As mobile technology becomes an increasingly common way for users to access the internet, you need to ensure that your mobile content (whether on a mobile website or in a mobile app) is as accessible to users as possible. In the past this process has been relatively siloed, with separate URLs for desktop and mobile content and apps tucked away in app stores.
But as app and mobile web usage continues to rise, the ways in which people access this content is beginning to converge, which means it's becoming more important to keep all of these different content locations linked up. This means that the way we think about managing our web and mobile content is evolving:
So how do we improve the interaction between these different types of content and different platforms, getting to the point of being able to have a single URL which takes the user to the most appropriate version of the content based on their personal context?
The first step is to ensure that we are correctly implementing deep linking (e.g., linking to a particular screen within an app) for apps which have comparable webpage content, to allow for our app content to rank in mobile search.
Image credit: Google Developers
Google indexation provides benefits for both Android and iOS apps. The benefits for Android apps are twofold:
  • users searching on an Android device who have not yet installed your app will see the app show up in mobile search results; and
  • Android users who do have your app installed will get query autocompletions when they use browser search which can include results from your app, as well as seeing enhanced display elements in the SERP (such as the app icon). It’s basically like rich snippets for apps.
Image credit: Google Developers
On iOS, app ranking is currently only supported for apps already installed on the device. Apple users should see search results which include links to installed apps and also include the enhanced display elements mentioned above.
In addition, Google recently announced that mobile apps which use the new App Indexing API for deep linking may receive a rankings boost in mobile web search. They are releasing a new and improved version of Google Now, "Now on Tap," in their latest OS update (Android M), which allows you to search content across your phone without navigating out of whatever app (or website) you are currently using. The catch is, that app content has to be in their index in order to be included in a "Now on Tap" search.
It’s not just Google, either; Apple is implementing their own version of a search index to allow iOS9 users to search and discover web and app content without using a third-party search engine, Bing has its own approach to app indexation and ranking, and other services aren’t far behind.
This post, however, will focus on how to setup your Android and iOS apps to appear in Google search results. While the idea of app indexation isn’t new, it is an area of rapid innovation and the process for getting your apps indexed by Google has recently been simplified. This post is therefore intended to provide a brief overview of that process and to serve as an update to the information which is currently available.

The implementation

The good news is that it’s getting simpler to add the relevant markup to your web content and get your app content indexed and ranking in mobile search results.
The basic process is only three steps:
  1. Support HTTP deep links in your mobile app. For iOS you will need to do this by setting up support for "Universal Links." "Universal Links" are what Apple calls HTTP links that have a single URL which can open both a specific page on a website and the corresponding view in an app.
    Note: At this point, you can register your app with Google, associate it with your website and stop there—as long as you are using the same URLs for your web content and your app content, they should be able to automatically crawl, index, and attempt to rank your app content based on your website’s structure. However, implementing App Indexing and explicitly mapping your web content to your app content using on-page markup can provide additional benefits and allow for a bit more control. Therefore, I recommend following the full process, if possible.
  2. Implement Google App Indexing using the App Indexing API for Android, or by integrating the App Indexing SDK for iOS 9.
  3. Explicitly map your web pages to their corresponding app screens using either a rel=alternate link element on the individual page, by referencing the app URLs in your XML sitemaps, or by using schema.org markup.
You can find a more step-by-step explanation of this process (looking at Android and iOS separately) below.

The app indexation process used to be a bit more complex, because HTTP links aren’t supported by older iOS versions. Instead, developers had to use something called "Custom URL Schemes" to link to iOS app content. This meant that you essentially had to create a unique scheme for your app URLs and then add support for these in the app code.
Custom URL schemes have a couple other downsides besides adding complexity, namely:
  • different app developers can claim the same custom URL scheme, whereas with HTTP links you can associate the app to a particular domain or set of domains; and
  • with custom URL schemes, tapping the URL when the app isn’t installed results in a broken link (because it only links to content within the app), whereas HTTP links are web links as well and can take the user to a webpage if the app isn’t installed (as long as the URL is the same for both the app view and the corresponding webpage).
While you can still use the custom URL scheme approach, the good news is that Google’s App Indexing is now compatible with HTTP deep link standards for iOS 9, which Apple calls "Universal Links."
You should still add markup to any webpages which have content corresponding to a particular app screen. Think of it like like rel=canonical or like mobile switchboard tags, but for apps. Be aware that when Google finds a link between a webpage and an app page which they think are equivalent, they will compare the two pages and you will receive a ‘Content Mismatch’ error in the Search Console if they don’t believe the content is similar enough.

