AI Analytics & Insights Playbook

AI ANALYTICS USE CASES

Use Case	AI Application	Value
Pattern detection	Find anomalies, trends	Proactive insights
Predictive analysis	Forecast performance	Better planning
Attribution	Multi-touch modeling	Accurate credit
Segmentation	Cluster analysis	Better targeting
Reporting	Automated generation	Time savings
Q&A	Natural language queries	Accessibility

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AI Analytics Tools

Tool	Best For	Capabilities
ThoughtSpot	Natural language queries	Search-driven BI
Tableau (AI)	Visual analytics	Explain Data, Ask Data
Power BI (AI)	Microsoft ecosystem	Q&A, Anomaly detection
Amplitude	Product analytics	Predictive cohorts
Google Analytics 4	Web analytics	Predictive metrics

Selection Criteria

Criterion	Weight	Questions
Data integration	High	Connects to our sources?
Ease of use	High	Non-technical users?
AI capabilities	Medium	What AI features?
Cost	Medium	Per-user or flat?
Security	High	SOC 2, data handling?

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DATA PREPARATION

Data Requirements

Requirement	Why	How
Clean data	AI amplifies garbage	Data quality process
Unified IDs	Cross-source analysis	Identity resolution
Historical data	Pattern recognition	12+ months preferred
Real-time access	Current insights	Streaming/API

Data Quality Checklist

• No duplicate records
• Consistent naming conventions
• Null values handled
• Date formats standardized
• Metrics definitions documented

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ANALYSIS METHODOLOGIES

AI-Assisted Analysis Workflow

1. Define question → 2. Prepare data →
3. Apply AI analysis → 4. Validate findings →
5. Translate to action → 6. Monitor impact

Analysis Prompts

Trend Analysis:

Analyze this marketing data for trends:

[paste data or describe]

Identify:
- Overall trend direction
- Seasonal patterns
- Anomalies or outliers
- Correlation with events
- Predicted trajectory

Performance Analysis:

Compare performance across these campaigns:

[paste data]

Provide:
- Top/bottom performers
- Key differentiating factors
- Statistical significance
- Recommended actions

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INSIGHT VALIDATION

Validation Framework

Check	Question	Action
Statistical	Is this significant?	Calculate p-value
Logical	Does this make sense?	Expert review
Historical	Consistent with past?	Compare periods
External	Explained by events?	Context check

Validation Process

1. AI generates insight
2. Human reviews logic
3. Verify with source data
4. Check for confounders
5. Confirm actionability

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ACTION TRANSLATION

Insight to Action Template

Insight	So What?	Now What?
[Finding]	[Impact/implication]	[Specific action]

Action Criteria

Criterion	Question
Actionable	Can we do something about it?
Impactful	Will action move metrics?
Feasible	Do we have resources?
Timely	Is this still relevant?

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AI-POWERED REPORTING

Automated Report Elements

Element	AI Role
Data pull	Automated
Visualization	Auto-suggested
Narrative	AI-generated draft
Insights	AI-highlighted
Recommendations	AI-suggested

Report Prompt

Generate an executive summary of this marketing data:

[paste data]

Include:
- 3-5 key takeaways
- Performance vs. targets
- Notable trends
- Recommended actions
- Forecast for next period

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