Generate an editorial plan based on real industry data
The company name and the figures in this case are fictional. The scenario is based on real use of the platform.
The client
Nuvola Blu, an architecture and interior design studio in Milan, 8 people. The owner handles communications directly, supported by a freelance social media manager who works 2 days a week.
The need
Every month the social media manager spent half a day preparing the next month's editorial plan. The problem was always the same: deciding which topics to focus on based on gut feeling and on whatever "seemed to work." She had no structured social listening system to understand what was actually driving engagement in the sector, nor a benchmark to gauge whether the studio's content was in line with competitors or completely off target. The result was editorial plans that changed direction every month with no clear strategy.
What she did with Strateego
Nuvola Blu monitored 5 architecture and design studios of similar size on Strateego, active on Instagram and LinkedIn. The platform had collected 90 days of data: every post published, the engagement each earned, the formats used, posting frequency, and the AI analysis of the sector's dominant themes. The social media manager opened ChatGPT with the Strateego MCP connector active and wrote this prompt:
The prompt
"Drawing on Strateego's data from the last 90 days, analyze what the brands I monitor are publishing and what's performing best in terms of engagement. Which topics generate the most interaction? Which formats perform best? Are there topics competitors aren't covering that represent an opportunity for us? Based on this analysis, create an editorial plan for next month for Instagram and LinkedIn: 3 posts per week per channel, with topic, suggested format, creative angle and recommended publishing day."
The result
ChatGPT analyzed Strateego's competitive data and first returned a summary of the sector's editorial landscape: competitors published an average of 4 posts a week, the content with the most engagement was project "before and after" posts (+78% vs. the average), reels showing the design process, and educational posts on materials and trends. It also identified a topic gap: none of the 5 competitors was covering material sustainability in a structured way, despite it being a growing subject in the sector.
Based on this data it produced a complete editorial plan for 4 weeks: 24 pieces of content in total (12 Instagram + 12 LinkedIn), each with its specific topic, recommended format (carousel, reel, single post, article), creative angle, and the optimal publishing day and time based on the engagement patterns found in the competitors' data. It suggested dedicating 30% of the content to sustainability, to own the gap before others did.
The benefits
The editorial plan wasn't based on hunches or generic trends found on Google, but on real social media monitoring data specific to the sector Nuvola Blu operates in. The resulting content strategy was data-driven in the most concrete sense: every choice — topic, format, frequency, timing — was backed by a real competitive benchmark. The sustainability gap, identified thanks to the AI analysis of Strateego's data, proved a winning call: the first 3 posts on the topic generated 45% more engagement than the studio's average.
Time saved
Preparing the editorial plan went from half a day of work (manual research, analyzing competitor profiles, brainstorming, drafting) to 15 minutes. But the real saving isn't just time: it's the quality of the decisions. Instead of guessing, the social media manager knew exactly what to focus on and why.
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