Getting Android apps indexed in Google

Step 1: Support HTTP deep links in your app by adding intent filters to your manifest.

An intent filter is a way of specifying how an app responds to a particular action. Intent filters for deep links have three required elements: <action>, <category>, and <data>. You can find more guidance on this from Google Developers. Here is their example of an intent filter which enables support for HTTP deep links:
<intent-filter android:label="@string/filter_title_viewrecipes">

 <action android:name="android.intent.action.VIEW" />

 <category android:name="android.intent.category.DEFAULT" />

 <category android:name="android.intent.category.BROWSABLE" />

 <data android:scheme="http"

 android:host="recipe-app.com"

 android:pathPrefix="/recipes" />

 </intent-filter>

</activity>
Noindex option:Just like for websites, you can add noindex directives for app content as well. Include a noindex.xml file in your app to indicate which deep links should not be indexed, and then reference that file in the app’s manifest (AndroidManifest.xml) file. You can find more detail on how to create and reference the noindex.xml file here.

Step 2: Associate your app to your site in Google Search Console.

This is done in Google Search Console (you can also do it from the Developer Console). As long as your app is set up to support deep links, this step is technically all you have to do to allow Google to start indexing your app. It will allow Google to index and crawl your app automatically by attempting to figure out the app structure from your website structure.
However, if you do stop here, you will not have as much control over how Google understands your content, which is why the explicit mapping of pages to app versions is recommended. Also, if you can’t use the API for some reason, you need to make sure that Googlebot can access your content. You can check that this is configured correctly in your site’s robots.txt file by testing some of your deep links using the robots.txt tester tool in the Search Console.

Step 3: Implement app indexing using the App Indexing API.

Using the App Indexing API is definitely worthwhile; apart from anything else, apps which use the API should receive a rankings boost in mobile search results, and you don’t need to worry about Googlebot struggling to access your content.
The App Indexing API allows you to annotate information about the activities within your app that support deep links (as laid out in your intent filters). For details on how to set this up, see the Google Developers guidance.

Step 4: Test your implementation.

You can test your implementation (always on a fresh installation of your app!) with the following tools. (Find more info about how to use each of these tools here.)
Android Debug Bridge – to test deep links from the command line
Fetch as Google (Search Console) – to test what Google sees when it crawls your app deep links
You can also track search traffic to these deep links in the Search Console’s Search Analytics report.

Getting iOS apps indexed in Google

Step 1: Support HTTP deep links in your app by setting up support for "Universal Links."

To support universal links in your iOS app, you need to first ensure that your app handles these links correctly by adopting the UIApplicationDelegate methods (if it doesn’t already use this protocol). Once this is in place, you can associate your app with your domain.
You’ll do this by:
  • adding an "associated domains" entitlement file to your app’s project in XCode that lists each domain associated with your app; and
  • uploading an apple-app-site-association file to each of these domains with the content your app supports—note that the file must be hosted at the root level and on a domain that supports HTTPS.
To learn more about supporting Universal Links, view the Apple Developer guidance.

Step 2: Register your app with Google (using the GoogleAppIndexing SDK for iOS 9).

You’ll need to add the App Indexing SDK to your app using the CocoaPods dependency manager. For step by step instructions, check the Google Developers’ guide. Basically what this does is allows you to register your app with Google, just like Android apps are registered via the Search Console. This also means that Google can now read the apple-app-site-association file to understand what URLs your app can open.

Step 3: Test your implementation.

You can test whether this is set up correctly by tapping a universal link in Safari on an iOS 9 device and checking that it opens the right location in your app.

Mapping your webpages to your app with on-page markup or sitemaps

Once you’ve set up the deep linking support for your Android and/or iOS app(s), the final step is to explicitly identify the corresponding webpages to the correct app screens using one of the supported markup options. This step allows you to indicate more clearly to Google what the relationship is between a given page and its corresponding app link (both of which should already share the same URL if you are using HTTP links). Following this step also allows you to indicate the relationship to Bing crawlers, which otherwise wouldn’t see the app content, and to allow Apple to index your iOS app.
You can do this mapping either in the head of the individual page using a link element, using schema.org markup (for Android only), or in an XML sitemap.

A note on formats for app links

The format for an Android HTTP link uses the format of:
android-app://{package_name}/http/{host_path}
The {package_name} is the app’s "Application ID," which is how it is referenced in the Google Play Store. So a link to the (example) Gizmos app might look like this:
android-app://com.gizmos.android/http/gizmos.com/example
For iOS links, you use the app’s iTunes ID instead of the Package Name. So an iOS app URL uses this format:
ios-app://{itunes_id}/{scheme}/{host_path}
For HTTP links the {scheme} is "http," which would mean your URL would look like this:
ios-app://{itunes_id}/http/{host_path}

How to reference your app links

Note: Google provides guidance on the three currently supported deep link methods here.

