Examining the Potential of Artificial Intelligence in Content Marketing


PHOTO: Andrei Popov


If you go by the marketing materials of most martech companies, AI-powered tools are everywhere. The irony is that marketers themselves are still struggling to understand where and how to apply AI to their strategy, process, stacks, and talent to get the most out of this rapidly developing technology.

Content marketing holds promise for AI

Marketing content, essential to the marketing process, is a promising area for AI-powered tools. The vendor landscape has grown from about five well-known vendors at the turn of the decade to over 50 or more today.

On the demand side, the 2021 State of Marketing AI report found that content marketing is one of the top areas where marketers are using AI. Of the 10 AI use cases for marketing, four were related to content creation: data-driven content creation; predict the winning creative (e.g. digital ads, landing pages, CTAs) before launch and without A/B testing; choose keywords and subject groups for content optimization; and optimizing website content for search engines.

Related Article: CX Decoded Podcast: Practical Use Cases for AI in Marketing

Where can AI impact content marketing?

AI typically makes it possible to perform repeatable and automated tasks smarter, faster, and at scale. Chris Penn, co-founder of marketing analytics firm Trust Insights, recently shared his experiences using EleutherAI’s latest open-source language model, GPT-NeoX-20B, for content use cases. He found that he generated consistent, readable text “very well” when given appropriate prompts, and suggests evaluating AI solutions for “specific, narrow tasks and deploying them to accomplish those tasks.” as quickly as possible”.

Use cases for content research, planning and strategy

Even before production, AI can help develop the most relevant and effective content schedule:

  • Content search: analyze the internet to collect data and generate topics, keywords and content ideas based on current and historical trends.
  • Hearing audits: scour unstructured raw data – especially on social media – to find brand and category mentions, offer topic suggestions based on audience sentiment.
  • Identify the best A/B tests options to improve content performance.

Content production use cases

AI is getting exponentially better at understanding, writing, and speaking human-like language, incorporating local language, grammar, punctuation, branded style guides, and other settings. It breathes unfathomable speed and scale into a traditionally time- and effort-intensive human activity like writing. Precision, context, and nuance are still areas of human intervention, even as AI continues to learn.

Some other features in this area include:

  • Develop article structure, table of contents, and first drafts of long-form content, which will save you a lot of time.
  • Create short form content such as social media posts, paid search and display ads, email subject lines and sales copy and formula content such as press releases and all-purpose plates.
  • Repurposing content at scale, such as turning long-form content into case studies, infographics, product descriptions, or even video scripts, translating content into multiple languages ​​at scale.
  • Detailed technical content such as financial and annual reports, technical manuals.
  • Iterative chatbot scripts based on continuous learning from user prompts.

Content Performance Optimization Use Cases

AI can also improve content performance in several ways:

  • Content personalization recommendations.
  • Speed ​​and scale of GTM, from research to content creation, distribution and measurement.
  • SEO performance: integrating the best keywords and designing SEO strategies.
  • Large-scale performance intelligence analytics, recommendations to improve content effectiveness.
  • Content and workflow standardization: Intelligent content management platforms can help streamline the process from planning to creation and distribution, especially for large distributed teams. They can also drive standardization of brand identity and messaging across content formats and functional silos such as email marketing and social media marketing teams.

Related Article: 8 Considerations When Selecting an AI Marketing Vendor

5 considerations for investing in AI tools for content marketing

Where can marketers start discovering AI tools for marketing processes and results?

  1. You should always start with content strategy and use cases: Not surprisingly, as with any technology, you first need clarity on what you want to accomplish and why. Tools are just the “how,” so ask yourself which tools can help you work faster and smarter toward your strategic goals, said Cathy McPhillips, director of growth at the Marketing Artificial Intelligence Institute. Identifying the right use cases is a natural outcome of this process, so that the most repetitive and mundane but important work can be done by AI-powered tools.
    John Cass and Scott Sweeney, co-founders of AIContentGen, which advises marketers on selecting AI content tools, say the three key metrics in tool selection should be the quality of generated content, the ease of use and searchability of the tool.
  2. Resource allocation: Write resources are expensive and can be redeployed to where they are really needed. Focus on shifting much of the heavy lifting to AI and reallocating humans to more value-creating tasks. As the role of marketing copywriters and content agencies continues to evolve, McPhillips said they will need to learn how to work with AI. Not so much in terms of using technology, which is increasingly plug-and-play, but using their newly freed up time on more important and rewarding tasks such as content context and relevance; be more involved in quality control, editing and fact-checking; and tweaking the tone and the manner of copying, etc.
  3. Realignment of processes: Integrating AI-powered content marketing tools into the overall martech stack is important for efficient and seamless workflows, but it’s equally important to incorporate subject matter expert workflows , management and legal approval in content created by AI.
  4. Build an ecosystem of experts: AI in all aspects of marketing is inevitable. CMOs should think about building in-house expertise for optimal use of technology and involving people in AI training to generate more relevant and brand-aligned content. If AI-written copy is the ultimate form of regurgitation, how can marketers infuse their original brand voice into content?
    Penn said big brands can “tune” very large pre-trained models with their own data (for example, all the blog posts they’ve written) to capture brand voice. This approach is much less computationally intensive than building the entire model from scratch, and SaaS vendors may even offer it as an additional “customization” service. Smaller brands may end up with more generic, industry-specific models (AI trained on healthcare, financial services, etc.). This can widen the performance gap between small and large companies. Since intelligent content has been an area that has seen small D2C companies punch above their weight lately, it will be interesting to see how AI can balance that out.
  5. Reassess performance measurement: AI content tools will impact efficiency (opex) and effectiveness (content marketing ROI). Will it change how the performance of marketers, writers, and the content itself is measured? Sweeney and Cass said that with AI tools able to research and create content more efficiently, content creators will gain additional stature as they become more productive and deliver higher quality content and conversions. improved. For marketers, content also offers the opportunity to differentiate the brand, so they’ll want to assess how AI tools can increase their competitive advantage and drive profitability.

Related Article: AI in Marketing: Use Cases and Examples in Content Marketing

Marketers still need to know more

The technology’s potential is evident in terms of lowering costs, accelerating revenue, and even differentiated content experiences. What’s holding marketers back, however, isn’t fear of AI, but rather the need for more knowledge and education on how best to leverage the potential of technology in a sustainable and integrated way. .

What’s promising is not just the level of technology today, but the fact that AI performance improves with use and more data. Ironically, this could be the perfect solution for a world overwhelmed with content and data, while vastly improving what we are capable of as humans.

About William G.

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