Advanced Linkbuilding: How to Find the Absolute Best Publishers and Writers to Pitch

Posted by KristinTynski

In my
last post
, I explained how using network visualization tools
can help you massively improve your content marketing PR/Outreach
strategy —understanding which news outlets have the largest
syndication networks empowers your outreach team to prioritize
high-syndication publications over lower syndication publications.
The result? The content you are pitching enjoys significantly more
widespread link pickups.

Today, I’m going to take you a little deeper — we’ll be
looking at a few techniques for forming an even better
understanding of the publisher syndication networks in your
particular niche. I’ve broken this technique into two parts:

  • Technique One — Leveraging Buzzsumo influencer data and twitter
    scraping to find the most influential journalists writing about any
    topic
  • Technique Two — Leveraging the Gdelt Dataset to reveal deep
    story syndication networks between publishers using in-context
    links.

Why do this at all?

If you are interested in generating high-value links at scale,
these techniques provide an undeniable competitive advantage
— they help you to deeply understand how writers and news
publications connect and syndicate to each other.

In our opinion at Fractl,
data-driven content stories that have strong news hooks, finding
writers and publications who would find the content compelling, and
pitching them effectively is the single highest ROI SEO activity
possible. Done correctly, it is entirely possible to generate
dozens, sometimes even hundreds or thousands, of high-authority
links with one or a handful of content campaigns.

Let’s dive in.

Using Buzzsumo to understand journalist influencer networks on any
topic

First, you want to figure out who your topc influencers are your
a topic. A very handy feature of Buzzsumo is its “influencers”
tool. You can locate it on the influences tab, then follow these
steps:

  • Select only “Journalists.” This will limit the result to
    only the Twitter accounts of those known to be reporters and
    journalists of major publications. Bloggers and lower authority
    publishers will be excluded.
  • Search using a topical keyword. If it is straightforward, one
    or two searches should be fine. If it is more complex, create a few
    related queries, and collate the twitter accounts that appear in
    all of them. Alternatively, use the Boolean “and/or” in your search
    to narrow your result. It is critical to be sure your search
    results are returning journalists that as closely match your target
    criteria as possible.
  • Ideally, you want at least 100 results. More is generally
    better, so long as you are sure the results represent your target
    criteria well.
  • Once you are happy with your search result, click export to
    grab a CSV.

The next step is to grab all of the people each of these known
journalist influencers follows — the goal is to understand which
of these 100 or so influencers impacts the other 100 the most.
Additionally, we want to find people outside of this group that
many of these 100 follow in common.

To do so, we leveraged Twint, a handy Twitter
scraper available on Github to pull all of the people each of these
journalist influencers follow. Using our scraped data, we built
an edge list, which allowed us to visualize the result in 
Gephi.

Here is an interactive version for you to explore, and here is a
screenshot of what it looks like:

This graph shows us which nodes (influencers) have the most
In-Degree links. In other words: it tells us who, of our media
influencers, is most followed. 

These are the top 10 nodes:

  • @maiasz
  • Radley Balko (@radleybalko) Opinion journalist, Washington
    Post
  • @johannhari101
  • @davidkroll
  • @narcomania
  • @milbank
  • @samquinones7
  • @felicejfreyer
  • @jeannewhalen
  • @ericbolling 

Who is the most influential?

Using the “Betweenness Centrality” score given by Gephi, we
get a rough understanding of which nodes (influencers) in the
network act as hubs of information transfer. Those with the highest
“Betweenness Centrality” can be thought of as the “connectors”
of the network. These are the top 10 influencers:

  • Maia Szalavitz (@maiasz) Neuroscience Journalist, VICE and
    TIME
  • Radley Balko (@radleybalko) Opinion journalist, Washington
    Post
  • Johann Hari (@johannhari101) New York Times best-selling
    author
  • David Kroll (@davidkroll) Freelance healthcare writer, Forbes
    Heath
  • Max Daly (@Narcomania) Global Drugs Editor, VICE
  • Dana Milbank (@milbank)Columnist, Washington Post
  • Sam Quinones (@samquinones7), Author
  • Felice Freyer (@felicejfreyer), Boston Globe Reporter, Mental
    health and Addiction
  • Jeanne Whalen (@jeannewhalen) Business Reporter, Washington
    Post
  • Eric Bolling (@ericbolling) New York Times best-selling
    author

@maiasz, @davidkroll, and @johannhari101 are standouts. There’s
considerable overlap between the winners in “In-Degree” and
“Betweenness Centrality” but they are still quite different. 

