I've loaded your file "sales_data.csv". It contains 5,247 rows and 8 columns:
- Date
- Region
- Product
- Sales_Rep
- Revenue
- Units_Sold
- Cost
- Customer_Type

What would you like to analyze?

Analyze this sales data and tell me:
1. Overall revenue trend (is it growing or declining?)
2. Which region is performing best and worst?
3. What are the top 5 products by revenue?
4. Are there any seasonal patterns?

Create visualizations to illustrate your findings.

I've analyzed your sales data. Here are the key findings:

1. REVENUE TREND
   - Overall trend: Declining (-8.3% from Q1 to Q3)
   - Q1 2025: $1.2M
   - Q2 2025: $1.1M  
   - Q3 2025: $1.1M
   
   [Line chart showing monthly revenue decline]

2. REGIONAL PERFORMANCE
   Best: West Region ($425K total, +12% growth)
   Worst: Northeast Region ($198K total, -23% decline)
   
   The Northeast decline is driving the overall downward trend.
   
   [Bar chart comparing regions]

3. TOP 5 PRODUCTS
   1. Premium Widget Pro: $287K (24% of total)
   2. Standard Widget: $215K (18%)
   3. Widget Lite: $178K (15%)
   4. Enterprise Widget Suite: $156K (13%)
   5. Widget Plus: $134K (11%)
   
   [Horizontal bar chart of top products]

4. SEASONAL PATTERNS
   - Strong peak in January (post-holiday)
   - Slump in July-August (summer)
   - Recovery starting in September
   
   This pattern is consistent across years, suggesting seasonal buying behavior.
   
   [Line chart with seasonal decomposition]

KEY INSIGHTS:
- Focus on understanding Northeast region's decline (interviewed reps?)
- West region strategies should be replicated elsewhere
- July/August slump could be mitigated with summer promotions
- Premium products drive 60% of revenue but only 25% of volume

I want to understand what drives high revenue sales. 

Analyze relationships between:
- Revenue and product type
- Revenue and customer type  
- Revenue and sales rep
- Revenue and day of week / time of month

Find any interesting patterns or correlations I should know about.

Here's what I found about high-revenue sales:

1. PRODUCT TYPE CORRELATION
   Strong correlation (r=0.78): Premium products have 3.2x higher average revenue per sale.
   
   Insight: Premium products are sold to larger customers with bigger budgets.

2. CUSTOMER TYPE PATTERN
   - Enterprise customers: $8,750 avg order (52% of revenue)
   - SMB customers: $1,200 avg order (31% of revenue)
   - Individual customers: $380 avg order (17% of revenue)
   
   [Box plot showing distribution]

3. SALES REP PERFORMANCE
   Top 20% of reps generate 65% of revenue (Pareto principle).
   
   Top 3 reps:
   - Sarah Johnson: $287K (focus: Enterprise, West region)
   - Mike Chen: $245K (focus: Enterprise, premium products)
   - Lisa Rodriguez: $198K (focus: SMB, high volume)
   
   Insight: Enterprise-focused reps vastly outperform volume-focused reps.

4. TIMING PATTERNS
   
   Day of week:
   - Tuesday & Wednesday have highest close rates (42%)
   - Friday has lowest close rate (18%)
   
   Time of month:
   - First 10 days: 58% of monthly revenue
   - Last 10 days: Only 15% of revenue
   
   Insight: Buyers make decisions early in month (budget availability?).

RECOMMENDATIONS:
1. Train all reps on Sarah Johnson's enterprise sales approach
2. Schedule important demos for Tuesday/Wednesday
3. Focus prospecting efforts on first week of each month
4. Develop premium product upsell strategy for existing SMB customers

Based on this data, predict next quarter's revenue.

Also tell me:
- What factors most influence revenue?
- What's the confidence level of your prediction?
- What scenarios could cause significant variation?

I've built a predictive model using your historical data.

REVENUE FORECAST - Q4 2025
Predicted: $1.15M (±$85K)
Confidence interval: $1.07M to $1.23M (90% confidence)

This assumes:
- Current trends continue
- No major market disruptions
- Seasonal patterns hold

KEY REVENUE DRIVERS (by importance):
1. Number of enterprise deals (45% of variance)
2. Premium product sales (28%)
3. West region performance (18%)
4. Overall sales team productivity (9%)

[Chart showing feature importance]

SCENARIO ANALYSIS:

Best case ($1.35M): 
- Northeast turnaround begins
- 2 additional enterprise deals close
- Holiday season boost in December

Expected case ($1.15M):
- Status quo continues
- Normal seasonal patterns

Worst case ($950K):
- Northeast continues declining
- One major customer churns
- Delayed enterprise deals

RECOMMENDATION:
Focus on closing 2-3 enterprise deals in first month of Q4 to derisk the quarter. Even if other metrics underperform, landing these deals puts you in good shape.

