The Death Of Fluff: How Marketers Need To Use AI

Over the last decade, we’ve seen the rise of inbound marketing. From those martech companies that have pushed it on us through content hub models and keyword-based content to the small or medium-sized businesses trying to drive more leads, inbound marketing has taken over.

For the last several years, companies that could produce the most content grabbed the most market share. They researched keywords and produced, produced, produced by creating boiling rooms of content creators.

But I believe we’re at a tipping point when it comes to inbound marketing, especially when it comes to content.

With the rise of artificial intelligence and machine learning, relying on this mass production of lightweight content like listicles, regurgitated infographics, and easily disguised promotions within blog posts isn’t going to last.

The Rise Of AI And Machine Learning In Inbound Marketing

If you’ve seen a display ad recently, there’s about an 80% chance it was brought to you by programmatic advertising, according to eMarketer.

AI has transformed content personalization by analyzing vast amounts of data. This allows marketers to craft messages that resonate with individuals based on their preferences and habits.

This targeted approach is making marketing efforts more effective and engaging than ever before.

It’s not just programmatic advertising. Some of the content you’re reading every day is likely to have been at least influenced by artificial intelligence.

According to an Adobe study (via CMO.com ), 15% of enterprises are using AI in 2018, and 31% have it on the agenda to implement within the next 12 months.

For those of us in B2B marketing roles, the platforms we’re using every day to create content are using AI to help us do our jobs. We see this in how AI can help marketers create optimized social media posts easily or Narrative Science’s Quill platform, which uses natural language generation (NLG). LeadGenius, my own company, uses machine learning and natural language processing to aggregate bulk business data sets at scale for use in CRM and MAS databases.

The integration of AI in marketing raises important ethical questions, particularly around data privacy. Marketers must consider how data is collected and used, ensuring transparency.

Balancing innovation with ethical responsibility is key to maintaining consumer trust in this technologically driven landscape.

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