How AI is Revolutionizing Content Marketing
Content creation has entered a new era of possibilities driven by artificial intelligence (AI). As capabilities advance, marketers can harness its potential to transform strategies in innovative ways. This article explores vital trends of AI adoption in content marketing and best practices for leveraging it to create better experiences.
AI's most transformative application is automating content generation. AI writer tools use natural language processing (NLP) to generate human-like long-form blog posts, social media captions, FAQs, and emails. Marketers provide relevant background information, and then AI handles the writing.
Tools like Jasper, Wordsmith, and Quill can produce hundreds of long-form articles or social media posts in seconds, a task that would otherwise take weeks of human effort. AI is revolutionising efficiency and solving the problem of content teams being pressed for time.
The automated copy has a consistent tone and follows brand guidelines—crucial elements for building a strong brand. AI enables ultra-personalization by creating multiple localised variations of a piece of content for different user segments.
AI liberally expands content possibilities. Human copywriters can focus on creative tasks while leaving repetitive work to algorithms.
The data generated by content opens up transformative AI opportunities. Predictive models can ingest engagement data, identify trends and patterns, and provide recommendations to optimise content.
Tools like Atoms, SocialPilot, and CoSchedule Prep use predictive analytics across blogs, social media, and emails to forecast a piece of content's click-through rate and engagement metrics. This intelligence enables the creation of consistently successful content.
Predictive analytics tools continuously gather post-publication performance data and refine their forecasting models. Thus, their recommendations become more precise, leading to a positive feedback loop of better content.
AI has introduced new paradigms for content testing and optimisation. A/B testing isn’t new, but manually setting up multiple variations requires significant effort.
AI-powered personalisation platforms like Marketo and Optimizely can programmatically generate micro-variations of content. These tools dynamically change headlines, images, calls to action, and body copy to create hundreds of personalised combinations. Then, they launch each variant to suitable user segments and discern the highest-performing versions based on engagement analytics.
This enables granular optimisation of multiple content elements simultaneously while eliminating guesswork. Content builders can use AI writing assistants to analyse existing copy and suggest improvements in readability, structure, length, and SEO.
The most futuristic AI application in content is its ability to learn from data and evolve content strategies.
AI agents can track analytics across all content over time - from blogs to emails to ads. They identify new trends, interests, and consumption patterns within their target audience. Accordingly, these systems can recommend adding or removing content types, switching formats, targeting different segments, and reallocating budgets - transforming the entire content roadmap.
An AI engine may suggest decreasing blog frequency while adding more podcasts and interactive videos based on waning engagement and increasing audio/video content consumption.
Such macro-level, data-backed recommendations enable brands to keep their content strategies relevant. AI becomes essential for creating adaptive content that evolves with changing users.
AI promises to elevate content creation from a fuzzy art to an intelligent science. Previously, creating engaging and viral content required guessing user preferences and pain points. It introduces algorithmic methods to eliminate uncertainty.
The permutations of content elements and engagement data enable brands to employ a systematic, comprehensive AI-based creation methodology. The vision is for every experience to become hyper-relevant, personalised, and optimised—not by chance but by intelligent design.
1. Start Small: Evaluate AI Software for a Single Content Channel
Implementing AI across all content channels is risky. Instead, start with a small, low-risk pilot focused on one area, like long-form blog posts. Choose an appropriate writing assistant tool. Measure its impact by tracking metrics like writer productivity, content output volume, and engagement analytics before and after AI adoption.
Define upfront success benchmarks aligned to goals, e.g., a 2x increase in monthly blog output with over 5% engagement lift. Once the pilot achieves the desired outcomes, replicate the process for other high-value content areas like social media or email marketing. Pursue a gradual rollout instead of an immediate AI takeover.
2. Ensure High-Quality Data: Clean, Audit, and Enhance Content Data
AI algorithms depend on input data for insights, so ensuring high-quality, reliable, and comprehensive training data is crucial. Before implementing any solution, audit existing content. Identify and fill gaps through research, such as surveys, interviews, and focus groups.
Devote time upfront to clean dirty data. Remove redundancies, correct inaccuracies in metrics reporting, and streamline formats for easy algorithmic processing.
Consider enhancing existing first-party data with external second-party data. This includes licensed third-party audience surveys or panels, census data, data cooperatives and exchanges, and social listening data. Expanding the diversity and depth of behavioural signals for AI leads to better intelligence.
3. Human + AI Collaboration: Creating Hybrid Content Teams
AI should enhance human creativity, not eliminate it. The best content strategies involve close collaboration between tools and experts like copywriters, designers, and growth hackers. Based on their strengths, define creative responsibilities between human team members and AI assistants.
Human copywriters focus on creativity and writing original, emotionally compelling content, while AI writing tools generate data-dense market reports to support the messaging with facts. Similarly, designers lead the creation of campaign themes while AI graphic platforms produce multiple personalised artefacts at scale.
Institute checkpoints for humans to review and supplement machine-generated output before publication to add nuanced finesse. They continuously retrain AI tools using the latest content data to enhance their intelligence, creating a symbiotic capability-building cycle.
4. Continual Improvement Mindset: Perpetual Testing and Optimization
AI adoption should be an ongoing optimisation exercise. AI functionalities should always be approached as in development, requiring continual experimentation, user testing, and refinement based on feedback.
Analyse content teams’ usage patterns to address friction in integrating AI tools within existing systems and processes. Survey users regularly for improvement suggestions.
Additionally, track content analytics post-AI implementation as new engagement data presents opportunities for further personalisation and performance lift. Feed these evolving behaviour signals into AI algorithms via retraining to improve the models over time.
Continuously test, measure, learn, and optimise to maximise ROI from AI spending. This mindset of continual improvement is vital to staying competitive in the rapidly changing world.
AI disruption has just begun. As algorithms improve, we can expect tools to write long-form persuasive copy, develop content strategies, ideate innovative formats, and design rich experiences. This will introduce a new age of intelligent content.
The possibilities of creating content that forges profound human connections, brand love, and viral reach will explode. The playing field will become more competitive as stellar material becomes accessible through AI.
Brands that embrace AI's potential today with an experimental spirit will have an early-mover advantage. They’ll significantly augment (but not entirely replace) their content staff through machine augmentation, enabling greater creativity.
Thanks to AI's vigilance and insight, the future belongs to creators of user-adaptive experiences. Intelligent content is no longer a future possibility but an imperative opportunity to seize, catalysing competitive advantage and growth today.
