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How Stacker.com Earned 1M+ Organic Monthly Visits Through Content Syndication [Case Study]

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How Stacker.com Earned 1M+ Organic Monthly Visits Through Content Syndication [Case Study]

The author’s views are entirely his or her own (excluding the unlikely event of hypnosis) and may not always reflect the views of Moz.

Note: Amanda Milligan collaborated with Stacker’s SEO specialist, Sam Kaye, to create this case study.

When a marketer is asked about the value of content syndication, they’ll typically list two main benefits:

  1. Increased brand awareness, as you’re reaching a wider audience.

  2. Improved engagement, as people can share and comment across multiple versions of the story.

But one benefit of content syndication that marketers frequently overlook is the potential to improve a site’s SEO performance.

While paid syndication (like press release distribution) can’t carry SEO value, developing strong content that’s appealing to publishers and their readers can generate massive amounts of link authority back to a publishing domain, and drive significant organic growth.

But it’s difficult to test and implement a comprehensive syndication strategy, so there aren’t many resources about its SEO impact.

In this case study, we:

  • Outline the processes used by Stacker to syndicate content.

  • Look into organic results on Stacker.com as a result of content syndication efforts.

  • Discuss how content syndication can be used as part of a long-term organic growth strategy.

The content creation and distribution methods used for Stacker.com are the same as those used for Stacker Studio brand partners, making Stacker.com’s organic success an excellent case study for the long-term effectiveness for content syndication strategies.

The evidence of syndication’s impact

Before digging into how syndication works for SEO, let’s begin by proving that content syndication works.

Stacker.com has no proactive digital PR or backlinking strategies. Our growth strategy has been utilizing content syndication as a model to reach new audiences and drive valuable domain authority. The result has been Stacker accumulating 20K “dofollowed” referring domains and over one million unique backlinks over the last four years.

Organic traffic growth

Organic traffic: Google Search Console

Over a period of 16 months, Stacker.com saw a significant acceleration in organic growth, increasing by approximately 500% — from fewer than 10K organic entries per day to more than 50K entries per day. (Our site used to be TheStacker.com, and you can see the exponential growth on that domain as well before migrating to Stacker.com.)

Backlinks

Backlinks: Google Search Console

Backlinks that appear on pages including rel=canonical tags are processed and valued by search engines, as evidenced by the 8M+ links created by this method & identified in Search Console. The majority of these links are in-text dofollows from syndicated article pickups with rel=canonical tags. This is an excellent indicator that Google is crawling and valuing these links.

GSC top external links overview for Stacker.com

Backlinks: Moz Pro (domain-wide)

Backlinks created via content syndication are also being picked up by Moz Pro and other third-party reporting tools.

Moz Pro reports a steady growth in the number of referring domains that correlates well with GSC link reporting metrics:

1657238511 409 How Stackercom Earned 1M Organic Monthly Visits Through Content Syndication

Moz: individual links

In addition to tracking account-wide backlinking growth, Moz also picks up individual instances of links created via content syndication, such as these syndicated SFGate pickups.

1657238511 520 How Stackercom Earned 1M Organic Monthly Visits Through Content Syndication

Domain Authority: Moz Pro

This accumulation of link authority over time has allowed Stacker to increase our Moz Pro Domain Authority score from 56 to 59 over the past year:

1657238511 967 How Stackercom Earned 1M Organic Monthly Visits Through Content Syndication

Organic performance: Summary

In 2021 alone, Stacker.com saw a 500% increase in referring domains, a 380% increase in organic traffic, and an improvement in domain authority from 56 to 59 due in large part to our content syndication efforts.

These long-term trends of organic growth, paired with the fact that syndicated links are being picked up by both Google Search Console and Moz Pro, are a clear indication that content syndication is an effective way to drive organic traffic.

How content syndication improves SEO authority

Stacker’s syndication approach provides link authority in two ways: in-text dofollow backlinks and rel=canonical tags.

An in-text backlink acts as a signal of source attribution, telling search engines that a particular piece of data or content has been taken from another source. A canonical tag does the same thing, except that it attributes the entire article, not just a piece of it, back to the original publisher. Both are signals of source attribution, and both indicate that a publisher trusts your content enough to feature and share the article on their website.

