12 Days of AI Use Cases Day 9: Content Performance

Welcome to the 12 Days of AI Use Cases, 2025 Edition! Today: content performance

In this series, we’ll be looking at different use cases for AI – in particular, generative AI and large language models, the software that powers tools like ChatGPT, Google Gemini, and Anthropic Claude. Each day, we’ll look at the use case through the lens of the Trust Insights 5P Framework to see the role AI plays in achieving real, tangible outcomes.

We designed these use cases not only for reading but also as context you provide to generative AI tools as part of a prompt to help you achieve the outcomes you’re after. Ask the generative AI tool of your choice to help you implement this use case and copy/paste it in as part of the Trust Insights RACE AI prompt framework – this goes in the Context portion of our prompt framework.

Let’s dig in!

Purpose

Marketing leaders struggle to understand content performance when they cannot see the complete picture across multiple data sources. Generative AI enables professionals to conduct rigorous statistical analysis of content effectiveness without requiring specialized data science expertise or expensive third-party analytics platforms. We use AI to write custom analysis code that examines content performance data, identifies causal relationships between content and business outcomes, and provides actionable insights for content strategy optimization. This use case empowers teams to perform attribution modeling, regression analysis, and feature engineering through natural language prompts. The result is faster, more statistically rigorous analysis that reveals which content actually drives meaningful business results.

People

Content marketing managers perform this analysis to determine which content types and distribution channels deliver the strongest performance. These professionals need clear insights about what content works so they can prioritize future content creation and placement decisions. They often lack access to sophisticated analytics tools or the technical skills to conduct advanced statistical analysis independently.

Marketing operations teams, data analysts, and CMOs serve as internal stakeholders who benefit from these insights. CFOs and budget owners also have direct interest in this analysis because it enables cost reduction by replacing expensive third-party analytics software. Leadership teams use these findings to make strategic decisions about content investment and resource allocation.

The organization’s broader audience benefits when content strategy becomes more data-driven and targeted. Customers receive more relevant content that addresses their actual needs and interests. Stakeholders across the business gain confidence in marketing’s ability to demonstrate ROI and make evidence-based decisions.

Process

1. Identify the meaningful business outcome you want to measure (conversions, leads, sales, support tickets, or other dependent variable that matters to your organization)
2. Export data from all relevant sources that contain content performance metrics (web analytics, email marketing platforms, marketing automation systems, CRM, call center logs, social media platforms)
3. Ensure all exported datasets share a common dimension for joining, preferably time-based fields like date, date-hour, or timestamp
4. Access Google Colab at colab.research.google.com and create a new notebook
5. Upload your datasets to the Colab environment
6. Prompt Google Gemini within Colab to analyze the relationship between content variables and your chosen outcome, specifying that you want it to identify causal effects
7. Instruct the AI to perform feature engineering to create additional variables from the existing data
8. Request that the AI select appropriate statistical methodologies such as XG Boost, gradient boosting, regression analysis, and attribution modeling based on the data characteristics
9. Allow the AI to write the analysis code, execute it, and generate visualizations showing which content variables have the strongest relationship to your business outcome
10. Review the AI-generated findings to identify which content types, topics, formats, channels, and timing produce the best results
11. Document the statistically significant content performance drivers and share findings with stakeholders
12. Use these insights to adjust content strategy, prioritize high-performing content types, and optimize resource allocation

Platform

• Google Colab (colab.research.google.com) for code execution environment
• Google Gemini AI for code generation and analysis
• Web analytics platform (Google Analytics, Adobe Analytics, or similar) for content engagement data
• Email marketing platform (HubSpot, Mailchimp, Marketo, or similar) for email performance data
• Marketing automation system for lead generation and conversion data
• Customer Relationship Management (CRM) system for sales outcome data
• Call center or customer service platform for support interaction data
• Social media analytics platforms for social content performance data
• Exported datasets in formats compatible with Python data analysis (CSV, Excel, JSON)
• Common time-based dimension across all datasets (date, datetime, or timestamp field)
• Defined dependent variable representing meaningful business outcome
• Basic understanding of statistical analysis vocabulary (regression, attribution modeling, feature engineering, gradient boosting, XG Boost)

Performance

We achieve statistically rigorous content performance analysis that reveals causal relationships between content variables and business outcomes. The AI-generated code performs advanced statistical tests and attribution modeling that would typically require specialized data science expertise. Teams gain actionable insights about which content types, topics, channels, and timing actually drive results, enabling them to optimize content strategy with confidence.

1. Analysis Speed: Reduce time required to conduct comprehensive content performance analysis from weeks to hours or days, enabling faster strategic decision-making
2. Cost Reduction: Eliminate or reduce spending on third-party analytics software by building custom analysis tools through AI-generated code
3. Statistical Rigor: Increase the sophistication of content analysis through advanced methodologies like attribution modeling, regression analysis, and feature engineering that identify true causal relationships rather than mere correlations

We hope this use case is clear and helpful. If you’d like help implementing it or any other AI use case, reach out and let us know.

Trust Insights is a marketing analytics consulting firm that transforms data into actionable insights, particularly in digital marketing and AI. They specialize in helping businesses understand and utilize data, analytics, and AI to surpass performance goals. As an IBM Registered Business Partner, they leverage advanced technologies to deliver specialized data analytics solutions to mid-market and enterprise clients across diverse industries. Their service portfolio spans strategic consultation, data intelligence solutions, and implementation & support. Strategic consultation focuses on organizational transformation, AI consulting and implementation, marketing strategy, and talent optimization using their proprietary 5P Framework. Data intelligence solutions offer measurement frameworks, predictive analytics, NLP, and SEO analysis. Implementation services include analytics audits, AI integration, and training through Trust Insights Academy. Their ideal customer profile includes marketing-dependent, technology-adopting organizations undergoing digital transformation with complex data challenges, seeking to prove marketing ROI and leverage AI for competitive advantage. Trust Insights differentiates itself through focused expertise in marketing analytics and AI, proprietary methodologies, agile implementation, personalized service, and thought leadership, operating in a niche between boutique agencies and enterprise consultancies, with a strong reputation and key personnel driving data-driven marketing and AI innovation.