Internet Content Marketing Strategy: Aligning Context & Intent

When defining a Content Strategy for Internet Marketing you need to align Context with User Intent.

A frequent problem I encounter when analyzing a website’s content marketing strategy is that the marketing team is creating content without fully thinking about Context and Intent.

To understand Content Context in its simplest form, let us take a basic one word search query like “apple.”

This search term can be used in two different contexts. 1) apple – the fruit 2) Apple – the company.

We know that search engines like Google understand Content Context because they have Contextual Advertising Networks like Google AdSense that successfully place fruit advertisements on pages dealing with fruit, and laptop advertisements on pages dealing with computers.  It would not be effective to advertise Macintosh Laptop Peripherals on a page with an apple pie recipe. Context is important.

Contextual factors also strongly influence the interpretation of a search query and the results that are returned. Contextual search is a form of optimizing Internet search results based on context provided by the user(User Context). Contextual search attempts to increase the precision of search results based on how valuable they are to individual users. Contextualized search coincides with the increasing popularity of using mobile phones when searching, and a user’s search history. The easiest user context to understand is a user’s current location, and how that impacts search results. Think “Local Search.”

That is User Context. Context also includes Content Context. You can think of Content Context as a component of natural language understanding. Search queries like “movie times” and “movie reviews” can be related to a particular topic “movies”. These can be grouped into what are referred to as Context Clusters. This how search engines like Google can return web pages for a search result even though the exact search term does not appear on the page. They understand the context of what is being searched for.

Content Context Clusters can be explained by going back to the example of searching for the word “apple”. A page with words like apple macbook, apple watch, and apple airpods on it probably is not discussing apple the fruit and is more likely discussing Apple the company, and Apple’s products.

When a search engine like Google offers a user search suggestions as they begin typing a query, one or more context clusters may be presented to the user based on a context cluster probability. The context cluster probability is indicative of a probability that at least one query input that belongs to the context cluster will be selected by the user. A list of queries grouped into the context cluster may be presented as options for a query input selection.

As mentioned earlier, a user’s search history can also effect search context and that takes us into the topic of User Intent.

So far we have discussed Context(both User Context, and Content Context). Next, we need to take a look at Intent.

Search Intent seeks to understand the reason why people conduct a specific search. Why are they searching? Are they searching because they have a question and want an answer to that question? Are they searching for a specific website? Are they searching because they want to buy something or read reviews before buying it?

With the Google Hummingbird and Google RankBrain algorithms, the Google search engine can interpret search intent and display results that meet the user’s search intent.

3 Types Of Search Intent

User Search Intent can be categorized in 3 broad areas.

Informational Intent: To know something. The user wants to answer a specific question. These queries will include “how to”, “what is”, “where is”, and “why do”.  Content in this category includes tutorials, and introduction articles.

Navigational Intent: To find something. The user wants to find a specific website or location. Examples of these queries are “closest gas station”, or “facebook.”  Google Local Search is a great example of how Google is presenting content based on a user’s search intent.

Transactional Intent: To buy something. The user wants to purchase a product. Most keywords that have high commercial intent will fall into this category. Look for keywords like ‘buy’, ‘online store’, and ‘shipping’.

You could also have a 4th category called Commercial Investigation. This is the intent to research something prior to purchasing it. The user may want to purchase a product in the future, but wants to research the product first by reading product reviews or learn more about how a product works. I still lump this into Transactional Intent for content creation strategies as I view it as part of the Consumer Funnel. As users move closer to the actual act of buying, their searches become more precise(less ambiguous) and are easier to optimize content around.  These queries include queries with the words “best”, “review”, “top 10”, etc.

How to Optimize Content for Context and Intent

Knowing a bit about Context and Intent can help guide your Content Marketing Strategies. First, try to plan your content creation to satisfy specific user intentions driving their search queries. Understand related keywords and include them in the content so that search engines can determine the context of the content. If the content is on a narrow subject that could be ambiguous or may be searched for using ambiguous search terms, include a broader background of the subject within the article’s content that will help establish the context of the information and include meaningful related terms or phrases within the content that are less ambiguous.