When a piece of Stacker content is syndicated (re-published in its original form on another publisher’s site), the syndicated version includes a rel=canonical tag back to the publishers’ hosted version, as well as an in-text dofollow backlink in the content intro:

Example rel=canonical tag from a syndicated piece
Example rel=canonical tag from a syndicated piece
Example of an in-text, dofollow backlink attributing authorship in a syndicated piece
Example of an in-text, dofollow backlink attributing authorship in a syndicated piece

When a Stacker article is rewritten instead of syndicated, (e.g., a publisher creates a locally-focused variant using Stacker source data), we request a backlink citing us as the original provider of the study.

Owned syndication vs. earned syndication

In the same way the industry talks about owned and earned media, you can think of two types of syndication as “owned syndication” and “earned syndication.”

Owned syndication involves reposting an article on multiple platforms by you. An example of this would be publishing an article on your blog and then republishing it on Medium, LinkedIn, and other accounts you run. While this might increase the number of people that see your article, the likelihood of driving organic traffic from these strategies reliably or at scale is virtually nil.

Earned syndication involves the approval from another publisher that your content is valuable to their audience, so this type of syndication is harder to achieve. However, in addition to reaching a wider audience than with owned syndication, you get the authority signal of having your content hosted on another publisher’s domain. (Someone decided your content was worth republishing in full, and what’s a greater sign of trust than that?)

Why isn’t everyone doing this?

Because it’s not easy. For the first few years of our existence, Stacker did nothing but build publisher relationships and master the art of newsworthy content. Getting content pickups at scale requires building trust with large news publishers, as well as a large volume of content news publishers find uniquely interesting and relevant. Content syndication is built upon a foundation of content quality, publisher trust, and the technical capability to share content at scale, and these three components can take years to develop.

Stacker journalists are committed to understanding the coverage needs of local news organizations and investing in stories that can drive meaningful value for their audiences. After five years of working with publishing partners, we’ve studied the data on pickups and audience reach to uncover insights into what stories can be most useful.

We landed on some key earned syndication tenets:

Contextualization is key

Any type of publisher you come across will have their core editorial calendar established with key topics they know their audience cares about. They’re not looking for outsiders to contribute to the heart of their publication, so don’t approach it that way. Instead, explore topics they typically cover and perhaps even particular stories they’ve run and ask yourself: What other perspective can I add to this story to contextualize it? Perhaps a historical angle or other comparison

Data always helps

Some publishers don’t have access to data analysts, or if they do, they’re working on a ton of other projects and it’s hard to scale data-focused content. If you’re able to provide stories based on data that’s been distilled and presented with clear insights, many publishers would appreciate that. Additionally, just knowing your content is backed by data rather than opinion makes it easier to vet (and trust).

Help publishers reach their goals

Our direct line of communication with multiple publishers, both local and national, has led to fascinating conversations around their goals. To sum it up, every publisher has unique focus areas when it comes to audience acquisition and engagement. Some are focused on converting users to subscription while others are focused on pageviews or time on site. Explore their site, see how they monetize, and consider how your content can help them meet these goals.

Let’s look at an example story Stacker created.

Feature image for Stacker MLB piece.

This piece uses Major League Baseball data to determine the most successful postseason teams. With data being the basis for the ranking, publishers don’t have to worry about the validity of the order, which is a major advantage in vetting.

This story offers original analysis in a way that can complement the local coverage of news organizations. While a sports beat writer might focus on the area team’s history, current team performance, or other local and newsy aspects of the story—this story offers contextual data analysis that can work for a variety of news organizations to augment their boots-on-the-ground reporting.

All in all, the article earned more than 300 publisher pickups and more than 100,000 story impressions. That’s an incredible amount of payoff for one piece of content, and earned syndication is the vehicle that made it possible.

The syndication takeaway

Like so many other SEO tactics, not all syndication is created equal. Potential clients have often asked me how Stacker is different from services like press release distribution platforms, with which they didn’t see SEO results.

Well, when you have sponsored or nofollow links, it’s never going to be the same as earned syndication. Getting white hat content pickups with consistency is difficult — it requires both top-tier content and the attention of journalists.

So my advice? Consider whether there are high-authority publications in your niche. Study what they publish and ask yourself:

  • Do you already publish content that they’d love?

  • Can you make some tweaks to already existing content to better fit their editorial style?

  • Can you create original research/reports that would interest their audience?

  • Would getting brand awareness with their audience help us improve your brand reach?

If the answer is yes to at least two of these questions, consider content syndication as a strategy.

