How Predictive Analytics Helps with Content
Let's be honest—most content creators have relied on their gut and experience for years. There's nothing wrong with that; those instincts are valuable. But we're missing insights that could make our work much more effective.
Think about how we typically plan content - we look back at what performed well and try replicating it. It's like driving forward while only looking in the rearview mirror. What if we could spot what our audience will want next, rather than just repeating what worked before?
Netflix is doing this well. They save about $1 billion annually by predicting what viewers want to watch next. With content everywhere these days, creating more stuff isn't enough anymore. We must be more thoughtful about what we create and when we share it.
The teams that figure this out gain a real edge, filling content gaps before competitors even notice they exist.
Three practical elements that make this work
1. Gathering the correct data (and using it)
To predict what will work, we need to collect meaningful information:
• How people interact with our content
• What they're saying and sharing on social
• What they're searching for
• Their feedback and conversations
• How our content performs over time
Raw numbers don't tell us much. What matters is connecting these pieces to see the complete picture. For example, knowing someone viewed your article is one thing, but understanding they found it through a specific search term, read it on their commute home, and then shared it with colleagues gives you much more to work with.
2. Finding the patterns that matter
With good data analysis, we can answer questions like:
• Which formats get people talking?
• What topics resonate with specific segments of our audience?
• When should we publish for maximum impact?
• What content journey leads people toward taking action?
These patterns often challenge what we think we know. We've found, for instance, that our longer content performs better on weekday evenings than in the mornings, when we always assumed people were fresh and ready to read.
Machine learning helps spot connections we'd never see ourselves - like correlations between seemingly unrelated factors such as weather patterns and engagement with specific topics.
3. Turning insights into content that works
All this data only matters if we use it to make better content decisions:
• Planning editorial calendars around predicted trends
• Creating more tailored experiences for different audience segments
• Choosing the proper channels for distribution
• Adjusting our approach based on what's working
The challenge is translating complex data patterns into something content teams can use. This means creating clear lines of communication between the data folks and the content creators and building systems for quick testing and refinement.
Challenges we've all faced (and how to deal with them)
1. Making sense of messy data
Challenge: Getting accurate, complete data from different sources.
Solution: Start with systematic collection processes and regular quality checks. Choose tools to gather multiple data streams to give you the complete picture.
2. Balancing personalisation and privacy
Challenge: Using data responsibly while respecting user privacy.
Solution: Be transparent about your data practices and focus on aggregate trends rather than individual tracking. Ensure you provide a clear value in exchange for the data people share with you.
3. Managing limited resources
Challenge: Finding the time and expertise to do this right.
Solution: Start small with focused pilot programs on specific content types. Expand as you demonstrate results and build your team's capabilities.
AI as a writing partner (not a replacement)
Look, we're not getting replaced by robots anytime soon. The magic that happens when a great writer sits down to craft a story or explain a complex concept? That's still uniquely human. But we've found AI tools can be incredibly helpful teammates in the content creation process.
We've been using them to test different headlines against what's worked well historically. Instead of spending hours debating which headline might perform better, we can get data-informed suggestions in seconds. The same goes for content structure—AI can analyse thousands of high-performing articles and help us identify patterns we might miss.
We're seeing across the industry that these tools work best for specific elements of content creation. They excel at analysing large volumes of existing content to identify what resonates with different audiences. They can suggest structural improvements that make complex information more digestible—like breaking up dense paragraphs, suggesting subheadings, or recommending where to add examples.
The key is to think of these tools as sophisticated assistants, not replacements. They handle pattern recognition and data analysis, freeing us to focus on the creative elements and human connections that make content truly valuable. Content creators still bring their experience, creativity, and unique perspective—AI just helps amplify what makes their work special.
Advanced systems enable content to adapt based on user behavior and preferences, creating more relevant experiences. This goes beyond simple A/B testing to content that evolves with each interaction, adjusting depth, complexity, and formatting to create better experiences.
We've all been there - you create a fantastic piece of content and then spend hours reworking it for every platform. A shorter version for Twitter, a more visual approach for Instagram, a professional angle for LinkedIn, and so on. It's exhausting.
We're just starting to experiment with tools that help solve this problem. We're not talking about those awful "post everywhere" buttons that dump the same content on different platforms. We mean intelligent systems that understand what makes each channel unique.
For example, we created a detailed guide on sustainable farming practices. Our new system helped us automatically generate a visually rich Instagram story, a discussion-oriented LinkedIn post, and a series of bite-sized Twitter threads—all from the original piece and maintaining our core message. Still, each is tailored to how people use those platforms.
Is it perfect? Not yet. We still need to review everything. But it's saving us hours of reformatting work and helping our content perform better across platforms. The tech is getting more competent at understanding what to say and how to say it on each channel.
We're particularly excited about how this will help smaller teams compete with more prominent brands. Without a specialist for each platform, you can focus on creating fewer, higher-quality pieces that effectively reach people wherever they prefer to engage.
Getting started: A practical approach
1. Take stock of your data situation
• Document what you're currently collecting
• Identify the gaps
• Choose tools to fill those gaps
2. Start small, then expand
• Pick one content type for your initial analysis
• Define clear success metrics
• Document what you learn and adjust your approach
3. Build teams that work well together
• Connect content creators with data specialists
• Encourage knowledge sharing in both directions
• Provide ongoing skills development
4. Keep improving
• Track performance against your goals
• Continuously test and refine your prediction models
• Share successful approaches across teams
After all this talk about algorithms and data patterns, we sometimes have to remind ourselves why we got into content creation in the first place: to connect with people, share ideas that matter, and spark something in someone else.
There's a growing conversation in our industry about finding the right balance. At content strategy conferences and professional forums, we constantly debate where human judgment ends and where data-driven decisions should begin.
One story circulating in content circles concerns a publication that went fully algorithmic for several months. Although their metrics initially improved, they started losing their core audience. Readers noticed something was missing—that human touch that made them connect with the content in the first place.
This is a crucial reminder. Data can tell us what topics might perform well and when to publish them, but it can't generate the original thinking, the unique perspective, or the authentic voice that makes content worth consuming in the first place.
Most content professionals consider predictive analytics a good compass, not the destination. It helps us navigate more efficiently to find our audience, but we still need to bring something valuable once we arrive. The magic happens when we combine the insights from our data with genuine human creativity and purpose.
Our teams' best work has always come from starting with a data-informed direction and then letting creators bring their full humanity to the task—their experiences, their passions, and their unique way of seeing the world. The analytics get us in the right neighborhood; the human element is what makes readers want to stay awhile.
People don't form relationships with algorithms. They connect with stories, ideas, and perspectives that feel authentic and meaningful. No amount of optimisation can replace that essential human connection.
What patterns might you discover in your audience's behavior? What opportunities could you uncover by looking forward instead of reflecting on past performance? How might these insights help you serve your audience more effectively?