Building upon the earlier example of “apple”, if you are creating an article on apple – the fruit you would include related terms like orchard, tree, and popular varities of apples like Cortland, Fuji, McIntosh. I realize this is an oversimplified example, but hopefully you get the point. I have found that including related words and terms not only helps with context, but helps with SEO.

Knowing about User Intent can further refine your content by dialing in the content to be about common search patterns like “How To Make Apple Pie”, “Where Is The Nearest Apple Store”, “How To Configure Apple TV”, and “Apple iPhone Review.”

When creating content for a “how to” query, structure the content so the H1 tag contains the query, and then put each step of the process in a H2 tag.

Aligning Content Context and Intent is part science, and part art. With time and practice you will get better at it, and naturally start thinking about content creation and content marketing strategies in a way that is optimized to perform better.

Cloud Computing: AWS vs. Google vs. Red Hat

A high level look at the cloud computing services and solutions offered by Amazon, Google, and Red Hat.

For the purposes of this article, I want to map out what I feel are the 3 best Cloud Computing providers: Amazon AWS, Google Cloud, and Red Hat.

Cloud Computing is a broad topic but can be categorized in to 3 main areas: Public Clouds, Private Clouds, and Hybrid Clouds.

Public Cloud computing gets most of the attention, and Amazon Web Services(AWS) has the most market share in this space. Amazon had first mover advantage and AWS has the lead both in terms of market share, but also in the number of Public Cloud services available.

In terms of reported market share based on revenue, Microsoft has the second most with their Azure cloud services, but many feel that is misleading since a large portion of what Microsoft includes in their “commercial cloud business” includes not only Azure, but also software-as-a-service(Saas) solutions like Office 365, Dynamics 365, and other segments of the Productivity and Business Processes Division.  If a company is a “Microsoft Shop” and has invested heavily in Microsoft’s technology then Microsoft Azure is the obvious choice… so I am not including it in this comparison.  They same can be said for cloud services from other companies like Oracle, and IBM. If you are invested heavily in their technology, then their cloud solutions may be the best fit for your organization.

According to the State of the Cloud report by Rightscale, Private Cloud and Hybrid Cloud strategies continue to grow. According to the report enterprises that combine public and private clouds grew to 58 percent in 2019 from 51 percent in 2018. Companies run a majority of workloads in cloud, according to Rightscale. Respondents overall run 38 percent of workloads in Public Cloud and 41 percent in Private Cloud. Respondents to the survey are already running applications in a combination of 3.4 public and private clouds and experimenting with 1.5 more for a total of 4.9 clouds. Among those using any public cloud, respondents are currently using 2.0 public clouds and experimenting with 1.8 more. Among those using any private cloud, respondents are currently using 2.7 private clouds and experimenting with 2.0 more.

In my opinion, Red Hat has the most viable solutions for Private Cloud, and Hybrid Cloud and so I am including Red Hat in this comparison.

AWS vs. Google vs. Red Hat Cloud Services Map

A quick way to understand what the various cloud services providers offer is to map similar functionality in a table. The following table provides a high-level mapping of the services provided by AWS, Google, and Red Hat platforms.

Hybrid Cloud & Private Cloud Computing

In cloud computing, hybrid cloud refers to the use of both on-premises resources in addition to public cloud resources. A hybrid cloud enables an organization to migrate applications and data to the cloud, extend their datacenter capacity, utilize new cloud-native capabilities, move applications closer to customers, and create a backup and disaster recovery solution with cost-effective high availability.

Private cloud refers to a model of cloud computing where IT services are provisioned over private IT infrastructure for the dedicated use of a single organization. A private cloud is usually managed via internal resources. Private Cloud and Virtual Private Cloud (VPC) are often used interchangeably. Technically speaking, a VPC is a private cloud using a third-party cloud provider’s infrastructure, while a private cloud is implemented over internal infrastructure.

