Tag Archive | "Graph"

How Mobile-First Indexing Disrupts the Link Graph

Posted by rjonesx.

It’s happened to all of us. You bring up a webpage on your mobile device, only to find out that a feature you were accustomed to using on desktop simply isn’t available on mobile. While frustrating, it has always been a struggle for web developers and designers alike to simplify and condense their site on mobile screens without needing to strip features or content that would otherwise clutter a smaller viewport. The worst-case scenario for these trade-offs is that some features would be reserved for desktop environments, or perhaps a user might be able to opt out of the mobile view. Below is an example of how my personal blog displays the mobile version using a popular plugin by ElegantThemes called HandHeld. As you can see, the vast page is heavily stripped down and is far easier to read… but at what cost? And at what cost to the link graph?

My personal blog drops 75 of the 87 links, and all of the external links, when the mobile version is accessed. So what happens when the mobile versions of sites become the primary way the web is accessed, at scale, by the bots which power major search engines?

Google’s announcement to proceed with a mobile-first index raises new questions about how the link structure of the web as a whole might be influenced once these truncated web experiences become the first (and sometimes only) version of the web Googlebot encounters.

So, what’s the big deal?

The concern, which no doubt Google engineers have studied internally, is that mobile websites often remove content and links in order to improve user experience on a smaller screen. This abbreviated content fundamentally alters the link structure which underlies one of the most important factors in Google’s rankings. Our goal is to try and understand the impact this might have.

Before we get started, one giant unknown variable which I want to be quick to point out is we don’t know what percentage of the web Google will crawl with both its desktop and mobile bots. Perhaps Google will choose to be “mobile-first” only on sites that have historically displayed an identical codebase to both the mobile and desktop versions of Googlebot. However, for the purposes of this study, I want to show the worst-case scenario, as if Google chose not only to go “mobile-first,” but in fact to go “mobile-only.”

Methodology: Comparing mobile to desktop at scale

For this brief research, I decided to grab 20,000 random websites from the Quantcast Top Million. I would then crawl two levels deep, spoofing both the Google mobile and Google desktop versions of Googlebot. With this data, we can begin to compare how different the link structure of the web might look.

Homepage metrics

Let’s start with some descriptive statistics of the home pages of these 20,000 randomly selected sites. Of the sites analyzed, 87.42% had the same number of links on their homepage regardless of whether the bot was mobile- or desktop-oriented. Of the remaining 12.58%, 9% had fewer links and 3.58% had more. This doesn’t seem too disparate at first glance.

Perhaps more importantly, only 79.87% had identical links on the homepage when visited by desktop and mobile bots. Just because the same number of links were found didn’t mean they were actually the same links. This is important to take into consideration because links are the pathways which bots use to find content on the web. Different paths mean a different index.

Among the homepage links, we found a 7.4% drop in external links. This could mean a radical shift in some of the most important links on the web, given that homepage links often carry a great deal of link equity. Interestingly, the biggest “losers” as a percentage tended to be social sites. In retrospect, it seems reasonable that one of the common types of links a website might remove from their mobile version would be social share buttons because they’re often incorporated into the “chrome” of a page rather than the content, and the “chrome” often changes to accommodate a mobile version.

The biggest losers as a percentage in order were:

  1. linkedin.com
  2. instagram.com
  3. twitter.com
  4. facebook.com

So what’s the big deal about 5–15% differences in links when crawling the web? Well, it turns out that these numbers tend to be biased towards sites with lots of links that don’t have a mobile version. However, most of those links are main navigation links. When you crawl deeper, you just find the same links. But those that do deviate end up having radically different second-level crawl links.

Second-level metrics

Now this is where the data gets interesting. As we continue to crawl out on the web using crawl sets that are influenced by the links discovered by a mobile bot versus a desktop bot, we’ll continue to get more and more divergent results. But how far will they diverge? Let’s start with size. While we crawled an identical number of home pages, the second-tier results diverged based on the number of links found on those original home pages. Thus, the mobile crawlset was 977,840 unique URLs, while the desktop crawlset was 1,053,785. Already we can see a different index taking shape — the desktop index would be much larger. Let’s dig deeper.

