12 Days of AI Use Cases Day 5: Quarterly Reviews

Welcome to the 12 Days of AI Use Cases, 2025 Edition! Today: Quarterly Reviews.

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 managers and business leaders must present comprehensive quarterly reviews to demonstrate results, identify successful strategies, and prescribe improvements for the upcoming quarter. Recency bias prevents most professionals from accurately recalling the full scope of activities and outcomes across a 90-day period, which leads to incomplete analyses and suboptimal planning. We use generative AI to aggregate and analyze both qualitative activity data and quantitative performance metrics from multiple sources, enabling us to create complete quarterly assessments that inform strategic decisions. This approach transforms quarterly reviews from memory-dependent exercises into data-driven strategic planning sessions that improve future performance.

People

Marketing managers, directors of analytics, and strategic marketing leaders perform this use case to synthesize quarterly performance data and develop prescriptive recommendations for leadership teams. These professionals manage multiple campaigns, initiatives, and stakeholders simultaneously, making comprehensive recall of all quarterly activities difficult without systematic documentation and analysis.

Internal stakeholders include executive leadership who review quarterly results, finance teams who evaluate budget allocation effectiveness, and marketing team members whose tactical execution generates the data we analyze. Department heads and project managers benefit from understanding which initiatives drove measurable outcomes and which require adjustment or discontinuation.

External audiences include clients (for agencies and consultancies), board members, and investors who evaluate organizational performance based on quarterly results and strategic planning quality. These stakeholders make resource allocation decisions based on the clarity and actionability of quarterly reviews, making comprehensive analysis essential for securing continued support and investment.

Process

1. Collect all qualitative data sources from the quarter, including daily stand-up meeting notes, weekly team meeting transcripts, project documentation, email threads, conference call recordings, client communications, and strategic planning documents.
2. Export quantitative performance data from marketing analytics platforms (Google Analytics, Adobe Analytics), marketing automation systems (HubSpot, Marketo, Pardot), CRM systems (Salesforce, Microsoft Dynamics), advertising platforms (Google Ads, Meta Ads Manager), and financial systems that track marketing spend and revenue attribution.
3. Aggregate qualitative data into a tool like Google NotebookLM or a similar AI analysis platform, organizing materials by time period, campaign, or initiative to maintain contextual relationships between related activities.
4. Process the qualitative data through the AI tool to identify all activities, initiatives, campaigns, experiments, and strategic pivots that occurred during the quarter, creating a comprehensive activity inventory that overcomes recency bias.
5. Load quantitative performance data into an analysis environment such as Google Colab, Excel with Python integration, or R Studio to calculate key performance indicators, trend lines, statistical significance tests, and comparative analyses against previous quarters or benchmark data.
6. Combine the qualitative activity inventory with quantitative performance results in a generative AI tool like ChatGPT, Claude, or Gemini, providing context about business strategy, competitive landscape, customer profiles, and market conditions to enable meaningful interpretation.
7. Ask the AI to correlate activities with outcomes, identifying which initiatives drove positive results, which underperformed relative to expectations, and which showed no measurable impact on business objectives.
8. Request prescriptive recommendations from the AI based on the activity-outcome correlation analysis, including specific suggestions for budget reallocation, tactical adjustments, strategic pivots, resource requirements, and initiative prioritization for the upcoming quarter.
9. Validate the AI-generated insights against your domain expertise and organizational context, adjusting recommendations to account for factors the AI may not fully understand such as organizational politics, resource constraints, or strategic initiatives in development.
10. Structure the final quarterly review presentation using the validated insights, organizing findings into clear sections covering quarter overview, successful initiatives with supporting metrics, underperforming areas with root cause analysis, and prescriptive recommendations with expected outcomes and resource requirements.

Platform

• AI Analysis Tools: Google NotebookLM for qualitative data aggregation and analysis, ChatGPT/Claude/Gemini for synthesis and correlation analysis
• Quantitative Analysis Platforms: Google Colab, Python (pandas, matplotlib, seaborn), R Studio, or Excel with Power Query for statistical analysis
• Marketing Analytics Data: Google Analytics 4, Adobe Analytics, platform-specific analytics (social media insights, email performance metrics, website engagement data)
• Marketing Automation Data: HubSpot, Marketo, Pardot campaign performance, lead scoring changes, automation workflow results
• CRM Data: Salesforce, Microsoft Dynamics pipeline changes, deal velocity, customer acquisition metrics, revenue attribution
• Advertising Platform Data: Google Ads, Meta Ads Manager, LinkedIn Campaign Manager spend and performance metrics
• Qualitative Source Materials: Meeting transcripts (Zoom, Teams, Google Meet), project management tools (Asana, Monday, Jira), email archives, Slack/Teams channel exports, strategic documents
• Financial Data: Marketing spend by channel and campaign, revenue attribution, customer acquisition costs, lifetime value calculations
• Contextual Information: Customer personas and profiles, competitive analysis documents, market research reports, business strategy documents, organizational goals and OKRs

Performance

We generate quarterly reviews that comprehensively analyze both activities and outcomes, enabling strategic leaders to make data-informed decisions about resource allocation and tactical priorities for the upcoming quarter. These reviews combine the completeness of AI-assisted recall with the interpretive context of human expertise, resulting in presentations that demonstrate clear understanding of what worked, why it worked, and how to improve performance. Organizations that implement this approach create a continuous improvement cycle where each quarter’s learnings systematically inform the next quarter’s strategy.

Key performance indicators for this use case:

1. Review Completeness Score: Measure the percentage of documented initiatives and activities from the quarter that appear in the final quarterly review, targeting 95%+ coverage to ensure we overcome recency bias and capture all relevant work rather than only recent or high-visibility projects.
2. Strategic Adjustment Rate: Track the number of prescriptive recommendations from quarterly reviews that leadership approves and implements in the subsequent quarter, targeting 60%+ implementation to demonstrate the actionability and relevance of AI-assisted analysis.
3. Quarter-over-Quarter Performance Improvement: Monitor the trend in primary business metrics (revenue, pipeline, lead quality, conversion rates) across quarters where we implement AI-assisted quarterly reviews, measuring whether the improved analytical depth correlates with improved business outcomes over a 12-month evaluation period.

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.