Option 1: Link rel=alternate element

To add an app link reference to an individual page, you can use an HTML <link> element in the <head> of the page.
Here is an example of how this might look if you have both an iOS and Android app:
<html>
<head>
 ...
<link rel="alternate" href="android-app://com.gizmos.android/http/gizmos.com/example" />
<link rel="alternate" href="ios-app://123456/http/gizmos/example" /></head>
<body> … </body>

Option 2: Schema.org markup (currently supported on Android only)

Alternatively, if you have an Android app, you can use schema.org markup for the ViewAction potential action on an individual page to reference the corresponding app link.
Here is an example of how this might look:
script type="application/ld+json">
{
 "@context": "http://schema.org",
 "@type": "WebPage",
 "@id": "http://gizmos.com/example",
 "potentialAction": {
 "@type": "ViewAction",
 "target": "android-app://com.gizmos.android/http/gizmos.com/example"
 }
}
</script>

Option 3: Add your app deep links to your XML sitemap

Instead of marking up individual pages, you can use an <xhtml:link> element in your XML sitemap, inside the <url> element specifying the relevant webpage.
Here is an example of how this would look if you have both an iOS and an Android app:
<?xml version="1.0" encoding="UTF-8" ?>
http://www.sitemaps.org/schemas/sitemap/0.9"
 xmlns:xhtml="http://www.w3.org/1999/xhtml">
<url>
 http://gizmos.com/example
 <xhtml:link rel="alternate" href="ios-app://123456/http/gizmos/example" /></url>
 <xhtml:link rel="alternate" href="android-app://com.gizmos.android/http/gizmos.com/example" />
...
</urlset>

Additional information

What about apps which don’t have corresponding web pages?

Unfortunately, as of this writing, Google does not officially offer app indexation for apps which don’t have corresponding web content. However, they are trying to move in this direction, and as such are beginning to try this out with a handful of apps with “app-only” content. If you have an app with app-only content, and would like to get this content indexed, you can express interest using this form.

What about getting my app indexed in Bing?

Bing supports two open standard options for linking webpages to app links:
To learn more about how to implement these types of markup, see the guidance on the Bing blog.

Quick reference checklists

Will Critchlow recently spoke about app indexation in his presentation at Searchlove London. He provided two useful checklists for Android and iOS app indexing:
To learn more about app indexing by Google, check out Emily Grossman and Cindy Krum’s excellent post over on SearchEngineLand.

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Yesterday, news emerged that Google was using a machine-learning artificial intelligence system called “RankBrain” to help sort through its search results. Wondering how that works and fits in with Google’s overall ranking system? Here’s what we know about RankBrain.
The information covered below comes from three sources. First, the Bloomberg storythat broke the news about RankBrain yesterday (see also our write-up of it). Second, additional information that Google has now provided directly to Search Engine Land. Third, our own knowledge and best assumptions in places where Google isn’t providing answers. We’ll make clear where any of these sources are used, when deemed necessary, apart from general background information.

What Is RankBrain?

RankBrain is Google’s name for a machine-learning artificial intelligence system that’s used to help process its search results, as was reported by Bloomberg and also confirmed to us by Google.

What Is Machine Learning?

Machine learning is where a computer teaches itself how to do something, rather than being taught by humans or following detailed programming.

What Is Artificial Intelligence?

True artificial intelligence, or AI for short, is where a computer can be as smart as a human being, at least in the sense of acquiring knowledge both from being taught and from building on what it knows and making new connections.
True AI exists only in science fiction novels, of course. In practice, AI is used to refer to computer systems that are designed to learn and make connections.
How’s AI different from machine learning? In terms of RankBrain, it seems to us they’re fairly synonymous. You may hear them both used interchangeably, or you may hear machine learning used to describe the type of artificial intelligence approach being employed.

So RankBrain Is The New Way Google Ranks Search Results?

No. RankBrain is part of Google’s overall search “algorithm,” a computer program that’s used to sort through the billions of pages it knows about and find the ones deemed most relevant for particular queries.

What’s The Name Of Google’s Search Algorithm?

google-hummingbird1-ss-1920

It’s called Hummingbird, as we reported in the past. For years, the overall algorithm didn’t have a formal name. But in the middle of 2013, Google overhauled that algorithm and gave it a name, Hummingbird.