Amazon and Google are now both investing heavily in Hybrid and Private Cloud solutions, but these solutions are afterthoughts and are not elegantly architected into their core platform. Amazon Outposts is a fully managed service from AWS where customers get AWS configured hardware and software delivered to their on-premise data center or co-location space to run applications in a cloud-native manner without having to run it at AWS data centers. Google Anthos lets you build and manage modern hybrid applications on existing on-premises investments or in the public cloud. Built on open source technologies pioneered by Google—including Kubernetes, Istio, and Knative—Anthos enables consistency between on-premises and cloud environments, but not nearly as consistent as Red Hat’s solutions.

Red Hat has the broadest supported solution with the OpenStack Platform. OpenStack is a set of free and open source software tools for building and managing cloud computing platforms for public and private clouds. Backed by some of the biggest companies in software development and hosting, as well as thousands of individual community members, many think that OpenStack is the future of cloud computing. OpenStack is managed by the OpenStack Foundation, a non-profit that oversees both development and community-building around the project.  OpenStack allows companies to utilize their existing investment in hardware to provision hybrid or private cloud solutions.  With AWS and Google, users are locked into those company’s ecosystem. Unlike AWS or Google’s solutions, OpenStack allows companies numerous options for which Cloud Provider to use.

And The Winner Is…

Like most things with technology, there is not a clear winner with Cloud Computing. It all depends on what problem you are trying to solve, and what your strategy is.

I am an advocate of Open Source solutions, and try to avoid vendor lock-in and proprietary solutions when possible.  The battle for Cloud Computing is actually more about API’s… and using those API’s ultimately means utilizing proprietary interfaces and microservices… which leads to some degree of vendor lock-in.  I will most likely write more about that in the future.

That being said, I think there are some clear leaders in various segments. For Infrastructure as a Service(IaaS) Google and Amazon have the largest most robust global assets and network. Amazon has the most global data centers but I lean toward Google’s data centers and their private global fiber network. Both Amazon and Google are best in class for infrastructure if you are just looking to spin up virtual machines and run servers in the cloud.

Moving up a level of abstraction gets us into the Platform as a Service(PaaS) space.  The goal of these solutions is to lower the barrier of entry and make developing applications possible to the the largest possible community of developers, researchers, and businesses.  The focus is on the developer for these solutions, reducing the need for skills in system administrators, ops, and security. These solutions scale your application by automatically starting new machine instances and deploying your application to the new instance. All your instances are automatically running behind a load balancer.

The PaaS solutions provide excellent integration with other services using simple API calls. It can be argued that the race for market dominance among cloud providers is actually all about which vendor has the best and broadest API’s.  Google has an advantage in this space with simple API’s to services including Google Accounts for login, Gmail, Google Maps, and more. Additionally, Google also has a broad array of Artificial Intelligence and Machine Learning services although AI as a service is available from many cloud providers now.

Moving up yet another layer of abstraction takes us to the Functions as a Service(FaaS) or serverless space. FaaS is the concept of serverless computing via serverless architectures. Software developers can leverage this to deploy an individual “function”, action, or piece of business logic. They are expected to start within milliseconds and process individual requests and then the process ends.

Just like PaaS, FaaS provides the ability to easily deploy an application and scale it, without having to provision or configure servers.  Function based apps can be used to replace microservice style architectures and background type services.  Serverless computing allows businesses to run compute-intensive functions on-demand with the near-unlimited scale of the cloud providers, and pay only for the time that code is actually running.

A lot of innovation is still going on in serverless computing and things are rapidly improving and changing.  Amazon uses AWS Lambda, Google uses Cloud Functions, and RedHat appears to be coalescing around Apache OpenWhisk.  A dominate solution or platform has not emerged yet.

Not every language is available on every platform for writing functions. JavaScript (Node.js) is really the only universally supported language and is the most commonly used in examples within documentation across all providers.

In terms of maturity, efficiency, language support, and ecosystem integration, AWS Lambda has the lead. However, if a Private or Hybrid Cloud and you desire Open Source solutions, then Apache OpenWhisk is the leader.