I want you to take a moment and really focus on this graph. Notice there are three categories:

  • Mobile Unique: Blue bars represent unique items found by the mobile bot
  • Desktop Unique: Orange bars represent unique items found by the desktop bot
  • Shared: Gray bars represent items found by both

Notice also that there are there are four tests:

  • Number of URLs discovered
  • Number of Domains discovered
  • Number of Links discovered
  • Number of Root Linking Domains discovered

Now here is the key point, and it’s really big. There are more URLs, Domains, Links, and Root Linking Domains unique to the desktop crawl result than there are shared between the desktop and mobile crawler. The orange bar is always taller than the gray. This means that by just the second level of the crawl, the majority of link relationships, pages, and domains are different in the indexes. This is huge. This is a fundamental shift in the link graph as we have come to know it.

And now for the big question, what we all care about the most — external links.

A whopping 63% of external links are unique to the desktop crawler. In a mobile-only crawling world, the total number of external links was halved.

What is happening at the micro level?

So, what’s really causing this huge disparity in the crawl? Well, we know it has something to do with a few common shortcuts to making a site “mobile-friendly,” which include:

  1. Subdomain versions of the content that have fewer links or features
  2. The removal of links and features by user-agent detecting plugins

Of course, these changes might make the experience better for your users, but it does create a different experience for bots. Let’s take a closer look at one site to see how this plays out.

This site has ~10,000 pages according to Google and has a Domain Authority of 72 and 22,670 referring domains according to the new Moz Link Explorer. However, the site uses a popular WordPress plugin that abbreviates the content down to just the articles and pages on the site, removing links from descriptions in the articles on the category pages and removing most if not all extraneous links from the sidebar and footer. This particular plugin is used on over 200,000 websites. So, what happens when we fire up a six-level-deep crawl with Screaming Frog? (It’s great for this kind of analysis because we can easily change the user-agent and restrict settings to just crawl HTML content.)

The difference is shocking. First, notice that in the mobile crawl on the left, there is clearly a low number of links per page and that number of links is very steady as you crawl deeper through the site. This is what produces such a steady, exponential growth curve. Second, notice that the crawl abruptly ended at level four. The site just didn’t have any more pages to offer the mobile crawler! Only ~3,000 of the ~10,000 pages Google reports were found.

Now, compare this to the desktop crawler. It explodes in pages at level two, collecting nearly double the total pages of the mobile crawl at this level alone. Now, recall the graph before showing that there were more unique desktop pages than there were shared pages when we crawled 20,000 sites. Here is confirmation of exactly how it happens. Ultimately, 6x the content was made available to the desktop crawler in the same level of crawl depth.

But what impact did this have on external links?

Wow. 75% of the external, outbound links were culled in the mobile version. 4,905 external links were found in the desktop version while only 1,162 were found in the mobile. Remember, this is a DA 72 site with over twenty thousand referring domains. Imagine losing that link because the mobile index no longer finds the backlink. What should we do? Is the sky falling?

Take a deep breath

Mobile-first isn’t mobile-only

The first important caveat to all this research is that Google isn’t giving up on the desktop — they’re simply prioritizing the mobile crawl. This makes sense, as the majority of search traffic is now mobile. If Google wants to make sure quality mobile content is served, they need to shift their crawl priorities. But they also have a competing desire to find content, and doing so requires using a desktop crawler so long as webmasters continue to abbreviate the mobile versions of their sites.

This reality isn’t lost on Google. In the Original Official Google Mobile First Announcement, they write…

If you are building a mobile version of your site, keep in mind that a functional desktop-oriented site can be better than a broken or incomplete mobile version of the site.

Google took the time to state that a desktop version can be better than an “incomplete mobile version.” I don’t intend to read too much into this statement other than to say that Google wants a full mobile version, not just a postcard.

Good link placements will prevail

One anecdotal outcome of my research was that the external links which tended to survive the cull of a mobile version were often placed directly in the content. External links in sidebars like blog-rolls were essentially annihilated from the index, but in-content links survived. This may be a signal Google picks up on. External links that are both in mobile and desktop tend to be the kinds of links people might click on.

So, while there may be fewer links powering the link graph (or at least there might be a subset that is specially identified), if your links are good, content-based links, then you have a chance to see improved performance.