So RankBrain Is Part Of Google’s Hummingbird Search Algorithm?

That’s our understanding. Hummingbird is the overall search algorithm, just like a car has an overall engine in it. The engine itself may be made up of various parts, such as an oil filter, a fuel pump, a radiator and so on. In the same way, Hummingbird encompasses various parts, with RankBrain being one of the newest.
In particular, we know RankBrain is part of the overall Hummingbird algorithm because the Bloomberg article makes clear that RankBrain doesn’t handle all searches, as only the overall algorithm would.
Hummingbird also contains other parts with names familiar to those in the SEO space, such as PandaPenguin and Payday designed to fight spam, Pigeon designed to improve local results, Top Heavy designed to demote ad-heavy pages, Mobile Friendly designed to reward mobile-friendly pages and Pirate designed to fight copyright infringement.

I Thought The Google Algorithm Was Called “PageRank”

PageRank is part of the overall Hummingbird algorithm that covers a specific way of giving pages credit based on the links from other pages pointing at them.
PageRank is special because it’s the first name that Google ever gave to one of the parts of its ranking algorithm, way back at the time the search engine began in 1998.

What About These “Signals” That Google Uses For Ranking?

Signals are things Google uses to help determine how to rank Web pages. For example, it will read the words on a Web page, so words are a signal. If some words are in bold, that might be another signal that’s noted. The calculations used as part of PageRank give a page a PageRank score that’s used as a signal. If a page is noted as being mobile-friendly, that’s another signal that’s registered.
All these signals get processed by various parts within the Hummingbird algorithm to ultimately figure out which pages Google shows in response to various searches.

How Many Signals Are There?

Google has fairly consistently spoken of having more than 200 major ranking signalsthat are evaluated that, in turn, might have up to 10,000 variations or sub-signals. It more typically just says “hundreds” of factors, as it did in yesterday’s Bloomberg article.
If you want a more visual guide to ranking signals, see our Periodic Table Of SEO Success Factors:

Periodic Table Of SEO Success Factors 2015

It’s a pretty good guide, we think, to general things that search engines like Google use to help rank Web pages.

And RankBrain Is The Third-Most Important Signal?

That’s right. From out of nowhere, this new system has become what Google says is the third-most important factor for ranking Web pages. From the Bloomberg article:
RankBrain is one of the “hundreds” of signals that go into an algorithm that determines what results appear on a Google search page and where they are ranked, Corrado said. In the few months it has been deployed, RankBrain has become the third-most important signal contributing to the result of a search query, he said.

What Are The First And Second-Most Important Signals?

Google won’t tell us what the first and second-most important signals are. We asked. Twice.
It’s annoying and arguably a bit misleading that Google won’t explain the top two. The Bloomberg article was no accident. Google wants some PR about what it considers to be its machine-learning breakthrough.
But to really assess that breakthrough, it’s helpful to know the other most important factors that Google uses now, as well as was was knocked behind by RankBrain. That’s why Google should explain these.
By the way, my personal guess is that links remain the most important signal, the way that Google counts up those links in the form of votes. It’s also a terribly aging system, as I’ve covered in my Links: The Broken “Ballot Box” Used By Google & Bing article from the past.
As for the second-most important signal, I’d guess that would be “words,” where words would encompass everything from the words on the page to how Google’s interpreting the words people enter into the search box outside of RankBrain analysis.

What Exactly Does RankBrain Do?

From emailing with Google, I gather RankBrain is mainly used as a way to interpret the searches that people submit to find pages that might not have the exact words that were searched for.

Didn’t Google Already Have Ways To Find Pages Beyond The Exact Query Entered?

Yes, Google has found pages beyond the exact terms someone enters for a very long time. For example, years and years ago, if you’d entered something like “shoe,” Google might not have found pages that said “shoes,” because those are technically two different words. But “stemming” allowed Google to get smarter, to understand that shoes is a variation of shoe, just like “running” is a variation of “run.”
Google also got synonym smarts, so that if you searched for “sneakers,” it might understand that you also meant “running shoes.” It even gained some conceptual smarts, to understand that there are pages about “Apple” the technology company versus “apple” the fruit.

What About The Knowledge Graph?

The Knowledge Graph, launched in 2012, was a way that Google grew even smarter about connections between words. More important, that it learned how to search for “things not strings,” as Google has described it.
Strings means searching just for strings of letters, such as pages that match the spelling of “Obama.” Things means that instead, Google understands when someone searches for “Obama,” they probably mean US President Barack Obama, an actual person with connections to other people, places and things.
The Knowledge Graph is a database of facts about things in the world and the relationships between them. It’s why you can do a search like “when was the wife of obama born” and get an answer about Michele Obama as below, without ever using her name:
obama wife

How’s RankBrain Helping Refine Queries?

