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2017: The Year of Machine Learning, Intelligent Content and Experiences

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2017: The Year of Machine Learning, Intelligent Content and Experiences

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It is common knowledge that the amount of information available in the digital ecosystem is exploding. By 2020 [1] it is expected to have grown from 130 exabytes to 40,000 exabytes.

Digital (and in our case search and content) data holds the keys to marketing success. It contains the critical patterns on consumer intent and behavior, preferences, and content/topics that brands need to provide customers with that critically personal, one-to-one experience that people today want to see.

The problem, however, is that the human brain is only capable of processing 1m gigabytes of memory. In other words, the amount of information available far exceeds the processing ability of humans. The term ‘Big data’- although often overused and misunderstood – is the science that drives the art of content marketing creation and engagement. However, it can only solve the critical questions of the modern marketer if people can learn how to use it. In 2017, the key to effective content marketing – that attracts, resonates and converts [2] – is incorporating machine learning and automation into the production process.

The role of machine learning

As we head into 2017, machine learning, deep learning and artificial intelligence will be the cornerstone of understanding data.

Machine learning has been part of a marketer’s everyday life for decades, without many realizing it. Modern day examples of machine learning [3] are found with Google, Apple’s Siri, IBM’s Watson, Facebook recommendations, Quora and (related questions) and any technology that says ‘suggestions’.

Taking Google as an example. On October 25th last year Google introduced RankBrain [4] to focus on Machine Learning. In short, this can be described as the ability of machine programs to ‘learn’ and predict behaviors. Machine learning can recognize patterns on its own and learn to predict responses.

https://www.bloomberg.com/news/articles/2015-10-26/google-turning-its-lucrative-web-search-over-to-ai-machines

According to Greg Corrado, a senior research scientist with Google:

“Search is the cornerstone of Google. Machine learning isn’t just a magic syrup that you pour onto a problem and it makes it better. It took a lot of thought and care in order to build something that we really thought was worth doing.”

More recently, in this article on Wired [5], Google machine learning is now begin to write featured snippet descriptions and using “sentence compression algorithms” on desktop results.

When brands equip themselves with technology to sift through the massive amounts of available information to find the discernable patterns and begin to make sense (learn) of the data, they will be able to walk away with actionable projects that can help them better engage their target audience.

The customer journey today has been disrupted by technology. It is no longer predictable and linear. It is also no longer led by the brands themselves. Instead, it is now in the hands of customers. This customer journey can now better be described as a series of micro touch points– moments [6] in time when customers realize that they have a question or need and turn to the internet to address their concerns.

Creating content and optimizing for these one-to-one experiences that address the unique needs and intent [7] of the individual, however, will only be possible when using machine learning to better understand the data flowing in from consumer behavior and the patterns that emerge.

To use an example, at BrightEdge (disclosure, my company) Data Mind [8] is a virtual team of data scientists built into the platform, that combines massive volumes of data with immediate, actionable insights to inform marketing decisions. Machine learning can also be used to detect anomalies in a site’s performance and interpret the reasons, such as industry trends, while making recommendations about how to proceed. This allows marketers to make decisions faster and accurately, capitalizing on positive gains and minimizing losses.

Machine learning and its ability to detect changes in interests and consumption behavior allows these organizations to be on the forefront of their industry and produce the material that people need before their competitors, boosting their reputation. Brands will also be able to understand the strategies put forth by their competitors. They will see how well they perform compared to others in their industry and can then make adjustments to their strategies to address the strengths or weaknesses that they find.

The Importance of automation in this process

As brands capture the critical information they need through machine learning, they will find that they still need the capability to take advantage of it quickly, before their competitors. As brands have begun to better understand the central role of the online world in modern commerce, the production of content has also picked up.

An estimated 77 percent of marketers [9] plan on increasing content production in the next year. This means that competition is tight. Just about every industry has multiple brands vying for the attention of customers– and many sectors are completely over saturated. For brands to establish themselves within this crowd, they need to understand how to use the data from their machine learning capabilities quickly: automation will be as important as mining the data. There is no space available for unnecessary content.

When companies decide to produce a new piece of content, basic steps such as uncovering important topics, help with content optimization [10], and access to information about the content already written on the topic should be completely automated. Brands should optimize material as they write, allowing it to be published fully equipped to rank as highly as possible from the moment they post it to the web.

The content produced will need to incorporate the information gleaned from machine learning, including competitor activities, what customers want to read, and where this particular piece of content will fit on the buyer’s journey. When this process can be largely automated, it will allow brand writers to produce the material ended quickly and efficiently while also creating more effective content and enhancing the position of the brand online.

Automation and Scale

Incorporating machine learning and automation into the content development process allows brands can now look beyond the insights they can gain from their own analysis and their ability to produce effective content based upon their intuition.

Automation will help these brands select the right topic and guide them through the optimization process as they write. This means that the content will rank as highly as possible as soon as they hit publish. Automation creates a more efficient process and maximize the content production ability of the brand, allowing them to compete against others in their industry. Ideally, the automation capabilities should also include the ability to link to other, related pages of content on the site, boosting engagement for the brand.

Kraft is a great example of a brand that has utilized machine learning to understand and automate. Kraft used it to track more than 22,000 different characteristics of its audience based upon how they interacted with the brand’s online content. As a result, Kraft now receives the equivalent of 1.1 billion ad impressions a year and its content marketing produce 4 X ROI [11].

Balancing Machine Learning and Human Capital

As the digital ecosystem becomes more complex and increasingly filled with unimaginable amounts of data, brands are going to have to turn to technology to effectively understand this information and use it to improve their content production and organic optimization efforts.

Machine learning will never replace humans as the main source of creativity; it will only help make our content production and optimization efforts more efficient. Machine can not manage people and foster creativity, emotion and cultures [12] that drive organizational content and organic search marketing efforts.

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References

  1. ^ By 2020 (www.cmo.com)
  2. ^ that attracts, resonates and converts (searchenginewatch.com)
  3. ^ Modern day examples of machine learning (www.cmo.com)
  4. ^ RankBrain (www.brightedge.com)
  5. ^ Wired (www.wired.com)
  6. ^ micro touch points– moments (searchenginewatch.com)
  7. ^ intent (searchenginewatch.com)
  8. ^ Data Mind (www.brightedge.com)
  9. ^ 77 percent of marketers (www.slideshare.net)
  10. ^ content optimization (searchenginewatch.com)
  11. ^ 4 X ROI (www.marketingprofs.com)
  12. ^ cultures (contentmarketinginstitute.com)
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