I was able to confirm this by looking at a subset of known good links. Using Fresh Web Explorer, I looked up fresh links to toysrus.com which is currently gaining a great deal of attention due to stores closing. We can feel confident that most of these links will be in-content because the articles themselves are about the relevant, breaking news regarding Toys R Us. Sure enough, after testing 300+ mentions, we found the links to be identical in the mobile and desktop crawls. These were good, in-content links and, subsequently, they showed up in both versions of the crawl.

Selection bias and convergence

It is probably the case that popular sites are more likely to have a mobile version than non-popular sites. Now, they might be responsive — at which point they would yield no real differences in the crawl — but at least some percentage would likely be m.* domains or utilize plugins like those mentioned above which truncate the content. At the lower rungs of the web, older, less professional content is likely to have only one version which is shown to mobile and desktop devices alike. If this is the case, we can expect that over time the differences in the index might begin to converge rather than diverge, as my study looked only at sites that were in the top million and only crawled two levels deep.

Moreover (this one is a bit speculative), but I think over time that there will be convergence between a mobile and desktop index. I don’t think the link graphs will grow exponentially different as the linked web is only so big. Rather, the paths to which certain pages are reached, and the frequency with which they are reached, will change quite a bit. So, while the link graph will differ, the set of URLs making up the link graph will largely be the same. Of course, some percentage of the mobile web will remain wholly disparate. The large number of sites that use dedicated mobile subdomains or plugins that remove substantial sections of content will remain like mobile islands in the linked web.

Impact on SERPs

It’s difficult at this point to say what the impact on search results will be. It will certainly not leave the SERPs unchanged. What would be the point of Google making and announcing a change to its indexing methods if it didn’t improve the SERPs?

That being said, this study wouldn’t be complete without some form of impact assessment. Hat tip to JR Oakes for giving me this critique, otherwise I would have forgotten to take a look.

First, there are a couple of things which could mitigate dramatic shifts in the SERPs already, regardless of the veracity of this study:

  • A slow rollout means that shifts in SERPs will be lost to the natural ranking fluctuations we already see.
  • Google can seed URLs found by mobile or by desktop into their respective crawlers, thereby limiting index divergence. (This is a big one!)
  • Google could choose to consider, for link purposes, the aggregate of both mobile and desktop crawls, not counting one to the exclusion of the other.

Second, the relationships between domains may be less affected than other index metrics. What is the likelihood that the relationship between Domain X and Domain Y (more or less links) is the same for both the mobile- and desktop-based indexes? If the relationships tend to remain the same, then the impact on SERPs will be limited. We will call this relationship being “directionally consistent.”

To accomplish this part of the study, I took a sample of domain pairs from the mobile index and compared their relationship (more or less links) to their performance in the desktop index. Did the first have more links than the second in both the mobile and desktop? Or did they perform differently?

It turns out that the indexes were fairly close in terms of directional consistency. That is to say that while the link graphs as a whole were quite different, when you compared one domain to another at random, they tended in both data sets to be directionally consistent. Approximately 88% of the domains compared maintained directional consistency via the indexes. This test was only run comparing the mobile index domains to the desktop index domains. Future research might explore the reverse relationship.

So what’s next?: Moz and the mobile-first index

Our goal for the Moz link index has always been to be as much like Google as possible. It is with that in mind that our team is experimenting with a mobile-first index as well. Our new link index and Link Explorer in Beta seeks to be more than simply one of the largest link indexes on the web, but the most relevant and useful, and we believe part of that means shaping our index with methods similar to Google. We will keep you updated!

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How to use the Knowledge Graph for higher rankings

Contributor Ryan Shelley recommends looking into the content displayed in a knowledge card and using what you find to develop a smart and targeted content marketing campaign for your website.

The post How to use the Knowledge Graph for higher rankings appeared first on Search Engine Land.

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Knowledge Graph Eats Featured Snippets, Jumps +30%

Posted by Dr-Pete

Over the past two years, we’ve seen a steady and substantial increase in Featured Snippets on Google SERPs. In our 10,000-keyword daily tracking set, Featured Snippets have gone from about 5.5% of queries in November 2015 to a recent high of just over 16% (roughly tripling). Other data sets, with longer tail searches, have shown even higher prevalence.

