Can’t-Miss Sessions at 2019’s B2B Marketing Exchange Conference #B2BMX

B2B marketers rejoice! This week marks the annual B2B Marketing Exchange (B2BMX) conference in Scottsdale, AZ. With the help of an amazing group of speakers, this event is designed to bring the latest and greatest B2B marketing trends to light in a way that enables teams to tackle any new marketing obstacles the year holds.

At B2BMX, TopRank Marketing CEO Lee Odden will help change the narrative as he tackles why B2B marketing doesn’t have to be boring in his session: Break Free of Boring B2B With Interactive Influencer Content. In this session, Lee will share ideas for top interactive formats for B2B, best practices for influencer content engagement, and how to pull it all together with examples from successful B2B brands. If you’ll be attending, please join us on Tuesday, Feb. 26 at 11:25 a.m. for Lee’s session!

When attending events, it can be tough to choose between different sessions because let’s face it, you want to see it all! To help you navigate B2BMX this week, I’ve put together a list of sessions that our team is looking forward to the most.

9 Other Must-See Sessions at B2BMX

#1 – Developing Fans in a World of Digital Overload

When: Tuesday, Feb. 26 26 at 8:45 a.m.

Who: David Meerman Scott, Best-Selling Author

Why Attend: Consumers today are overloaded and overwhelmed with the amount of data and marketing crossing their screens each day. In this session, David will help B2B companies focus on the notion of looking past leads and prospects to develop passionate fans for their brands, ideas, solutions and services.

#2 – Can Marketing Save the World?

When: Tuesday, Feb. 26 at 9:30 a.m.

Who: Carlos Abler, Leader of Content Marketing & Strategy, 3M

Why Attend: Today’s audiences know what they want, and they want it now. 3M’s Carlos Abler’s session will focus on how brands can address the demands of the modern day customer and deliver content excellence to add value.

#3 – Humanizing Even The World’s Most Boring B2B Brands: How to Win With Empathy, Creativity…And A Little Humor

When: Tuesday, Feb. 26 at 1:30 p.m.

Who: Tim Washer, Keynote Speaker & Event Emcee

Why Attend: Tim’s vast experience as a comedian and producer have led to him helping some of today’s largest organizations find humor (in unexpected places). In his session, Tim will lead the audience through a series of exercises to overcome stumbling blocks that get in the way of creativity and approaches for creative storytelling (no matter your budget).

#4 – ABM at Scale: Oracle’s Account-Based Strategy With People, Process, Data & Tech

When: Tuesday, Feb. 26 at 3:45 p.m.

Who: Kelvin Gee, Senior Director, Modern Marketing Business Transformation, Oracle

Why Attend: Coordinating marketing and sales is a challenge for every company. Let alone, an enterprise brand like Oracle with customers in 175 countries. In his session, Kelvin will share ideas for taking an account-based approach as well as the best ways to orchestrate activities in-market.

#5 – Sales & Marketing Alignment – From Hate to Love in 60 Days

When: Wednesday, Feb. 27 at 10:40 a.m.

Who: Shahid Javed, Director Enterprise Marketing, Hughes Network Systems

Why Attend: Sales and marketing alignment doesn’t happen overnight. Shahid will share a framework to break down common silos and develop alignment, all in 60 days.  Attendees will also learn ways to create great content that supports sales teams and improves engagement.

#6 – ‘Next Level’ B2B Content Marketing Strategies

When: Wednesday, Feb. 27 at 10:40 a.m.

Who: Michael Brenner, CEO, Marketing Insider Group

Why Attend: Marketers are in the hot seat at many organizations large and small. The reason? For years marketing activities weren’t directly tied to ROI. But that has all changed. Join Michael Brenner as he walks you through how to put the customer at the center of all of your marketing activities in order to deliver better outcomes for your business.

#7 – Martech, Process & People: A Journey to Channel Transformation

When: Wednesday, Feb. 27 at 11:20 a.m.

Who: Liz Cope, Director, Marketing Technology & Operations, Ingersoll Rand

Why Attend: Transformation requires an integration beyond just technology. Processes and the people who build and enforce them also need to align in order to be successful. In her session, Liz will share how to pull it all together for better channel transformation.

#8 – Messaging Apps Applied to B2B Marketing

When: Wednesday, Feb. 27 at 11:20 a.m.

Who: Andrew Spoeth, Director, Digital Marketing, CA Technologies

Why Attend: Messaging apps have been widely used by B2C marketers for years. And now, B2B marketers are beginning to see early success incorporating messaging apps into their strategies. But how does one go about incorporating messaging into a B2B strategy? Andrew will answer this question and provide insights into current trends and tools available for B2B marketers.

#9 – Tapping Influencers to Fuel Your Content Creation Engine

When: Wednesday, Feb. 27 at 1:35 p.m.

Who: Amanda Maksymiw, Content Marketing Director, Fuze

Why Attend: As a B2B marketing agency with a focus on Influencer Marketing, we are especially excited for this presentation from Amanda Maksymiw. In her session, Amanda will help brands learn how to leverage influencers for more effective content creation and collaborate with influencers in a way that extends beyond social media.

See You in Scottsdale!

If you’re attending B2BMX this week, we hope to see you there! If not, you can follow along online with the hashtag #B2BMX. For live updates from the conference, you can follow  @TopRank, @leeodden, @azeckman and @CaitlinMBurgess on Twitter. In addition to speaking and tweeting, team members from TopRank Marketing will be live blogging sessions throughout the conference so be sure to follow the blog for more.

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

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:

  • 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

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
  • David Kroll (@davidkroll) Freelance healthcare writer, Forbes Heath
  • Jeanne Whalen (@jeannewhalen) Business Reporter, Washington Post
  • Travis Lupick (@tlupick), Journalist, Author
  • Johann Hari (@johannhari101) New York Times best-selling author
  • Radley Balko (@radleybalko) Opinion journalist, Washington Post
  • Sam Quinones (@samquinones7), Author
  • Eric Bolling (@ericbolling) New York Times best-selling author
  • Dana Milbank (@milbank)Columnist, Washington Post
  • Mike Riggs (@mikeriggs) Writer & Editor, Reason Mag 

@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!

We Dipped Our Toes Into Double Featured Snippets

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?