activities.

From my experience in overseeing hundreds of CRM implementations, there is always some level of non-compliance in logging activities (like calls, meetings, or emails). This leads to skewed attribution models.

Mo‘ models, mo’ problems: Each analytics platform has a set of five or more attribution models you can use to optimize your campaigns around.

Without a clear understanding of the pros and cons of each model, the person building the attribution reporting may not be structuring or configuring them to align with your organizational goals.

Data Privacy

Since GDPR, CCPA, and other privacy laws were enacted, analytics data continues to get murkier each year.

For organizations that rely on web visitors to opt-in to tracking, attribution modeling suffers due to the inability to pull in tracking for every touchpoint.

How Do You Measure Marketing Attribution?

Measuring attribution is all about giving credit where it is due. There are dozens of attribution tools out there to assign credit to the digital or offline touchpoint.

Attribution measurement starts with choosing the data model that aligns with your business goals.

Certain attribution models favor interactions earlier on in the customer journey whereas others give the most credit towards interactions closer to a transaction.

Here is a scenario of how to measure marketing attribution in a first-touch attribution model (we’ll get to the different models next):

A prospect comes to the website through a paid search ad and reads the blog.

Two days later, she comes back to the site and views a couple of product pages.

Three days later, she comes back through an organic listing from Google and then converts on the site by signing up for a discount coupon.

With a first-touch attribution model, the paid search ad will get 100% of the credit for that conversion.

As you can see, choosing the “right” model can be a contentious issue, as each model gives a percentage of credit to a specific interaction or placement along the path toward becoming a customer.

If your business relies on paid search, SEO, offline, and other channels, then likely one of the individuals working on one of those channels is going to look like the superhero, whereas the other marketers will look like they aren’t pulling their weight.

Ideally, when you are choosing an attribution tool, you’ll be able to build reports that allow you to compare various attribution models, so you have a better understanding of which channels and interactions are most influential during certain time periods leading up to conversion or purchase.

What Are Different Marketing Attribution Models?

Marketers can use various marketing attribution models to examine the effectiveness of their campaigns.

Each attribution tool has will have a handful of models you can optimize campaigns and build reports around. Here is a description of each model:

First-Click Attribution

This model gives credit to the first channel that the customer interacted with.

This model is popular to use when optimizing for brand awareness and top-of-funnel conversions/engagement.

Last-Click Attribution

This model gives all of the credit to the last channel that the customer interacts with.

This model is useful when looking to understand which channels/interactions were most influential immediately before converting/purchasing.

Last-click attribution is the default attribution model for Google Analytics.

Multi-Touch/Channel Attribution

This model gives credit to all of the channels or touchpoints that the customer interacted with throughout their journey.

This model is used when you are looking to give weight evenly or to specific interactions.

There are variations of the multi-touch model including time-decay, linear, U-shaped, W-shaped, and J-shaped.

Customized

This model allows you to manually set the weight for individual channels or placements within the customer journey.

This model is best for organizations that have experience in using attribution modeling, and have clear goals for what touchpoints are most impactful in the buyers’ journey.

Marketing Attribution Tools

There are several different tools available to help marketers measure and analyze marketing attribution. Some attribution tools are features within marketing automation platforms or CRM systems like Active Campaign or HubSpot.

Others are stand-alone attribution tools that rely on API or integrations to pull in and analyze data, like Triple Whale or Dreamdata.

As you are evaluating tools, consider how much offline or sales data needs to be included within your attribution models.

For systems like HubSpot, you can include sales activities (like phone calls and 1:1 sales emails) and offline list import data (from tradeshows).

Other tools, like Google Analytics, are not natively built to pull in that kind of data and would require advanced development work to include these activities as part of your model.

(Full disclosure: I work with HubSpot’s highest-rated partner agency, SmartBug Media.)

Additionally, if you need to be able to see the very specific touchpoints (like a specific email sent or an ad clicked), then you need a full-funnel attribution system that shows this level of granularity.

Attribution modeling is a powerful tool that marketers can use to measure the success of their campaigns, optimize online/offline channels, and improve customer interactions.

It is important, though, to understand attribution’s limitations, the pros and cons of each model, and the challenges with extracting conclusive data before investing large budgets towards attribution technology.

More resources: 


Featured Image: Yuriy K/Shutterstock



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