Near the end of October (far-right of the graph), we saw our first significant dip (spotted by Brian Patterson and Chris Long on SEL). This dip occurred over about a 4-day period, and represents roughly a 10% drop in searches with Featured Snippets. Here’s an enhanced, 2-week view (note: Y-axis is expanded to show the day-over-day changes more clearly):

Given the up-and-to-the-right history of Featured Snippets and the investments people have been making optimizing for these results, a 10% drop is worthy of our attention.

What happened, exactly?

To be honest, when we investigate changes like this, the best we can usually do is produce a list of keywords that lost Featured Snippets. Usually, we focus on high-volume keywords, which tend to be more interesting. Here’s a list of keywords that lost Featured Snippets during that time period:

  • CRM
  • ERP
  • MBA
  • buddhism
  • web design
  • anger management
  • hosting
  • DSL
  • ActiveX
  • ovulation

From an explanatory standpoint, this list isn’t usually very helpful – what exactly do “web design”, “buddhism”, and “ovulation” have in common (please, don’t answer that)? In this case, though, there was a clear and interesting pattern. Almost all of the queries that lost Featured Snippets gained Knowledge Panels that look something like this one:

These new panels account for the vast majority of the lost Featured Snippets I’ve spot-checked, and all of them are general Knowledge Panels coming directly from Wikipedia. In some cases, Google is using a more generic Knowledge Graph entry. For example, “HDMI cables”, which used to show a Featured Snippet (dominated by Amazon, last I checked), now shows no snippet and a generic panel for “HDMI”:

In very rare cases, a SERP added the new Knowledge Panel but retained the Featured Snippet, such as the top of this search for “credit score”:

These situations seemed to be the exceptions to the rule.

What about other SERPs?

The SERPs that lost Featured Snippets were only one part of this story. Over the same time period, we saw an explosion (about +30%) in Knowledge Panels:

This Y-axis has not been magnified – the jump in Knowledge Panels is clearly visible even at normal scale. Other tracking sites saw similar, dramatic increases, including this data from RankRanger. This jump appears to be a similar type of descriptive panel, ranging from commercial keywords, like “wedding dresses” and “Halloween costumes”…

…to brand keywords, like “Ray-Ban”…

Unlike definition boxes, many of these new panels appear on words and phrases that appear to be common knowledge and add little value. Here’s a panel on “job search”…

I suspect that most people searching for “job search” or “job hunting” don’t need it defined. Likewise, people searching for “travel” probably weren’t confused about what travel actually is…

Thanks for clearing that up, Google. I’ve decided to spare you all and leave out a screenshot for “toilet” (go ahead and Google it). Almost all of these new panels appear to be driven by Wikipedia (or Wikidata), and most of them are single-paragraph definitions of terms.

Were there other changes?

During the exact same period, we also noticed a drop in SERPs with inline image results. Here’s a graph of the same 2-week period reported for the other features:

This drop almost exactly mirrors the increase in Knowledge Panels. In cases where the new panels were added, those panels almost always contain a block of images at the top. This block seems to have replaced inline image results. It’s interesting to note that, because image blocks in the left-hand column consume an organic position, this change freed up an organic spot on the first page of results for those terms.

Why did Google do this?

It’s likely that Google is trying to standardize answers for common terms, and perhaps they were seeing quality or consistency issues in Featured Snippets. In some cases, like “HDMI cables”, Featured Snippets were often coming from top e-commerce sites, which are trying to sell products. These aren’t always a good fit for unbiased definitions. Its also likely that Google would like to beef up the Knowledge Graph and rely less, where possible, on outside sites for answers.

Unfortunately, this also means that the answers are coming from a much less diverse pool (and, from what we’ve seen, almost entirely from Wikipedia), and it reduces the organic opportunity for sites that were previously ranking for or trying to compete for Featured Snippets. In many cases, these new panels also seem to add very little. Someone searching for “ERP” might be helped by a brief definition, but someone searching for “travel” is unlikely looking to have it explained to them.

As always, there’s not much we can do but monitor the situation and adapt. Featured Snippets are still at historically high levels and represent a legitimate organic opportunity. There’s also win-win, since efforts invested in winning Featured Snippets tend to improve organic ranking and, done right, can produce a better user experience for both search and website visitors.

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Google showing knowledge graph data in local panels

Google is merging the local panel and knowledge graph panels into one for some searches related to big companies with many local venues.