What else can we learn?

The middle of the visualization holds many of the largest sized
nodes. The nodes in this view are sized by “In-Degree.” The large,
centrally located nodes are disproportionately followed by other
members of the graph and enjoy popularity across the board (from
many of the other influential nodes). These are journalists
commonly followed by everyone else. Sifting through these centrally
located nodes will surface many journalists who behave as
influencers of the group initially pulled from BuzzSumo.

So, if you had a campaign about a niche topic, you could
consider pitching to an influencer surfaced from this data
—according to our the visualization, an article shared in their
network would have the most reach and potential ROI

Using Gdelt to find the most influential websites on a topic with
in-context link analysis

The first example was a great way to find the best journalists
in a niche to pitch to, but top journalists are often the most
pitched to overall. Often times, it can be easier to get a pickup
from less known writers at major publications. For this reason,
understanding which major publishers are most influential, and
enjoy the widest syndication on a specific theme, topic, or beat,
can be majorly helpful.

By using Gdelt’s massive and fully comprehensive database of
digital news stories, along with Google BigQuery and Gephi, it is
possible to dig even deeper to yield important strategic
information that will help you prioritize your content
pitching.

We pulled all of the articles in Gdelt’s database that are
known to be about a specific theme within a given timeframe. In
this case (as with the previous example) we looked at “behaviour
health.” For each article we found in Gdelt’s database that
matches our criteria, we also grabbed links found only within the
context of the article.

Here is how it is done:

  • Connect to Gdelt on Google BigQuery — you can find a
    tutorial here
    .
  • Pull data from Gdelt. You can use this command: SELECT
    DocumentIdentifier,V2Themes,Extras,SourceCommonName,DATE FROM
    [gdelt-bq:gdeltv2.gkg] where (V2Themes like ‘%Your Theme%’).
  • Select any theme you find,
    here
    — just replace the part between the percentages.
  • To extract the links found in each article and build an edge
    file. This can be done with a relatively simple python script to
    pull out all of the <PAGE_LINKS> from the results of the
    query, clean the links to only show their root domain (not the full
    URL) and put them into an edge file format.

Note: The edge file is made up of Source–>Target pairs. The
Source is the article and the Target are the links found within the
article. The edge list will look like this:

  • Article 1, First link found in the article.
  • Article 1, Second link found in the article.
  • Article 2, First link found in the article.
  • Article 2, Second link found in the article.
  • Article 2, Third link found in the article.

From here, the edge file can be used to build a network
visualization where the nodes publishers and the edges between
them represent the in-context links found from our Gdelt data pull
around whatever topic we desired.

This final visualization is a network representation of the
publishers who have written stories about addiction, and where
those stories link to.

What can we learn from this graph?

This tells us which nodes (Publisher websites) have the most
In-Degree links. In other words: who is the most linked. We can see
that the most linked-to for this topic are:

  • tmz.com
  • people.com
  • cdc.gov
  • cnn.com
  • go.com
  • nih.gov
  • ap.org
  • latimes.com
  • jamanetwork.com
  • nytimes.com

Which publisher is most influential? 

Using the “Betweenness Centrality” score given by Gephi, we get
a rough understanding of which nodes (publishers) in the network
act as hubs of information transfer. The nodes with the highest
“Betweenness Centrality” can be thought of as the “connectors” of
the network. Getting pickups from these high-betweenness centrality
nodes gives a much greater likelihood of syndication for that
specific topic/theme. 

  • Dailymail.co.uk
  • Nytimes.com
  • People.com
  • CNN.com
  • Latimes.com
  • washingtonpost.com
  • usatoday.com
  • cvslocal.com
  • huffingtonpost.com
  • sfgate.com

What else can we learn?

Similar to the first example, the higher the betweenness
centrality numbers, number of In-degree links, and the more
centrally located in the graph, the more “important” that node
can generally be said to be. Using this as a guide, the most
important pitching targets can be easily identified. 

