Balancing data and intuition in content strategy
I believe in the power of data in content strategy, but I don’t let it run the show. In my work, especially in tech-heavy fields like cloud FinOps and container solutions, data is an essential tool, but it’s not the only one in my toolbox. I want to share a bit about how I use data to improve content and why I make sure to balance that with other creative instincts.
How I use data to improve content
Content performance analysis: I use data to understand how different types of content resonate with different audiences. Metrics like click-through rates, time on page, and conversion rates help me assess performance. This is true whether it’s a deep-dive technical whitepaper or a blog post aimed at developers. By segmenting these metrics by audience type (e.g., enterprise vs. startup readers), I can tailor future content to be more effective and targeted.
Audience insights and engagement: Leveraging tools like Google Analytics or HubSpot, I track where my audience comes from and what keeps them engaged. If data tells me that content with specific headlines gets higher engagement, I optimize future content to reflect those patterns. I also pay attention to social media engagement — especially on LinkedIn, where I’ve shared tips and commentary. Those insights can reveal what resonates (or doesn’t) with my followers.
SEO and keyword analysis: Data is also key for refining my keyword strategies. By analyzing organic traffic and keyword rankings, I’m able to adjust the content to improve search visibility. Tools like Ahrefs and SEMrush help me find opportunities where search interest aligns with missing content — something I’ve looked at specifically for Docker to enhance bottom-of-the-funnel opportunities.
Content gaps and opportunities: I use data to identify content gaps — particularly when examining competitors. Looking at what’s out there helps me spot where more educational or deep-dive content is needed. This kind of analysis helps to drive potential customers down the funnel more effectively, and it’s been a useful tactic when helping companies like Docker explore new monetization opportunities.
A/B testing for messaging: For me, data-driven A/B testing is crucial to continuously improving content. Whether it’s testing headlines, CTAs, or different structures for messaging, the feedback data provides is invaluable. This is particularly relevant for sales enablement content — understanding what engages a SalesOps audience best.
Why I don’t depend solely on data
Human context matters: Data informs decisions, but the context of content consumption matters just as much. Sometimes, data can’t capture the nuance of a developer’s pain point or the emotion behind an enterprise buying decision. I make a conscious effort to balance what the numbers tell me with insights gained from firsthand audience interactions. This is especially important in tech-heavy sectors, where people’s needs and pain points can’t always be boiled down to metrics.
Qualitative insights: Quantitative data is great, but qualitative feedback — comments, interviews, conversations with sales teams — offers irreplaceable insight because it helps capture the emotions, motivations, and deeper context that numbers alone cannot provide. It helps me understand the “why” behind the content performance, which is crucial for creating messaging that speaks directly to customer needs, not just based on abstract metrics.
Experience and intuition: After years of writing for complex markets, I’ve developed an intuition for content that will work. I’ve researched gaps in messaging for companies like Capital One — gaps that data alone might miss. By marrying data with industry knowledge and intuition, I’m able to address gaps that algorithms may not identify, resulting in content that is sharper and more targeted.
The audience isn’t always in the data: One of the most important lessons I’ve learned is that tech buyers — especially those adopting DevOps or AI solutions — are often on the cutting edge. Data can lag behind their evolving needs. That’s why I make it a point to experiment and test content focused on emerging trends, ensuring that I’m not just following audience behavior, but also anticipating it. That means thinking beyond the search queries of today and trying to create content that speaks to tomorrow’s challenges.
Data is a powerful tool, but it’s not the only one. The most successful content strategies I’ve worked on combine data-driven insights with human context, creativity, and the ability to read between the lines — something no algorithm can do (yet). Balancing these approaches is what helps me create content that not only attracts attention but builds genuine connections.
Will Kelly is a writer, marketer, and keen observer of the IT industry. Medium is home to his personal writing. He’s written for CIO, TechTarget, InfoWorld, and others. His career includes stints in technical writing, training, and marketing. Follow him on X: @willkelly.