The methods Google already uses to refine queries generally all flow back to some human being somewhere doing work, either having created stemming lists or synonym lists or making database connections between things. Sure, there’s some automation involved. But largely, it depends on human work.
The problem is that Google processes three billion searches per day. In 2007, Google said that 20 percent to 25 percent of those queries had never been seen before. In 2013, it brought that number down to 15 percent, which was used again in yesterday’s Bloomberg article and which Google reconfirmed to us. But 15 percent of three billion is still a huge number of queries never entered by any human searcher — 450 million per day.
Among those can be complex, multi-word queries, also called “long-tail” queries. RankBrain is designed to help better interpret those queries and effectively translate them, behind the scenes in a way, to find the best pages for the searcher.
As Google told us, it can see patterns between seemingly unconnected complex searches to understand how they’re actually similar to each other. This learning, in turn, allows it to better understand future complex searches and whether they’re related to particular topics. Most important, from what Google told us, it can then associate these groups of searches with results that it thinks searchers will like the most.
Google didn’t provide examples of groups of searches or give details on how RankBrain guesses at what are the best pages. But the latter is probably because if it can translate an ambiguous search into something more specific, it can then bring back better answers.

How About An Example?

While Google didn’t give groups of searches, the Bloomberg article did have a single example of a search where RankBrain is supposedly helping. Here it is:
What’s the title of the consumer at the highest level of a food chain
To a layperson like myself, “consumer” sounds like a reference to someone who buys something. However, it’s also a scientific term for something that consumes food. There are also levels of consumers in a food chain. That consumer at the highest level? The title — the name — is “predator.”
Entering that query into Google provides good answers, even though the query itself sounds pretty odd:
food chain consumer
Now consider how similar the results are for a search like “top level of the food chain,” as shown below:
top_level_of_the_food_chain_-_Google_Search
Imagine that RankBrain is connecting that original long and complicated query to this much shorter one, which is probably more commonly done. It understands that they are very similar. As a result, Google can leverage all it knows about getting answers for the more common query to help improve what it provides for the uncommon one.
Let me stress that I don’t know that RankBrain is connecting these two searches. I only know that Google gave the first example. This is simply an illustration of how RankBrain my be used to connect an uncommon search to a common one as a way of improving things.

Can Bing Do This, Too, With RankNet?

Back in 2005, Microsoft starting using its own machine-learning system, called RankNet, as part of what became its Bing search engine of today. In fact, the chief researcher and creator of RankNet was recently honored. But over the years, Microsoft has barely talked about RankNet.
You can bet that will likely change. It’s also interesting that when I put the search above into Bing, given as an example of how great Google’s RankBrain is, Bing gave me good results, including one listing that Google also returned:
What’s_the_title_of_the_consumer_at_the_highest_level_of_a_food_chain_-_Bing
One query doesn’t mean that Bing’s RankNet is as good as Google’s RankBrain or vice versa. Unfortunately, it’s really difficult to come up with a list to do this type of comparison.

Any More Examples?

Google did give us one fresh example: “How many tablespoons in a cup?” Google said that RankBrain favored different results in Australia versus the United States for that query because the measurements in each country are different, despite the similar names.
I tried to test this by searching at Google.com versus Google Australia. I didn’t see much difference, myself. Even without RankBrain, the results would often be different in this way just because of the “old fashioned” means of favoring pages from known Australian sites for those searchers using Google Australia.

Does RankBrain Really Help?

Despite my two examples above being less than compelling as testimony to the greatness of RankBrain, I really do believe that it probably is making a big impact, as Google is claiming. The company is fairly conservative with what goes into its ranking algorithm. It does small tests all the time. But it only launches big changes when it has a great degree of confidence.
Integrating RankBrain, to the degree that it’s supposedly the third-most important signal, is a huge change. It’s not one that I think Google would do unless it really believed it was helping.

When Did RankBrain Start?

Google told us that there was a gradual rollout of RankBrain in early 2015 and that it’s been fully live and global for a few months now.

What Queries Are Impacted?

Google told Bloomberg that a “very large fraction” of queries are being processed by RankBrain. We asked for a more specific figure but were given the same large fraction statement.

Is RankBrain Always Learning?

All learning that RankBrain does is offline, Google told us. It’s given batches of historical searches and learns to make predictions from these.
Those predictions are tested and if proven good, then the latest version of RankBrain goes live. Then the learn-offline-and-test cycle is repeated.