Understanding some of the edge clusters gives additional
insights into other potential opportunities. Including a few
clusters specific to different regional or state local news, and a
few foreign language publication clusters.

Wrapping up

I’ve outlined two different techniques we use at Fractl to
understand the influence networks around specific topical areas,
both in terms of publications and the writers at those
publications. The visualization techniques described are not
obvious guides, but instead, are tools for combing through large
amounts of data and finding hidden information. Use these
techniques to unearth new opportunities and prioritize as you get
ready to find the best places to pitch the content you’ve worked
so hard to create.

Do you have any similar ideas or tactics to ensure you’re
pitching the best writers and publishers with your content? Comment
below!

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Social Media Audience: Your Audience Might Not Be Where You Think They Are


social media audience being where your audience is today and tomorrow

Marketing is the science of influencing buying decisions by
convincing your customers that they need what you are selling –
no matter if it’s a product or a service. In this day and age,
marketing is at the heart of the economy – after all, there are
simply far too many products and services for…

Read
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The post Social
Media Audience: Your Audience Might Not Be Where You Think They
Are
authored by Neal Schaffer
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Influencer
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How to Build a Successful Instagram Ad Campaign With Only $5 a Day

Want to do more with Instagram ads? Wondering how to create an
affordable Instagram ad campaign that runs automatically? In this
article, you’ll discover how to create and run a self-sustaining
Instagram ad sequence that converts followers into customers for as
little as $5 per day. #1: Identify Your Most Engaging Instagram
Posts Let’s dive

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We Dipped Our Toes Into Double Featured Snippets

Posted by TheMozTeam

This post was originally published on the STAT blog.

Featured snippets, a vehicle for voice search and the answers to
our most pressing questions, have doubled on the SERPs — but not
in the way we usually mean. This time, instead of appearing on two
times the number of SERPS, two snippets are appearing on the same
SERP. Hoo!

In all our years of obsessively stalking snippets, this is one
of the first documented cases of them doing something a little
different. And we are here for it.

While it’s still early days for the double-snippet SERP,
we’re giving you everything we’ve got so far. And the bottom
line is this: double the snippets mean double the opportunity.

Google’s case for double-snippet SERPs

The first time we heard mention of more than one snippet per
SERP was at the end of January in
Google’s “reintroduction” to featured snippets
.

Not yet launched, details on the feature were a little sparse.
We learned that they’re “to help people better locate
information” and “may also eventually help in cases where you
can get contradictory information when asking about the same thing
but in different ways.”

Thankfully, we only had to wait a month before Google released
them into the wild and gave us a little more insight into their
purpose.

Calling them “multifaceted” featured snippets (a definition
we’re not entirely sure we’re down with),
Google explained
that they’re currently serving
“‘multi-intent’ queries, which are queries that have several
potential intentions or purposes associated,” and will eventually
expand to queries that need more than one piece of information to
answer.

With that knowledge in our back pocket, let’s get to the good
stuff.

The double snippet rollout is starting off small

Since the US-en market is Google’s favorite testing ground for
new features and the largest locale being tracked in STAT, it made
sense to focus our research there. We chose to analyze mobile SERPs
over desktop because of Google’s (finally released)
mobile-first indexing
, and also because that’s where Google
told us
they were starting.

After waiting for enough two-snippet SERPs to show up so we
could get our (proper) analysis on, we pulled our data at the end
March. Out of the mobile keywords currently tracking in the US-en
market in STAT, 122,501 had a featured snippet present, and of
those, 1.06 percent had more than one to its name.

With only 1,299 double-snippet SERPs to analyze, we admit that
our sample size is smaller than our big data nerd selves would
like. That said, it is indicative of how petite this release
currently is.

Two snippets appear for noun-heavy queries

Our first order of business was to see what kind of keywords two
snippets were appearing for. If we can zero in on what Google might
deem “multi-intent,” then we can optimize accordingly.

By weighting our double-snippet keywords by tf-idf, we found
that nouns such as “insurance,” “computer,” “job,” and
“surgery” were the primary triggers — like in [general
liability insurance policy] and [spinal stenosis surgery].

