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Skyword Employs Artificial Intelligence to Hyper-Personalize Enterprise Content

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One of the top challenges marketers face is creating adequate volumes of relevant content cost-effectively and sustainably. Content marketing platform Skyword ( @Skyword ) thinks the right combination of humans and artificial intelligence could be the solution.

The company uses deep learning to deliver personalized on-site and email content recommendations for higher engagement and conversions. Skyword’s freelance marketing in artificial intelligencecommunity then makes those recommendations reality by producing buzzworthy content for global enterprises.

Skyword’s work gives us an enticing glimpse into a future in which marketers work hand-in-hand with artificial intelligence systems to improve and scale their work. Skyword founder and CEO Tom Gerace (@tomgerace) gives us the full story on this type of AI in content marketing.

In a single sentence or statement, describe Skyword?

Today’s leading enterprises use Skyword’s software, freelance community and services to build an engine for sustained creation and distribution of original content and stories to reach, engage, convert and support a loyal audience.

How does Skyword use artificial intelligence (i.e. machine learning, natural language generation, natural language processing, deep learning, etc.)?

Skyword uses artificial intelligence in its personalization solution. Skyword Personalized Recommendations (SPR) is an artificial intelligence based engine that delivers personalized on-site and email content recommendations that generate increased engagement and conversions.

Our customers are creating valuable experiences with great content that their audiences may find useful. However, their customers and prospects often get frustrated when they don’t find the specific information they are looking for fairly easily. People don’t want to have to browse, search and spend time that they don't have. They expect marketers to figure out their needs and present exactly what they want in an efficient way.

We built a deep learning engine that can accommodate many different algorithms. Many of them are machine learning algorithms and our infrastructure allows us to easily add and tweak algorithms with an ensemble algorithm sitting in front of them. This allows us to deliver accurate content recommendations that serve the right content to the right person at the right time.

What do you see as the limitations of artificial intelligence as it exists today?

When we look at the application of AI in marketing, the primary limitations lie in the constraints in the system. Those constraints primarily come from the inputs into the model, the algorithms available to the model and/or the actions the model is capable of driving.

In marketing, the disparate disconnected systems that form the marketing stack can drive a major obstacle in the first of these. Historically, you’ve had different platforms for content creation, analytics, content hosting, demand generation, sales, etc. Consequently, there is a lot of data loss. When you lose the connection between data points, you are only able to assess some behavior in aggregate versus individual use cases. This also means delays in gathering the data so you can’t act in real time or near real time.

In our particular space, that meant marketers didn’t have the content, usage data and channel data—and couldn’t access direct results. We’ve tried to tackle this by creating an integrated platform with a range of data inputs that tie out to a single user.

What do you see as the future potential of artificial intelligence in marketing and sales?

The future potential of artificial intelligence in marketing is to create a totally responsive experience for the user which includes the early engagement through to sale and customer support. With a unified approach that brings together data, content and channels, marketers will be able to predict how an individual wants to interact with a brand and deliver experiences that provide the greatest value for both the consumer and the brand.  

What makes Skyword different than competing or traditional solutions?

Skyword uses a deep learning system that uses an algorithm of algorithms, so we can constantly be putting in new mathematical models that can be predictive when looking across millions of pieces of content and billions of user observations. This will allow us to get smarter as we get better at selecting the right algorithms.

Unlike other personalization solutions, Skyword is a single source for content creation, distribution and personalization, making it easy to deliver and measure the efficacy of relevant, personalized content experiences. No other content marketing platform offers the ability to deliver personalized content experiences based on machine learning technology.

Who are your prototype customers in terms of company size and industries?

Skyword’s clients are global enterprises. Approximately 45 percent of our customers are B2B companies, 45 percent are B2C and 10 percent are media companies. Skyword’s customers span a range of industries including financial services, healthcare, retail and technology.

What are the primary use cases of Skyword for marketers and sales professionals?

Skyword’s customers use the platform as an engine to create, storify, distribute and manage original content sustainably. They also access our community of more than 10,000 contributors to find specific subject matter experts and creatives to fuel their content strategies. The majority of our clients use our editorial and content strategy services to execute a content strategy that differentiates their brands. They create and manage the editorial and approval workflow for a range of content through Skyword, including articles, videos, photography, infographics, case studies, ebooks and more.

Any other thoughts on AI in marketing, or advice for marketers who are just starting to explore the possibilities of AI?

My advice would be to understand what type of experience you are trying to create and the value you are providing to the consumer. Also, you need to make sure that you have access to the data and content to build a truly predictive, responsive system.  You also need to consider the ROI and ROE of the project. Ask yourself what value is this providing to our business in terms of both revenue and margins.

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