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YouTube Ad Specs, Sizes, and Examples [2024 Update]

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YouTube Ad Specs, Sizes, and Examples

Introduction

With billions of users each month, YouTube is the world’s second largest search engine and top website for video content. This makes it a great place for advertising. To succeed, advertisers need to follow the correct YouTube ad specifications. These rules help your ad reach more viewers, increasing the chance of gaining new customers and boosting brand awareness.

Types of YouTube Ads

Video Ads

  • Description: These play before, during, or after a YouTube video on computers or mobile devices.
  • Types:
    • In-stream ads: Can be skippable or non-skippable.
    • Bumper ads: Non-skippable, short ads that play before, during, or after a video.

Display Ads

  • Description: These appear in different spots on YouTube and usually use text or static images.
  • Note: YouTube does not support display image ads directly on its app, but these can be targeted to YouTube.com through Google Display Network (GDN).

Companion Banners

  • Description: Appears to the right of the YouTube player on desktop.
  • Requirement: Must be purchased alongside In-stream ads, Bumper ads, or In-feed ads.

In-feed Ads

  • Description: Resemble videos with images, headlines, and text. They link to a public or unlisted YouTube video.

Outstream Ads

  • Description: Mobile-only video ads that play outside of YouTube, on websites and apps within the Google video partner network.

Masthead Ads

  • Description: Premium, high-visibility banner ads displayed at the top of the YouTube homepage for both desktop and mobile users.

YouTube Ad Specs by Type

Skippable In-stream Video Ads

  • Placement: Before, during, or after a YouTube video.
  • Resolution:
    • Horizontal: 1920 x 1080px
    • Vertical: 1080 x 1920px
    • Square: 1080 x 1080px
  • Aspect Ratio:
    • Horizontal: 16:9
    • Vertical: 9:16
    • Square: 1:1
  • Length:
    • Awareness: 15-20 seconds
    • Consideration: 2-3 minutes
    • Action: 15-20 seconds

Non-skippable In-stream Video Ads

  • Description: Must be watched completely before the main video.
  • Length: 15 seconds (or 20 seconds in certain markets).
  • Resolution:
    • Horizontal: 1920 x 1080px
    • Vertical: 1080 x 1920px
    • Square: 1080 x 1080px
  • Aspect Ratio:
    • Horizontal: 16:9
    • Vertical: 9:16
    • Square: 1:1

Bumper Ads

  • Length: Maximum 6 seconds.
  • File Format: MP4, Quicktime, AVI, ASF, Windows Media, or MPEG.
  • Resolution:
    • Horizontal: 640 x 360px
    • Vertical: 480 x 360px

In-feed Ads

  • Description: Show alongside YouTube content, like search results or the Home feed.
  • Resolution:
    • Horizontal: 1920 x 1080px
    • Vertical: 1080 x 1920px
    • Square: 1080 x 1080px
  • Aspect Ratio:
    • Horizontal: 16:9
    • Square: 1:1
  • Length:
    • Awareness: 15-20 seconds
    • Consideration: 2-3 minutes
  • Headline/Description:
    • Headline: Up to 2 lines, 40 characters per line
    • Description: Up to 2 lines, 35 characters per line

Display Ads

  • Description: Static images or animated media that appear on YouTube next to video suggestions, in search results, or on the homepage.
  • Image Size: 300×60 pixels.
  • File Type: GIF, JPG, PNG.
  • File Size: Max 150KB.
  • Max Animation Length: 30 seconds.

Outstream Ads

  • Description: Mobile-only video ads that appear on websites and apps within the Google video partner network, not on YouTube itself.
  • Logo Specs:
    • Square: 1:1 (200 x 200px).
    • File Type: JPG, GIF, PNG.
    • Max Size: 200KB.

Masthead Ads

  • Description: High-visibility ads at the top of the YouTube homepage.
  • Resolution: 1920 x 1080 or higher.
  • File Type: JPG or PNG (without transparency).

Conclusion

YouTube offers a variety of ad formats to reach audiences effectively in 2024. Whether you want to build brand awareness, drive conversions, or target specific demographics, YouTube provides a dynamic platform for your advertising needs. Always follow Google’s advertising policies and the technical ad specs to ensure your ads perform their best. Ready to start using YouTube ads? Contact us today to get started!

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Why We Are Always ‘Clicking to Buy’, According to Psychologists

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Why We Are Always 'Clicking to Buy', According to Psychologists

Amazon pillows.

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A deeper dive into data, personalization and Copilots

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A deeper dive into data, personalization and Copilots

Salesforce launched a collection of new, generative AI-related products at Connections in Chicago this week. They included new Einstein Copilots for marketers and merchants and Einstein Personalization.