It’s important to note that we don’t see this mirrored in
single-snippet SERPs. When we refreshed our snippet research in
November 2017, we saw that snippets appeared most often for
“how,” followed closely by “does,” “to,” “what,”
and “is.” These are all words that typically compose full
sentence questions.

Essentially, without those interrogative words, Google is left
to guess what the actual question is. Take our [general liability
insurance policy]keyword as an example — does the searcher want
to know what a general liability insurance policy is or how to get
one?

Because of how vague the query is, it’s likely the searcher
wants to know everything they can about the topic. And so, instead
of having to pick, Google’s finally caught onto the wisdom of the
Old El Paso taco girl — why not have both?

Better leapfrogging and double duty domains

Next, we wanted to know where you’d need to rank in order to
win one (or both) of the snippets on this new SERP. This is what we
typically call “source position.”

On a single-snippet SERP and ignoring any SERP features, Google
pulls from the first organic rank 31 percent of the time. On
double-snippet SERPs, the top snippet pulls from the first organic
rank 24.84 percent of the time, and the bottom pulls from organic
ranks 5–10 more often than solo snippets.

What this means is that you can leapfrog more competitors in a
double-snippet situation than when just one is in play.

And when we dug into who’s answering all these questions, we
discovered that 5.70 percent of our double-snippet SERPs had the
same domain in both snippets. This begs the obvious question: is
your content ready to do double duty?

Snippet headers provide clarity and keyword ideas

In what feels like the first new addition to the feature in a
long time, there’s now a header on top of each snippet, which
states the question it’s set out to answer.
With reports
of headers on solo snippets (and
“People also search for” boxes
attached to the bottom —
will this madness never end?!), this may be a sneak peek at the new
norm.

Instead of relying on guesses alone, we can turn to these
headers for what a searcher is likely looking for — we’ll trust
in Google’s excellent consumer research. Using our [general
liability insurance policy] example once more, Google points us to
“what is general liabilities insurance” and “what does a
business insurance policy cover” as good interpretations.

Because these headers effectively turn ambiguous statements into
clear questions, we weren’t surprised to see words like “how”
and “what” appear in more than 80 percent of them. This trend
falls in line with keywords that typically produce snippets, which
we touched on earlier.

So, not only does a second snippet mean double the goodness that
you usually get with just one, it also means more insight into
intent and another keyword to track and optimize for.

Both snippets prefer paragraph formatting

Next, it was time to give formatting a look-see to determine
whether the snippets appearing in twos behave any differently than
their solo counterparts. To do that, we gathered every snippet on
our double-snippet SERPs and compared them against our November
2017 data, back when pairs weren’t a thing.

While Google’s order of preference is the same for both —
paragraphs, lists, and then tables — paragraph formatting was the
clear favorite on our two-snippet SERPs.

It follows, then, that the most common pairing of snippets was
paragraph-paragraph — this appeared on 85.68 percent of our
SERPs. The least common, at 0.31 percent, was the table-table
coupling.

We can give two reasons for this behavior. One, if a query can
have multiple interpretations, it makes sense that a paragraph
answer would provide the necessary space to explain each of them,
and two, Google really doesn’t like tables.

We saw double-snippet testing in action

When looking at the total number of snippets we had on hand, we
realised that the only way everything added up was if a few SERPs
had more than two snippets. And lo! Eleven of our keywords returned
anywhere from six to 12 snippets.

For a hot minute we were concerned that Google was planning a
full-SERP snippet takeover, but when we searched those keywords a
few days later, we discovered that we’d caught testing in
action.

Here’s what we saw play out for the keyword [severe lower back
pain]:

After testing six variations, Google decided to stick with the
first two snippets. Whether this is a matter of top-of-the-SERP
results getting the most engagement no matter what, or the phrasing
of these questions resonating with searchers the most, is hard for
us to tell.

The multiple snippets appearing for [full-time employment] left
us scratching our head a bit:

Our best hypothesis is that searchers in Florida, NYS, Minnesota,
and Oregon have more questions about full-time employment than
other places. But, since we’d performed a nation-wide search,
Google seems to have thought better of including location-specific
snippets.

Share your double-snippet SERP experiences

It goes without saying — but here we are saying it anyway —
that we’ll be keeping an eye on the scope of this release and
will report back on any new revelations.

