Turn Data Thought Leadership Into Growth: Semrush’s Guide
▼ Summary
– Semrush transitioned from sporadic data studies to a structured, repeatable program to address market unpredictability and AI dominance in search.
– The new initiative established clear ownership, a topic pipeline, and a dedicated distribution engine to ensure consistent output and visibility.
– Original data studies provide unique competitive advantages by generating uncopiable content that attracts journalists and earns media citations.
– Proprietary research significantly enhances brand authority and directly supports product marketing campaigns while answering customer questions.
– This strategy has delivered measurable business results, including substantial organic traffic, user registrations, and new customer acquisitions without paid promotion.
Transforming Data Insights Into a Scalable Growth Engine
For years, Semrush approached data studies the same way many organizations do: sporadically and opportunistically. We produced one or two major reports annually whenever time permitted and ideas aligned. However, two significant shifts forced us to reconsider this strategy. First, AI began dominating organic search results, fundamentally altering how content is discovered. Second, our market became increasingly crowded and unpredictable, making it impossible to plan quarterly traffic goals based on historical trends alone.
We needed a more reliable method to drive attention, attract citations, and maintain top-of-mind awareness. The solution was not to create a better single study, but to institutionalize data research into an official, repeatable program. This required clear ownership, a structured topic pipeline, a defined production process, and a robust distribution engine. Drawing from my experience as the Content & Product Marketing Lead at Semrush, here is how we built that system and how you can adapt it for your team.
Why Proprietary Data Is a Critical Marketing Asset
High-quality, original data studies remain a gold standard in content marketing because they generate assets that did not previously exist. In an era where AI easily repurposes existing opinions and playbooks, sharing unique insights allows brands to differentiate themselves. Backing these viewpoints with proprietary data amplifies several key benefits:
- Uncopyable Originality: Competitors might replicate a blog post in a day, but they cannot match your proprietary dataset.Since establishing data-driven thought leadership as an official program, we have consistently attracted thousands of unique visitors without paid promotion, driven hundreds of registrations, and acquired new customers.
Building the Program: From Ad Hoc to Strategic
We transitioned from releasing massive, sporadic PDFs to maintaining an always-on research program. This shift was essential for establishing long-term authority. Here is the playbook we followed.
1. Secure Executive Buy-In and Clear Ownership
Every successful program begins with organizational commitment. Because data projects require cross-functional coordination, you need more than just enthusiasm; you need resources. Two elements are critical:
- Company-Level Recognition: The program must have a dedicated budget line and a stakeholder who defends it in planning meetings.Some companies, like Adobe with its Digital Insights team, go further by building entire departments around this function. Regardless of structure, ensure design, campaign, and email teams understand the expected impact so they can prioritize support.
2. Align Research With Business Priorities
Strategic research planning goes beyond listing interesting topics. It requires alignment with industry trends, business goals, product roadmaps, and customer needs. When planning quarterly research, consider:
- Core topical areas your brand should own.The primary filter is alignment. For example, Semrush believes unifying SEO and AI visibility is the best path forward. Our research supports this POV, strengthening our narrative. To find relevant topics, listen to your customers. I interviewed users weekly and noticed a recurring question: “Why do we get cited but not mentioned?” We partnered with Kevin Indig to publish a study answering this exact question, demonstrating the power of addressing specific user concerns.
3. Ensure Practical Value Beyond the Data
Publishing data for its own sake is a common trap. If a study does not offer actionable insights, reveal something genuinely new, or provoke follow-up questions, it likely wastes resources. While proprietary data earns links, it is becoming more common. To stand out, identify angles competitors have missed and ensure your findings give readers something they can use,a playbook, a takeaway, or a different decision-making framework.
Always address the why, what, and how:
- Why the finding matters.
- What it means for the reader.
- How they can apply it.
Data without a “so what” is merely noise.
4. Streamline Production Workflows
Speed is often the biggest bottleneck in data marketing. Ideas can sit in backlogs for months, allowing competitors to publish first. To accelerate output, define workflows and Standard Operating Procedures (SOPs) for each study type. Empower marketers with tools like survey budgets and AI credits, and create study brief templates for data teams. Establish agreed-upon timelines, such as a 48-hour review period for briefs and seven to fourteen days for data delivery depending on complexity.
Studies typically fall into four categories:
- Data Science-Heavy Studies: Require defined intake processes and clear briefs.
- Expert Collaborations: Partner with analysts like Kevin Indig to bring external perspectives and expand reach.
- Marketer-Led Studies: Lightweight analyses or surveys that do not require engineering time.
- Co-Branded Studies: Time-intensive but highly rewarding for relationship building and shared audiences.
Our collaboration with LinkedIn combined our AI-citation data with their engagement metrics to analyze why content gets resurfaced by AI. This joint effort resulted in our most viral research piece, featured heavily by media and influencers.
5. Build a Multi-Channel Distribution Engine
Distribution is where many programs fail. You need a repeatable process for every study. Effective tactics include:
- Repurposing Content: Create short-form videos, slide decks for sales, newsletter breakdowns, and LinkedIn carousels.Ensure every study has a lifecycle that extends well beyond the initial report launch.
6. Measure Meaningful Success Metrics
Measurement often swings between tracking too much or too little. Focus on metrics that indicate progress without obsessing over direct attribution, which is rarely straightforward for thought leadership. Key indicators include:
- Traffic and on-page engagement.While monitoring downstream revenue like MRR is valuable, it should not be the primary goal. The impact of a data program compounds over time, requiring patience and consistent execution.
Final Thoughts
Data thought leadership becomes a powerful growth channel when treated as a structured program with real ownership, strategic alignment, and planned distribution. You do not need a perfect system on day one. Start with one experiment, prove the model works, and then scale the infrastructure. Remember that even unique content can become commoditized. Differentiating through customer value and connecting your data to broader industry trends will keep your research relevant and impactful.
(Source: Hubspot.com)