Does RankBrain Do More Than Query Refinement?

Typically, how a query is refined — be it through stemming, synonyms or now RankBrain — has not been considered a ranking factor or signal.
Signals are typically factors that are tied to content, such as the words on a page, the links pointing at a page, whether a page is on a secure server and so on. They can also be tied to a user, such as where a searcher is located or their search and browsing history.
So when Google talks about RankBrain as the third-most important signal, does it really mean as a ranking signal? Yes. Google reconfirmed to us that there is a component where RankBrain is directly contributing somehow to whether a page ranks.
How exactly? Is there some type of “RankBrain score” that might assess quality? Perhaps, but it seems much more likely that RankBrain is somehow helping Google better classify pages based on the content they contain. RankBrain might be able to better summarize what a page is about than Google’s existing systems have done.
Or not. Google isn’t saying anything other than there’s a ranking component involved.

How Do I Learn More About RankBrain?

Google told us people who want to learn about word “vectors” — the way words and phrases can be mathematically connected — should check out this blog post, which talks about how the system (which wasn’t named RankBrain in the post) learned the concept of capital cities of countries just by scanning news articles:
image00
There’s a longer research paper this is based on here. You can even play with your own machine learning project using Google’s word2vec tool. In addition, Google has anentire area with its AI and machine learning papers, as does Microsoft.

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Many B2B marketers — particularly those selling to enterprise companies and targeting specific job titles — struggle with the task of generating SEO traffic that actually reaches their target audience.
There’s often a perception that these types of purchases (big ticket items bought by large companies) aren’t something that key stakeholders are likely to search for. The thought is that they buy based on experience with the product, recommendations or familiarity with the brand — not based upon a transactional search result.
This is complicated even further by a few additional factors that can be obstacles in generating the right kind of search traffic for your B2B business:
  • Limited Relevant Search Volume. Frequently, if you sell a very specific product that solves a very specific problem, you won’t have massive numbers of folks searching for that item and may struggle to grow traffic. If I’m selling human resources software and targeting mid-to-large companies, variations of “HR software” and “human resources software” may send some really solid early-stage leads, but “niching down” to specific features of my product and specific challenges my prospects face around my product may start to offer very low search volume and diminishing returns.
  • High Competition. In addition to having a somewhat limited universe of possible searches to target, there’s also the challenge that SEO is a more mature industry, and many of my competitors will now at least be “checking the box” and updating title tags, trying to rank for some of the most obvious terms searchers will use to describe their product (which, again, may also be the most fruitful in terms of combined relevance and available search volume).
  • Evolving Search Results. Once upon a time, you could create a sales page about how great you were, load up on lower-quality off-site links to that page and rank really well in search results. Google is rewarding different types of listings these days, however, and the page you’d like to see ranking for your desired term may be tough to push high in search results (and the content you can actually get to rank may not convert as well as you’d like and may raise questions with your CEO as to why you have a page about human resources software that doesn’t talk about how great your human resources software is).
So how do you avoid a situation where you’re spending time and effort chasing high-competition, low-return rankings that won’t yield enough traffic, leads and sales to make your efforts worthwhile?
Let’s look at some specific strategies and tactics B2B companies can leverage to generate relevant search traffic (and great leads).

Think People First, Not Keywords

Like any marketing effort, you want to start with the question:
Who is buying my product?
This exercise is about identifying the person who you’d like to buy your software, not just the terms you think people will use to describe your product. Ideally, you’ve already spent time as a company and a marketing department thinking about this question.
For my fictional HR software company, my target will be HR professionals (say director level) in midsized to large companies.
I want to get these people to my website. Business buyers are still people, and people search for things, even if they’re not searching in the volume I’d like for the most intuitive way to describe my product. I need to start asking questions about these people who are buying software like mine and ask myself:
  • What problems do these people consistently wrestle with?
  • What content do they consume on other sites?
  • How can I solve those problems, and create that kind of content on my site?
The answers to these questions will unlock a ton of content ideas for topics that are highly relevant to your target audience. Frequently, these topics will also represent keywords and search terms that are significantly less competitive and easier to rank for in search results (since they’re less obvious and less likely to be targeted by your competitors). There are a number of great ways to get this information, including:

1. Talk To The People You’re Targeting

Novel idea, right? Talk to your customers and prospects (one-on-one or via survey) and find out, specifically: where are their biggest challenges, what do they spend the most time on on a day-to-day basis, what sites do they read?
An extension of this is to regularly meet with the sales and services folks at your company to learn what problems customers and prospects most often have, common objections they face and the language customers and prospects are using to describe different issues and feature requests.