In the meantime, we’re keen to know what you’re seeing. Have
you had any double-snippet SERPs yet? Were they in a market outside
the US? What keywords were surfacing them? 

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New Facebook Group Management Tools

Welcome to this week’s edition of the Social Media Marketing
Talk Show, a news show for marketers who want to stay on the
leading edge of social media. On this week’s Social Media
Marketing Talk Show, we explore new Facebook group management tools
with special guest, Bella Vasta. Watch the Social Media Marketing
Talk Show

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What a Two-Tiered SERP Means for Content Strategy – Whiteboard Friday

Posted by willcritchlow

If you’re a big site competing to rank for popular head terms,
where’s the best place to focus your content strategy? According to
a hypothesis by the good folks at Distilled, the answer may lie in
perfectly satisfying searcher intent.

https://fast.wistia.net/embed/iframe/z392yvpkam?videoFoam=true

Click on the whiteboard image above to open a high resolution
version in a new tab!

If you haven’t heard the news, the Domain
Authority
 metric discussed in this episode will be updated on
March 5th, 2019 to better correlate with Google
algorithm changes. Learn about what’s
changing below:

Learn more about
the new DA

Video Transcription

Hi, Whiteboard Friday fans. I’m Will Critchlow, one of the
founders at Distilled, and what I want to talk about today is
joining the dots between some theoretical work that some of my
colleagues have been doing and some of the client work that we’ve
been doing recently and the results that we’ve been seeing from
that in the wild and what I think it means for strategies for
different-sized sites going on from here.

Correlations and a hypothesis

The beginning of this I credit to one of my colleagues, Tom
Capper, THCapper on
Twitter
, who presented at our Search Love London conference a
presentation entitled “The
Two-Tiered SERP
,” and I’m going to describe what that means in
just a second. But what I’m going to do today is talk about what I
think that the two-tiered SERP means for content strategy going
forward and base that a little bit on some of what we’re seeing in
the wild with some of our client projects.

What Tom presented at Search Love London was he started by
looking at the fact that the correlation between domain authority
and rankings has decreased over time. So he pulled out some stats
from February 2017 and looked at those same stats 18 months later
and saw a significant drop in the correlation between domain
authority and rankings. This ties into a bunch of work that he’s
done and presented elsewhere around potentially less reliance on
links going forward and some other data that Google might be using,
some other metrics and ranking factors that they might be using in
their place, particularly branded metrics and so forth.

But Tom saw this drop and had a hypothesis that it wasn’t just
an across-the-board drop. This wasn’t just Google not using links
anymore or using links less. It was actually a more granular effect
than that. This is the two-tiered SERP or what we mean by the
two-tiered SERP. So a search engine result page, a SERP, you’ve got
some results at the top and some results further down the page.

What Tom found — he had this hypothesis that was born out in
the data — was that the correlation between domain authority and
rankings was much higher among the positions 6 through 10 than it
was among the top half of the search results page and that this can
be explained by essentially somewhat traditional ranking factors
lower down the page and in lower competition niches and that at the
top of the page, where there’s more usage data, greater search
volume and so forth in these top positions, that traditional
ranking factors played less of a part.

They maybe get you into the consideration set. There are no
domains ranking up here that are very, very weak. But once you’re
in the consideration set, there’s much less of a correlation
between these different positions. So it’s still true on average
that these positions 1 through 5 are probably more authoritative
than the sites that are appearing in lower positions. But within
this set there’s less predictive value.

The domain authority is less predictive of ranking within this
set than it is of ranking within this set. So this is the
two-tiered SERP, and this is consistent with a bunch of data that
we’ve seen across the place and in particular with the outcomes
that we’re seeing among content campaigns and content strategies
for different kinds of sites.

At Distilled, we get quite a lot of clients coming to us wanting
either a content strategy put together or in some cases coming to
us essentially with their content strategy and saying, “Can you
execute this? Can you help us execute this plan?” It’s very common
for that plan to be, “We want to create a bunch of big pieces of
content that get a ton of links, and we’re going to use that link
authority to make our site more authoritative and that is going to
result in our whole site doing better and ranking better.”

