Observed Signal · Mar 7, 2022 · Analysis · Source: CMSWire · Impact: 2/5 · Sentiment: Positive
AI Content Marketing Use Cases and Tool Selection Guidance
This CMSWire feature examines how artificial intelligence is being applied to content marketing, one of the most promising areas for AI adoption in marketing. The article notes the vendor landscape for AI content generation has grown from about five vendors at the turn of the decade to more than 50. It cites the 2021 State of Marketing AI Report, which found that four of the top 10 AI marketing use cases relate to content creation, including predicting winning creative, choosing keywords, and optimizing website content for search. Contributors including Chris Penn of Trust Insights, Cathy McPhillips of the Marketing Artificial Intelligence Institute, and AIContentGen co-founders John Cass and Scott Sweeney discuss use cases in research, production, repurposing, personalization and SEO optimization. The article also outlines five considerations for marketers investing in AI content tools, such as resource allocation, process realignment, building internal expertise, and reassessing performance measurement.
Provides an overview of AI-powered content marketing use cases and vendor selection guidance; relevant to MarTech and AI adoption but not an industry-shifting event.
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Key Takeaways & Evidence Grounding
- The AI content generation vendor landscape grew from about five vendors at the turn of the decade to more than 50 by 2022.
- The 2021 State of Marketing AI Report found four of the top 10 AI marketing use cases involved content creation, including predicting winning creative and optimizing website content for search.
- Chris Penn of Trust Insights tested EleutherAI's GPT-NeoX-20B open-source language model and found it generated coherent, readable text with appropriate prompts.
- AI content tools can be applied to content research, audience audits, long-form and short-form production, repurposing, personalization, and SEO performance optimization.
- CMOs should consider resource reallocation, process realignment, internal AI expertise, and performance measurement when investing in AI content tools.
Ontology Mapping & Concepts
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