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The Marketer’s Guide to Facebook Graph Search

Posted by SimonPenson

Social search has long been heralded as the “next big thing.” The opportunity to create the search engine for people is too enticing; the prize being held above all others in the race to build the next Google.

Facebook is widely recognised as the only company other than the search giant itself capable of creating such a product, and it’s one of the key reasons behind its enormous 
price-to-earnings ratio. Few are investing on the strength of the current format; instead they know that the data it has at its fingertips could be world-changing in terms of information retrieval and advertising.

And that project began in earnest, publicly at least, with the launch of Graph Search in 2013.

At the time the product lacked any significant features and after a short fanfare, marketers’ focus shifted elsewhere. The engineers, however, had very clear instructions to iterate, fast, and the results of that work are now starting to float to the surface.

What is graph search?

Graph Search is Facebook’s way of mapping all the data we give the platform together in a really useful way. It is by far the best example of “Social Search” – the premise of creating a search engine based not on websites but on entities – people, places and things.

The company has been quietly iterating it since last year. There’s still a long way to go but the foundations are already there for what promises to be the only true rival to Google in the world of information organization and retrieval and only days ago did they start testing new functionality that allows some users to search through content as well as people, interests and things.

Search operators

So what has changed that makes Facebook’s search engine worth talking about
again for digital marketers?

The answer is the introduction of a large number of much more sophisticated search operators, or ways of searching, layered over the top of a greater connected data set.

To help understand what we mean, we have created this free 
Facebook Graph Search Cheat Sheet, brimming with many of the useful connotations you may want to use to improve your understanding of your customers, or those of your competitors.

This post is about using it specifically to find, learn about, and work with influencers in your space and build out a much more detailed picture of your existing and prospective audiences.

Using Graph Search

Before you can even begin extracting useful information from the platform, however, you need to check to see if you have access to the full search facility. To do that there is a very simple little hack that involves changing your language settings.

Claiming Graph Search

If you are reading this from the US then chances are you will already have Graph Search by default. That is not the case for all. If you fall outside of this and still have the painfully poor old search box you must head into settings to change this.

To do it search for your settings in the top nav.

Next, go to the General account settings and change your language to English (US) and hey, presto: You should now have enabled Graph Search.

Marketing uses

There will undoubtedly be scores more ways for marketers to use Graph Search as the months roll on and functionality improves, but as we examine the options today we can divide them into five key areas:

  1. Audience insight
  2. Influencer discovery
  3. Influencer research
  4. GSO – Graph Search optimization
  5. Advertising

1. Audience insight

Number one on the list has to be the ability it gives you to join the dots in your audience research work. The endless hours I and other marketers spent sitting behind one way screens as part of
ethnography research group work in past decades made true insight very labour-intensive and expensive. It was also only partially effective, as by the time data from these methods was processed it was often weeks, or even months, old.

Not for one moment am I saying, however, that those methods have no value, as getting in front of your audience to see how they really use or interact with your product(s) can be extremely useful. But the data pot is small.

Where you can really start to trust your findings is when the data you are handling is “big,” aggregating the beliefs of tens of thousands of current or potential customers.

So, how does Graph Search help? Let’s look at that in a little more detail.

Initial research

We have already discussed how the functionality of Facebook’s search engine has come on in leaps and bounds and here we can start putting that to use.

We’ve created a free cheat sheet to help you navigate the scores of search operators (
download it here).

Let’s look at a couple of examples in real time now so you can see how it works.

For this run-through we’ve chosen a brand in the UK entertainment space, but we have kept the brand anonymous. The process of running through that data, however, should give you a very good understanding of how to work through this, step-by-step, for your own brand.

To begin with, we can start with something relatively simple: A look at other pages liked by those that like the brand. This helps create a better understanding of the other interests of the audience:

To achieve this, search for: “Pages liked by people who like INSERT BRAND”.

As yet, however, we are not getting into anything especially insightful. To really dive into the exciting data we must find a way of segmenting these random brand and page affinities with broader interest sets.

To do this we can again lean on Graph Search to provide us with that detail.

To achieve this search for: ”favorite interests of people who like INSERT BRAND”.

As you can see, there are already some revealing interests coming to the fore, and we’ll look at those in greater detail a little later.