An anonymized case study

We’ve seen that that is performing differently in different
cases, and in particular it’s performing better on smaller sites
than it is on big sites. So this is a little anonymized case study.
This is a real example of a story that happened with one of our
consulting clients where we put in place a content strategy for
them that did include a plan to build the domain authority because
this was a site that came to us with a domain authority
significantly below that of their key competitors, also with all of
these sites not having a ton of domain authority.

This was working in a B2B space, relatively small domains. They
came to us with that, and we figured that actually growing the
authority was a key part of this content strategy and over the next
18 months put out a bunch of pieces that have done really well and
generated a ton of press coverage and traction and things. Over
that time, they’ve actually outstripped their key competitors in
the domain authority metrics, and crucially we saw that tie
directly to increases in traffic that went hand-in-hand with this
increase in domain authority.

But this contrasts to what we’ve seen with some much larger
sites in much more competitive verticals where they’re already
very, very high domain authority, maybe they’re already stronger
than some of their competitors and adding to that. So adding big
content pieces that get even more big authoritative links has not
moved the needle in the way that it might have done a few years
ago.

That’s totally consistent with this kind of setup, where if you
are currently trying to edge in the bottom or you’re competing for
less competitive search terms, then this kind of approach might
really work for you and it might, in fact, be necessary to get into
the consideration set for the more competitive end. But if you’re
operating on a much bigger site, you’ve already got the competitive
domain authority, you and your competitors are all very powerful
sites, then our kind of hypothesis is that you’re going to be
needing to look more towards the user experience, the conversion
rate, and intent research.

Are you satisfying searcher intent for competitive head terms?

What is somebody who performs this search actually looking to
do? Can you satisfy that intent? Can you make sure that they don’t
bounce back to the search results and click on a competitor? Can
you make sure that in fact they stay on your site, they get done
the thing they want to get done, and it all works out for them,
because we think that these kinds of things are going to be much
more powerful for moving up through the very top end of the most
competitive head terms.

So when we’re working on a content strategy or putting our
creative team to work on these kinds of things on bigger sites,
we’re more likely to be creating content directly designed to rank.
We might be creating content based off a ton of this research, and
we’re going to be incrementally improving those things to try and
say, “Have we actually satisfied the perfect intent for this super
competitive head term?”

What we’re seeing is that’s more likely to move the needle up at
this top end than growing the domain authority on a big site. So I
hope you found that interesting. I’m looking forward to a vigorous
discussion in the comments on this one. But thank you for joining
me for this week’s Whiteboard Friday. I’ve been Will Critchlow from
Distilled. Take care.

Video
transcription
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The Influence of Voice Search on Featured Snippets

Posted by TheMozTeam

This post was originally published on the STAT blog.

We all know that featured snippets provide easy-to-read,
authoritative answers and that digital assistants love to say them
out loud when asked questions.

This means that featured snippets have an impact on voice search
— bad snippets, or no snippets at all, and digital assistants
struggle. By that logic: Create a lot of awesome snippets and win
the voice search race. Right?

Right, but there’s actually a far more interesting angle to
examine — one that will help you nab more snippets and optimize
for voice search at the same time. In order to explore this, we
need to make like Doctor Who and go back in time.

From typing to talking

Back when dinosaurs roamed the earth and queries were typed into
search engines via keyboards, people adapted to search engines by
adjusting how they performed queries. We pulled out unnecessary
words and phrases, like “the,” “of,” and, well, “and,”
which created truncated requests — robotic-sounding searches for
a robotic search engine.

The first ever dinosaur to use Google.

From this, we may be inclined to format all of our “best”
and “how to” keyword content into lists. But, as you can see in
the chart above, paragraphs and tables are still appearing here,
and we could be leaving snippets on the table by ignoring them. If
we have time, we’ll dig into which keywords those formats are a
better fit for and why.

Get tracking

You could be the Wonder Woman of meta descriptions, but if you
aren’t optimizing for the right kind of snippets, then your
content’s going to have a harder time getting heard. Building out
a voice search-friendly keyword list to track is the first step to
lassoing those snippets.

Want to learn how you can do that in STAT? Say hello and
request a tailored
demo
.

Need more snippets in your life? We dug into Google’s
double-snippet SERPs
for you — double the snippets, double
the fun.

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