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How Machine Learning Can Improve Content Marketing


Peter Cassidy
Founder and Chief Product Officer
Stackla

While there are many parts of a marketer’s job that can be automated, content creation is not one of them. Still, there are aspects of the content workflow that technology can augment and strengthen. Peter Cassidy, founder and chief product officer of social media content aggregation platform Stackla, spoke with eMarketer’s Maria Minsker about the role machine learning will play in the future of content marketing.

eMarketer: What are the challenges associated with content marketing from a technology perspective?

Peter Cassidy: The biggest challenge is the lack of content to fuel the technology investments that companies have made, which makes it difficult for them to see a return on those investments. For example, marketers are investing in web personalization technology to tell a targeted story to every individual customer, but they don’t have the volume of content needed to make that personalization worthwhile. There’s all this data and technology available for personalization, but realistically, brands are deciding between just two versions of their ads or just two versions of their websites.

eMarketer: What role does machine learning play in improving content generation and content marketing?

Cassidy: Machine learning-powered tools can find the best content out on the web, including thought leadership pieces, brand-relevant user-generated content and other content types. The technology can then determine how content is being engaged with in real time, and how it’s being pushed out across channels. These types of tools can also make recommendations to brands about which content they should use, and how to best target it.

“There’s all this data and technology available for personalization, but realistically, brands are deciding between just two versions of their ads.”

eMarketer: You mentioned user-generated content. Can artificial intelligence technology actually help scale marketers’ use of user-generated content, or is it just too difficult to automate the process of vetting that content?

Cassidy: The biggest obstacle to incorporating user-generated content in advertising is having to ask the content creator’s permission. It’s very easy for a marketer to promote social content organically without having to ask permission, but if they want to use it in a paid environment or store a copy of that content, they have to ask permission. But rights management is a challenge that technology can solve. Marketers grapple with it, but vendors can help achieve this at scale.

eMarketer: What are some concerns marketers may have about automating content marketing or using machine learning to analyze and gather content?

Cassidy: Marketers are cautious about the notion that technology can help identify or predict content performance better than a human being. Marketers have to understand that technology gives them control and empowers them to make well-informed decisions, but it’ll ultimately always be humans that choose to publish or not publish something.

“Marketers are cautious about the notion that technology can help identify or predict content performance better than a human being.”

eMarketer: How might machine learning-based content marketing technology fit into the marketing technology stack?

Cassidy: Whether it’s user-generated or branded content, a machine learning-based [content marketing and content management tool] can store it all centrally and make sure that it’s tagged and classified in a way that enables marketers to dynamically call the right content out for the right audience at the right time. With that in place, marketers can extract tagged content and put it into an email automation tool, push it into an ad or place it into a website homepage, all from the same central library.

This is key as marketers move towards a multichannel approach—if they’ve got disparate content sources at disparate touchpoints, it becomes tough to manage, and creates a disconnected user journey.

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