As with any research, however, results can be skewed by small data sets, and so to bulk out those numbers it is possible to combine your brand with others in the same space.

To achieve this search for: “favorite interests of people who like INSERT BRAND and INSERT COMPETITOR”.

Once you have a list of relevant and useful results, the options are almost endless, and it is at this point that you can decide to add extra depth to the areas that matter most.

For instance, for our example brand it is important to understand what drinks and food the audience likes, as they run a large number of “brick-and-mortar” outlets.

To achieve this search for: ‘favorite ‘DRINK/FOOD’ of people who like ‘INSERT BRAND’.

And given that discussions may be ongoing around brand ambassadors it may be useful to extract some information about favourite celebrities, musicians or entertainers.

To achieve this search for: ‘favorite ‘DRINK/FOOD’ of people who like ‘INSERT BRAND’.

The results here can be truly eye opening – and there is still more you can dig into, which we’ll look at a little later.

Quantitative data

The challenge with the above is that while it can give you significant qualitative insight the problem is gauging just how much of your audience shares that same interest.

It’s all well and good creating content to suit individuals, but you may be wide of the mark if you don’t have a fuller view of shared interests.

Thankfully, however, there is a way to get just that.

I’ve written previously here about
extracting social data for use in informing strategy and we can use that same principle here to add the richness we need to give us the confidence necessary to make real decisions.

The science bit

Grabbing that data is easier than you think and while it’s not perfect the result is worth the effort. Here’s how it works.

There are no fancy tools either I’m afraid, just a little bit of simple math, and to help make that process as hassle free as possible we’ve already built
this simple calculator, which should make the process as pain-free as possible.

Step one

Start by jumping onto Facebook’s Ad Centre and click ‘create ad’. You’ll then be presented with this screen. Click on any of these, but we’ll use the Page Likes option here.

Once in the console scroll down until you get to the Audience section.

Start by selecting the geography you wish to look at. You can choose to focus on a global audience by default, but for this study we have chosen the UK. On the right hand side you’ll then be able to see how many people fit the selection. For instance, here we can see that there are 36,000,000 people in the UK on Facebook.

The next step is to add the audience interest. This can be anything from an interest to a brand, so let’s start with our example entertainment brand (BRAND A). The right-hand column now tells us that there are 96,000 people in the UK that ‘Like’ them.

The next step is to start to understand a little more about those interests we saw earlier using Graph Search.

Remember the pole dancing ‘interest’. Who couldn’t? The question is, just how many of those who already show an affinity with the brand ‘Like’ this alternative entertainment activity when compared to the average person?


To do that we simply add pole dancing to our brand audience as you can see below and it gives us the combined audience of 126,000 people.

OK so far? Now comes the math part – and this is where the calculator can come in useful.

Below we can see the formula that sits behind the calculator and this will give us a better understanding of just how much our audience likes pole dancing.

Taking the numbers we have just talked through we can now create a sum that looks a little like this and it tells us that 6.25% of the brand’s audience likes pole dancing.

feels like a high percentage but, feeling is not good enough. We need to know for certain and to truly understand what that means we need to look at the average person and then compare the two.

To do that we work through the same process by first getting the number for the UK Facebook audience and the pole dancing audience separately.

And we can then use this simple formula to work out what percentage of the average Facebook audience likes pole dancing.

The result here is that just 0.1% of the average audience likes this particular interest. The brand audience just got very interesting, as there is a huge over-indexing of this particular interest. We know, therefore, that content around this subject matter will resonate!

The idea from here is to then rinse and repeat this process for multiple interests so you can chart them against each other like this:

This is where we really start to understand our audience. The pink column represents the example brand’s audience and the blue the average Facebook audience. We can clearly see where the over indexing is.

Those are the interests you want to really concentrate on as part of your content plan, as you know there is a high propensity to engage.

Competition ideas

Graph Search can also be used to refine those content ideas. Let’s say, for instance, that you are running a competition to win restaurant vouchers. Rather than generically doing the same thing for everyone in the UK why not look to see if there is a North/South, or state divide?

Below we have used the same process as previously described but also looked at how location affects affinity with different restaurant brands.

The data above suggests there is a definite North/South divide and the marketer would be better offering McDonalds or Nandos vouchers to those in Manchester and Frankie and Benny chain coupons to those in London for better engagement.