To better understand, not only the potential impact of the new products, but the evolving Salesforce architecture, we sat down with Bobby Jania, CMO, Marketing Cloud.

Dig deeper: Salesforce piles on the Einstein Copilots

Salesforce’s evolving architecture

It’s hard to deny that Salesforce likes coming up with new names for platforms and products (what happened to Customer 360?) and this can sometimes make the observer wonder if something is brand new, or old but with a brand new name. In particular, what exactly is Einstein 1 and how is it related to Salesforce Data Cloud?

“Data Cloud is built on the Einstein 1 platform,” Jania explained. “The Einstein 1 platform is our entire Salesforce platform and that includes products like Sales Cloud, Service Cloud — that it includes the original idea of Salesforce not just being in the cloud, but being multi-tenancy.”

Data Cloud — not an acquisition, of course — was built natively on that platform. It was the first product built on Hyperforce, Salesforce’s new cloud infrastructure architecture. “Since Data Cloud was on what we now call the Einstein 1 platform from Day One, it has always natively connected to, and been able to read anything in Sales Cloud, Service Cloud [and so on]. On top of that, we can now bring in, not only structured but unstructured data.”

That’s a significant progression from the position, several years ago, when Salesforce had stitched together a platform around various acquisitions (ExactTarget, for example) that didn’t necessarily talk to each other.

“At times, what we would do is have a kind of behind-the-scenes flow where data from one product could be moved into another product,” said Jania, “but in many of those cases the data would then be in both, whereas now the data is in Data Cloud. Tableau will run natively off Data Cloud; Commerce Cloud, Service Cloud, Marketing Cloud — they’re all going to the same operational customer profile.” They’re not copying the data from Data Cloud, Jania confirmed.

Another thing to know is tit’s possible for Salesforce customers to import their own datasets into Data Cloud. “We wanted to create a federated data model,” said Jania. “If you’re using Snowflake, for example, we more or less virtually sit on your data lake. The value we add is that we will look at all your data and help you form these operational customer profiles.”

Let’s learn more about Einstein Copilot

“Copilot means that I have an assistant with me in the tool where I need to be working that contextually knows what I am trying to do and helps me at every step of the process,” Jania said.

For marketers, this might begin with a campaign brief developed with Copilot’s assistance, the identification of an audience based on the brief, and then the development of email or other content. “What’s really cool is the idea of Einstein Studio where our customers will create actions [for Copilot] that we hadn’t even thought about.”

Here’s a key insight (back to nomenclature). We reported on Copilot for markets, Copilot for merchants, Copilot for shoppers. It turns out, however, that there is just one Copilot, Einstein Copilot, and these are use cases. “There’s just one Copilot, we just add these for a little clarity; we’re going to talk about marketing use cases, about shoppers’ use cases. These are actions for the marketing use cases we built out of the box; you can build your own.”

It’s surely going to take a little time for marketers to learn to work easily with Copilot. “There’s always time for adoption,” Jania agreed. “What is directly connected with this is, this is my ninth Connections and this one has the most hands-on training that I’ve seen since 2014 — and a lot of that is getting people using Data Cloud, using these tools rather than just being given a demo.”

What’s new about Einstein Personalization

Salesforce Einstein has been around since 2016 and many of the use cases seem to have involved personalization in various forms. What’s new?

“Einstein Personalization is a real-time decision engine and it’s going to choose next-best-action, next-best-offer. What is new is that it’s a service now that runs natively on top of Data Cloud.” A lot of real-time decision engines need their own set of data that might actually be a subset of data. “Einstein Personalization is going to look holistically at a customer and recommend a next-best-action that could be natively surfaced in Service Cloud, Sales Cloud or Marketing Cloud.”

Finally, trust

One feature of the presentations at Connections was the reassurance that, although public LLMs like ChatGPT could be selected for application to customer data, none of that data would be retained by the LLMs. Is this just a matter of written agreements? No, not just that, said Jania.

“In the Einstein Trust Layer, all of the data, when it connects to an LLM, runs through our gateway. If there was a prompt that had personally identifiable information — a credit card number, an email address — at a mimum, all that is stripped out. The LLMs do not store the output; we store the output for auditing back in Salesforce. Any output that comes back through our gateway is logged in our system; it runs through a toxicity model; and only at the end do we put PII data back into the answer. There are real pieces beyond a handshake that this data is safe.”

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