2. Look At Conference Agendas

My company creates and promotes content on behalf of businesses. Sometimes this means doing content ideation in a niche we’re unfamiliar with. A great early step at fleshing out content ideas is to look at conference agendas.
Organizers here have a strong financial incentive to focus tracks and presentations around topics that are interesting to attendees. To help identify opportunities for my HR software company, I’d look at the agendas for events that HR professionals would be likely to attend:
Using conference agendas for B2B keyword research
In this example screenshot from the EBN Benefits Forum & Expo agenda, I can quickly spot some interesting potential content topics such as:
  • Private Exchange (I could take angles like tips about implementing this, pros and cons and so on)
  • Individual Health Insurance
  • Private Exchanges vs. Self-Managed Plans
  • Health Insurer Consolidation
This was just the first agenda I looked at from the first conference; as I study a number of different conferences, I’ll start to see some common issues and different combinations of topics I can attack in different content assets here.

3. Forums, Support Content And Q&A Sites

I probably have some of my own support and forum content on my own site. This could be an unmined trove of great content ideas. What are my users asking frequently here? What are popular feature requests?
Even if I can’t build these for my customers immediately, detailing a great way to do this manually/outside my software could be a really popular content asset (and will probably map to a search term my prospects are looking for — if a known segment of your target market is struggling with an issue, it’s virtually guaranteed a bigger slice of folks have the same issue).
You can also use the same approach to look at your competitors’ forums and support content. If they’re featuring a specific support question on the main page of their support section, that’s probably because it’s a common issue their users (who presumably are either my direct prospects or have very similar issues to my prospects) have.
You can think similarly for hot topics on their forums, feature requests from their users and more. If Zenefits is a direct competitor for my HR software company, I can see at a glance in their help section how they categorize common questions and issues:
B2B competitor keyword analysis through analyzing support content.
Beyond that, I can plug that subdomain into a tool like SEMrush to see what search terms specifically are driving traffic to their help subdomain:
Example of using SEM Rush for competitive keyword research for B2B companies
Here there’s a treasure trove of possible content topics I know my prospects are likely to be interested in. As I dig into multiple competitors’ support sections, I’ll once again start to see common themes in topics being focused on and questions that frequently come up.

4. Content Your Customers And Prospects Are Consuming

What sites do your prospects read frequently? As with conference organizers, publishers have a strong vested interest in writing about topics that are interesting to their audience, so if you can identify the publications your prospects are reading, you can run those sites through tools like SEMrush (to see the terms that drive the most traffic to those sites), as well as tools like BuzzSumo (to see the content that gets shared most frequently).
This can often yield better ideas for keywords and content topics than traditional competitive keyword research, because your competitors are probably looking at other competitors and maintaining a narrow focus around obvious terms.

5. Tools Your Prospects Are Using

In addition to your product, what other tools are your prospects using? You probably haven’t solved every single tech problem your prospect has, so what other tools are they using?
These would be the types of companies you might target for partnership or co-marketing opportunities. This information can yield great content ideas in the way of:
  • A “best free tools for HR {directors, pros, etc.}” list.
  • A “best {category of tools}” list where you — as a neutral party since you don’t have an offering in this space — compare and contrast the different options your prospect may be considering.
  • A guide to buying {category of tools}, where you can objectively walk through some of the key considerations for your prospects as they purchase a specific type of software.
By helping your prospects identify useful tools and evaluate classes of tools that are tangential but not competitive to your offering, you can become a trusted source of information and can frequently rank well for search terms they’re looking for.
Frequently, these types of comparisons will actually outrank the individual tool companies themselves, since this is the type of content these searchers are actually looking for and will be more likely to click on, consume and share than an individual tool provider’s sales page.

“Traditional” Keywords Can Be Your Friend, Too: How To Attack Core Keywords And Get More Out Of What’s Already Working