If your organization needs a Private Cloud, or a Hybrid Cloud and wants to avoid vendor locking I think their is a very clear winner. Red Hat uses Open Stack as their native platform for cloud services, and also uses it for their hybrid and private cloud solutions. Additionally, because OpenStack is open source software, you can run it on Amazon AWS and on Google Cloud.  The same applies for OpenShift, and OpenWhisk. The ability to choose from numerous cloud vendors along with having the same solution for private and hybrid cloud scenarios makes OpenStack, OpenShift, and OpenWhisk the ideal technologies to build your cloud computing solutions on if you are considering a Hybrid Cloud or Private Cloud.

More than 50 percent of Facebook users are fake according to a report

“Facebook has been lying to the public about the scale of its problem with fake accounts, which likely exceed 50% of its network. Its official metrics—many of which it has stopped reporting quarterly—are self-contradictory and even farcical. The company has lost control of its own product.”

It is widely accepted that a large portion of accounts on Facebook are fake, and our position is that a large potion of advertisement budget spent on Facebook is worthless because it gets consumed by click-fraud farms. But the problem may be bigger than we thought. Following Facebook’s horrible 2018 year, a report released on Thursday, Jan. 24, 2019 suggests as many as half of Facebook’s two billion accounts are fake or duplicates.

According to Aaron Greenspan, the platform’s measurement of its user base isn’t—and will never be—precise enough. Therefore, the metrics they report are overestimated.

The report also comes with a disclosure that reads: “Aaron Greenspan owns FB put options in his personal capacity. He entered into a confidential settlement with Mark Zuckerberg and Facebook, Inc. in 2009.”

The fake accounts are particularly dangerous because they “often defraud other users on Facebook, through scams, fake news, extortion, and other forms of deception” and often “involve governments,” he writes.

He further accused the company of “selling” ads to “hundreds of millions of phantom buyers – users who do not actually exist.”

How can any social media company verify a “real identity” from a fake identity? The only way to do so is to institute a process much like opening an online bank account, a process that requires some form of deeper identification.

If you use Facebook at all, you have most likely received a friend request from someone you don’t know. If you research that user account, chances are you will find it is an account that has few if any photos posted, few posts, and very little engagement on any content on that user’s wall. It is a fake account. Fake accounts are big business, and Facebook is not doing enough to stop the proliferation of fake accounts on its platform.

In future blog posts I will dive into specific examples of why advertising on Facebook can be a complete waste of money, including an example of promoting a video on Facebook that generated over 40,000 views but did not result in a single phone call. This led to a very interesting discovery of how Facebook counts “views”, resulting in view counts that are exponentially inflated.

I believe Facebook can not be fixed, at least not without new senior management in place. A new social media platform is needed that demands arduous verification and constantly monitors its user base to eliminate cloned and fake accounts. The number of “real” accounts in such a network would be a fraction of what Facebook reports its user base is, but users could actually trust that new platform. Advertisers would be able to trust that a large percentage of their advertising dollars are being consumed by click-fraud farms.

The Rise Of Online Privacy

After a never-ending erosion of online privacy via companies tracking everything users do online, the trend seems to be reversing direction and users are being given the tools they need to take back some control over how their online activities are tracked.

If you hate searching for a product on one site and then seeing ads for that product on every other site you visit, you are not alone. A few leading technology companies have learned that protecting their customers is good for business, and help is on the way. Web browsers like Safari, Firefox, and Brave are now blocking this type of tracking by default.

Companies that profit by harvesting user’s online profiles are upset about these changes, claiming it’s “bad for the ad-supported online content and services consumers love.” These companies are also upset at the rapid rise of Ad Blockers in web browsers. On several websites I operate, over 50% of users are visiting the website with browsers that use Ad Blockers.

Two ways companies are giving control of privacy back to users is through blocking cross-site tracking, and fighting browser fingerprinting.

What Is Cross-Site Tracking?

Cross-site tracking refers to companies collecting a user’s browsing data across multiple websites. When you browse from site to site, you’re often followed by tracking mechanisms that collect data on what websites you have visited, what pages you visited, what searches you performed, etc.

What Is Browser Fingerprinting?