2. Influencer discovery

Another area that Graph Search can really help marketers with is in finding key influencers and evangelists – both of which are critical to today’s marketing plans.

There are several ways you can do this.

To begin with let’s take a look at our example brand once more and start uncovering those people with the most potential reach. We are able to use a couple of more advanced Graph Search operators to see whose attention they may already have.

A great place to start is by looking at the blogosphere in a little more detail, and that starts with this search:

We can immediately see here that there are a number of people that run blogs and already like the brand; a brilliant conversation opener when you reach out to them.

And if you struggle to find bloggers who do follow the brand then why not utilize this search instead to see if they follow, or are friends with, people who work for the brand?

We can see here which other options are available to us in this section of Facebook’s data set if we want to refine our search. It doesn’t end here, either, as there are many more nuances available:

And if larger sites are your ultimate targets then a search for journalists is also possible, as below:

While you can also refine by location if your campaign needs to target a country, region or city:

Should you wish to refine that search further still you can target specific publications. Our search here for The Telegraph newspaper in the UK brings up more than 1,000 people, many of whom are key journalists:

And we can then use the internal search tool to refine further by job title:

By using it to find “writers,” it creates this search, and we have a list of 12 people to then connect with via LinkedIn or via a call to the newspaper’s news desk.

3. Influencer research

The value that Graph Search can bring to your marketing plan doesn’t end there either.

Using it to paint your audience understanding picture and discovering influencers in your network is obviously very powerful in itself but you can also use the platform to add further colour to your influencer pitches.

As journalists are pitched to every hour of the day, warming that conversation with some prior intelligence can make the difference between success and failure.

It’s possible to find out a lot about individuals with this same process and give you a much better understanding of the journalist you want to work with as a result.

Take this search for example. Let’s say we want to find out more about the Assistant Editor at The Telegraph magazine, discovered with this little search:

We can then use many of the searches previously mentioned to build a really good picture of his interests, and what makes him tick.

Knowing that he clearly supports Tottenham Hotspur and that he will be excited about the new Inbetweeners movie coming out gives you a brilliant “in” and conversation warmer.

And we can even see where he likes to spend time. Below you can see one of many searches that use Bing’s map functionality to bring extra location-based insight. It gives us the places the Assistant Editor has visited:

It’s this area of Graph Search that could offer the most promise in the long term, and I would be worried if I ran Tripadvisor or any other review-based site, on this evidence.

While review sites are generally based on the general public view Facebook is able to slice and dice to give you the views of anyone from your friends or family to those with similar interests or ages to you—trusted reviews from people you know and or respect.

4. GSO – Graph Search optimization

If Graph Search is going to become more useful then the likelihood is that people will start using it more and that means one thing; the birth of Graph Search optimization, or GSO.

Being at the top of those lists could drive significant traffic to brand pages and understanding how Facebook orders that will be key.

There is very little information on this area at present but logic suggests that the same signals used to order Facebook’s activity wall would apply to Graph Search.

This may mean that Likes and the new links with extra weight given to Likes from people and pages with the largest audiences. The more authoritative the person or brand behind the connection the greater the impact on Graph Search rankings.

If you then throw page actions into the mix, such as content likes, shares and comments as well as app usage and so on, you soon build up a clear picture of how GSO may work.

Could this mean that the digital PR of the future includes work to incentivize key influencers to Like Facebook Pages, or engage with key content? Only time will tell.

And given the fact that the Menlo Park company is already testing a search facility for content, this area will undoubtedly become more important still.

5. Advertising

The value of the insight also extends to social advertising. Given that CPCs are often higher in important commercial niches, it can really pay to understand where else your audience may be interacting.

If, for instance, those you want to target are also very likely to have an affinity with gardening, then creating ad groups to test your advertising in that niche can often result in a reduction in CPCs.

Not all interest sets will work here and in practice it is a good idea to create six or more campaigns targeting the top shared interests and then run them for a day or so to see how they perform.


Graph Search is here to stay and will increasingly become a major weapon in any marketers’ armory as we all look for ways of making our strategies and campaigns smarter and more effective.

The audience insight it gives and ability to drill down into the minutia is what those investing in Facebook see as its real value, and they’ve clearly only just started opening the treasure chest.

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