Even if a core keyword like “HR software” is highly competitive, doesn’t have a ton of search traffic and is difficult to rank for with your product page, that doesn’t necessarily mean you have to ignore it.
In a recent post, I detailed a number of ways to dig deeper with a core SEO keyword that’s working via both paid search and organic search, such as:
  1. Testing Your PPC Ad Copy & Landing Pages
  2. Turning Searchers into Display Targets with Display Select Keywords
  3. Bidding More Aggressively on Return Visitors with RSLA Campaigns
  4. Using Similar Audiences & Remarketing for the Landing Pages Driving Traffic for Your Keyword
  5. Using Bing Ads to Get Incremental Traffic for Your Keywords
  6. Fleshing Out the Page That’s Ranking for Modified Versions of the Core Term
  7. Reconsidering the Ranking Page’s Title Tag
  8. Creating New Content Targeting Modified Terms
  9. Mine The Search Result For Advertising Opportunities
You can learn more about each tactic in the original post.
You can also focus efforts on getting more out of the content on your site that’s already working (assuming that content exists). In my post on how to squeeze more value out of your most important SEO landing pages, I walked through several ways you can capitalize on pages that are already working, namely:
  1. Determine What’s Already Ranking & Driving Traffic
  2. Run Your URL Through The Keyword Tool
  3. Get Google Suggest Data For The Primary Topic Of The Page
  4. Update Your Page’s Title Tag, Meta Description & Alt Attributes
  5. Flesh Out The Page With Additional Content
  6. Add Different Content Types If Applicable
  7. Create A Specific, Customized Offer
  8. Layer Outreach On Top Of That Page
  9. Do Some Social Promotion For The Page
  10. Link To The Page Internally With Different, Useful Anchors
A full breakdown of each step here is outlined in the initial post.

Executing Against Great Topics: Choose the Right Assets And The Right Offers

By following the process outlined above, you’ll likely have a ton of ideas for new relevant content that can drive qualified B2B SEO traffic, as well as a number of ideas for getting more value out of core SEO keywords and pages that are already driving quality SEO traffic.
The bad news is, you still have a lot of work to do.

1. Determine Priorities

First you need to triage what’s likely a large list of possible opportunities. Here you’ll want to consider the possible search volume, relevance to your prospects and the realistic likelihood you can actually rank for these terms.

2. Map Topics To Content Types

From there you need to work down the list of potential keywords and topics to map specific types of content you can create for each of your topics. There are a number of different ways to map compelling content types to targeted keywords, and the asset you use will have to do with the keywords you’re targeting. Some examples might be:
  • Core Keywords. For your core keywords, you may want to get really aggressive and just talk frankly about yourself and all of your competitors, but most companies may want to take a different approach here. You can target core terms by baking them into an expert roundup or group interview format, you can list out the relevant conferences for your topic, or you can list off a lot of tips, quotes or resources for learning more about a core keyword (e.g., “best tips for evaluating HR software,” Human Resources Conferences and so on.)
  • Hot Topics. This might be a good opportunity to round up a lot of great tips and quotes about something that you found in your research of popular sites and conference agendas or a nice open-ended question to pose to thought leaders in your niche (e.g., “What are some common misconceptions HR pros have about private exchange versus self-managed plans?”).
  • Valuable Tools And Resources. Creating a really comprehensive “best of” list that helps prospects find useful resources (blogs or conferences on topics they’re interested in) and/or compares tools can be a great way to target a lot of different terms and topics you may uncover in your research.
  • Low Competition, Specific Terms. For modified versions of your core keywords or just very specific lower competition terms, you may not need a massive resource (shorter content can win sometimes, too) — just a brief glossary-style overview of a topic could be well-positioned to rank for the term and may be precisely what searchers were looking for (Bonus points if you can get your content into the answer box).

3. Create, Promote And Get Leads from Your Content

Finally, you need to create the content, promote it and map a specific offer to your content.
Your content creation efforts should have been executed with promotion in mind (Try to make each asset as fail-proof as possible), and you should have a specific plan for who will link to and share your content (and why). If you’re not sure of how to execute on outreach and promotion, there are lots of different resources on link building and tons of great information about content promotion.
You also need an offer strategy. Having a specific informational offer or “content upgrade” mapped to popular posts can be a major help in turning the relevant traffic you’re driving to content assets into actual leads. If you’re not sure of how to execute or what types of offers to use, there are a lot of different places to find inspiration and learn the nuts and bolts of setting up a content upgrade.

Final Thoughts

Generating qualified B2B SEO traffic isn’t “easy,” and it definitely requires more in the way of research, strategy and content creation and promotion than it did as recently as a few years ago.
That said, there’s still a lot of valuable organic traffic to be had for business-to-business marketers, and the assets you now have to develop to be able to drive relevant SEO traffic also have tremendous additional value outside of driving search traffic, which likely wasn’t the case back when you were building links from forum signatures to your product pages.
Additionally, with the proper execution, that traffic will compound over time, frequently driving leads more efficiently and scalably than many other marketing channels.

So if you have the budget and resources, consider taking another look at your topic ideation, content creation and content promotion processes. You may still be able to drive valuable B2B SEO traffic after all.

Source  URL :
http://searchengineland.com/increase-qualified-b2b-seo-traffic-2016-beyond-233725
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