Browser Fingerprinting is an incredibly accurate method of identifying unique browsers and tracking their online activity even when cookies are disabled. Browser Fingerprinting works by building a unique “signature” of your device based on identification points like IP Adress, Web Browser, Screen Resolution, Plugins enabled, etc. This type of fingerprinting works even if you clear your cookies.

Behind the scene, the companies that are tracking your online activity are building in-depth profiles of who you are, and what your interests are… including some very private information. On the surface this may seem benign or beneficial to users. However, Facebook’s repeated privacy scandals have revealed how companies can exploit this user information.

How Users Can Take Back Some Control?

Apple has taken a leadership position in protecting user privacy. Apple has implemented new features in Safari called “Intelligent Tracking Prevention”.

Apple’s Intelligent Tracking Prevention works to prevent cross-site tracking. Safari will prevent tracking mechanisms from companies like Facebook, Google, and advertising networks from loading on websites until you explicitly click them. If you want to use Facebook features on a website, you’ll get a prompt asking if you want Facebook.com to access your cookies and website data. If you don’t allow that access, Facebook won’t be able to track your browsing activities online, even if you’re signed into Facebook while browsing.

Safari’s new restrictions will make it much more difficult for websites to uniquely identify you using Browser Fingerprinting.

Starting with version 65 of Firefox blocking Cross-Site Tracking and Browser Fingerprinting is enabled by default.

And of course there is my favorite web browser Brave. Brave is an open source web browser that blocks advertisements, tracking pixels, and tracking cookies by default.

It is good that a cross-platform browsers like Firefox and Brave implement these features, because it is unlikely that Google Chrome is going to do anything that potentially hurts ad revenue any time soon. However, even Google Chrome has the ability to send a “Do Not Track” request if you go into its Advanced Privacy & Security Settings and you can install ad-blocking plugins for Google Chrome.

While Duck Duck Go’s Traffic Growth (an online search alternative that respects a users right to privacy) probably doesn’t concern Google much at this point, if they start losing web browser market share they may be forced to address user’s concerns over the data they are collecting and how they are using it.

Using Data Analysis To Design Better Photos

Better cameras don’t necessarily take photos that people think are better.

The visual image criteria that customers respond favorably to is an ongoing area of interest to me. I routinely use knowledge gained through A/B testing of images to achieve competitive advantage by producing images specifically designed to appeal to specific users.

In a previous article I discussed how Netflix uses image analysis to create visual content.

This data analysis reveals trends in color preferences and style for images used at Netflix.

Cameras get better each year with sensors capable of capturing more pixels, and more dynamic range. The result are images that are sharp, and have more details in shadows and highlights than ever before.

Advances in Computational Photography allow cell phone cameras to automatically take “better photos” without extra effort and knowledge of photography. Throwing processing power at raw images lets smartphones and cameras do some amazing things. Computational Photography tweaks settings between each shot ensuring that people and scenes always “look their best”.

At least that is the claim.

The problem is that better cameras don’t necessarily take photos that the majority of people prefer because they don’t take trends in color preference and style into consideration.

Marques Brownlee has a popular channel on Youtube where he reviews tech products. He created a video called “The Blind Smartphone Camera Test 2018!” which revealed that “better cameras” are not capturing and processing photos that the vast majority of people prefer to the images captured by less capable cameras.

He used 16 of the most popular mobile phones in a winner takes all bracket style competition. He included powerful phones like the Pixel 3, Hydrogen, and the iPhone X, and iPhone XS along with less capable cameras used by the Blackberry and Xiaomi Pocophone.

Most images are viewed on social media platforms now, and it is well known that Twitter and Instagram have horrible image compression, but it makes sense to use these platforms to test trends and image preferences.

The results revealed an obvious trend. People do not like the perfectly exposed high dynamic range photos that the “best cameras” captured.

There’s obviously a massive subjective component: people like brighter, warmer, punchy photos.

Computation photography and large image sensors capable of capturing copious amounts of detail and high dynamic range are still not a substitute for good glass, and an artistic understanding of photographic principals and composition.

In the end, you have to understand what your customers prefer, and craft visual strategies designed to appeal to those preferences.