Analysis Patterns

These patterns show how to ask for analysis, audits, and insights. Start with these examples and adapt them to your needs.

Email Performance Analysis

Basic analysis:

Analyze my email performance for the last 30 days and identify
which campaigns had the lowest engagement

With comparison:

Compare email performance across all campaigns in Q4. Show me
what's working and what's not, with specific recommendations.

Trend analysis:

Show me how our email open rates have changed month over month
this year. Are we trending up or down? What's driving the trend?

Segment-level:

Break down email engagement by customer segment. Which segments
respond best to which types of content?

Pipeline Analysis

At-risk deals:

Analyze my sales pipeline and identify deals at risk of slipping.
What are the warning signs and what should we do about them?

Pipeline health:

Create a pipeline health report showing conversion rates at each
stage. Where are we losing deals?

Forecasting:

Based on historical patterns, predict which open opportunities
are most likely to close this quarter.

Tech Debt Audits

Marketing Cloud:

Run a tech debt audit on my Marketing Cloud account. Find unused
data extensions, broken journeys, and outdated email templates.
Prioritize what to clean up first.

Salesforce org:

Audit my Salesforce org for technical debt. Look for unused custom
fields, outdated automation, and duplicate processes.

Data Cloud:

Audit my Data Cloud setup. Are there data streams that haven't
run recently? Segments that aren't being used? Identity resolution
rules that might be outdated?

Tech debt audits work best when you ask for prioritization. "What should I clean up first?" helps focus on high-impact changes.

Data Quality Assessment

Contact data:

Analyze the quality of our contact data. What percentage have
valid emails? How many duplicates do we have? What's missing?

Data completeness:

Check how complete our customer records are. For each key field,
show me what percentage of records have data.

Consistency:

Look for data consistency issues across Sales Cloud and Marketing
Cloud. Are customer records in sync? Where are the gaps?

Security and Compliance

Access review:

Audit user permissions in my Salesforce org. Who has admin access?
Are there users with permissions they don't need?

Sharing rules:

Review our sharing settings. Is sensitive data properly restricted?
Are there any overly permissive rules?

Field-level security:

Check field-level security for our custom objects. Are PII fields
properly protected?

Performance Reporting

Executive summary:

Create an executive summary of our marketing performance this
quarter. Include key metrics, wins, and areas for improvement.
Keep it to one page.

Dashboard creation:

Build a dashboard showing our key sales metrics: pipeline value,
win rate, average deal size, and sales cycle length.

Attribution analysis:

Analyze which marketing channels are driving the most revenue.
Include first-touch and last-touch attribution.

Customer Analysis

Churn prediction:

Identify customers at risk of churning. Look at engagement drop-off,
support tickets, and usage patterns.

Lifetime value:

Calculate customer lifetime value by segment. Who are our most
valuable customers and what do they have in common?

Behavior patterns:

Analyze customer behavior patterns before their first purchase.
What actions predict conversion?

Configuration Analysis

Flow review:

Review all active Flows in my org. Which ones have errors?
Which ones haven't fired in 90 days?

Automation overlap:

Check for automation that might be overlapping or conflicting.
Are there multiple automations triggered by the same events?

Integration health:

Review our integrations with external systems. Are they running
successfully? Any errors or failures?

How to Get Better Analysis

Be specific about time ranges:

Analyze email performance for Q4 2024, not just "recently"

Ask for comparisons:

Compare this quarter to last quarter, not just show current metrics

Request recommendations:

Don't just show me the data — tell me what to do about it

Prioritize findings:

If there are many issues, tell me which ones to fix first