Segment my customers into groups based on their buying behavior.

I want to understand:
- How many segments make sense?
- What characterizes each segment?
- How should I market to each segment differently?

I've performed clustering analysis and identified 4 distinct customer segments:

SEGMENT 1: "High-Value Enterprises" (12% of customers, 48% of revenue)
Characteristics:
- Average order: $15,250
- Purchase frequency: Quarterly
- Products: Enterprise Suite, Premium packages
- Decision cycle: 60-90 days
- Price sensitivity: Low

Marketing approach:
- White-glove service
- Dedicated account managers
- Custom solutions/pricing
- Executive-level relationships

SEGMENT 2: "Growing SMBs" (28% of customers, 32% of revenue)
Characteristics:
- Average order: $2,400
- Purchase frequency: Monthly
- Products: Standard & Plus packages
- Decision cycle: 14-30 days
- Price sensitivity: Moderate

Marketing approach:
- Success stories from similar companies
- ROI calculators
- Scalable solutions emphasis
- Upgrade paths to enterprise

SEGMENT 3: "Budget-Conscious Buyers" (45% of customers, 16% of revenue)
Characteristics:
- Average order: $480
- Purchase frequency: Sporadic
- Products: Lite packages, single units
- Decision cycle: 1-7 days
- Price sensitivity: High

Marketing approach:
- Discounts and promotions
- Self-service options
- Bundle deals
- Annual prepay discounts

SEGMENT 4: "One-Time Buyers" (15% of customers, 4% of revenue)
Characteristics:
- Average order: $350
- Purchase frequency: One-time only
- Products: Individual items
- Decision cycle: Immediate
- Price sensitivity: Very high

Marketing approach:
- Minimize acquisition cost
- Automated onboarding
- Upsell in cart
- Referral incentives

[Scatter plot showing segments in 2D space]

STRATEGIC RECOMMENDATIONS:
1. Invest 70% of sales resources in Segments 1 & 2
2. Create automated nurture campaigns for Segment 3
3. Minimize spending on Segment 4 (low lifetime value)
4. Develop upgrade program to move Segment 2 → Segment 1

This dataset has problems:
- Missing values in several columns
- Duplicate entries
- Date formats are inconsistent
- Some text fields have typos or variations

Clean this data and show me:
1. What problems you found
2. How you fixed them
3. Summary of changes made

I'm uploading two files:
1. sales_data.csv (transaction data)
2. customer_info.csv (customer demographics)

Merge these files on customer_id and analyze:
- Does customer age correlate with purchase amount?
- Do certain industries buy more premium products?
- What's the lifetime value by customer segment?

This data contains daily website traffic for the past year.

Create a forecast for the next 30 days. Include:
- Predicted values with confidence intervals
- Identification of trends and seasonality
- Anomaly detection (unusual spikes or drops)
- What might cause forecast to be wrong?

I ran an A/B test:
- Control group: 5,000 users, 450 conversions
- Treatment group: 5,000 users, 523 conversions

Is this difference statistically significant?
What's the confidence interval?
Should I roll out the treatment to everyone?

Analyze customer cohorts based on signup month.

Show me:
- Retention rates by cohort
- Revenue per cohort over time
- Which cohorts are most valuable?
- Are recent cohorts better or worse than older ones?

Create a cohort retention heatmap.

Analyze this sales data and provide:
1. Month-over-month growth rate
2. Top and bottom performing [regions/products/reps]
3. Seasonality patterns
4. Forecast for next [month/quarter]
5. Recommendations to improve underperforming areas

Create visualizations to support your findings.

Analyze campaign performance:
1. Which channels have the best ROI?
2. What's the customer acquisition cost by channel?
3. Are there diminishing returns at higher spend levels?
4. Recommend budget allocation for next month

Show cost per acquisition and conversion rate by channel.

Analyze customer behavior:
1. Segment customers by value and engagement
2. Calculate customer lifetime value by segment
3. Identify churn risk factors
4. Which customers should we prioritize for retention efforts?

Create a customer value matrix (RFM analysis).

Analyze expenses:
1. Where are we overspending vs budget?
2. What's driving cost increases?
3. Are there any unusual transactions?
4. Forecast expenses for next quarter

Highlight areas needing immediate attention.

I think our premium products sell better in Q4 due to year-end budgets.

Test this hypothesis:
1. Compare Q4 vs other quarters for premium vs standard products
2. Is the difference statistically significant?
3. Does this pattern hold across years?
4. Are there other factors at play?

Compare performance across these dimensions:
- New vs returning customers
- Direct vs channel sales
- Industry verticals

Which combination drives the highest revenue?
Create a matrix showing avg revenue by segment combination.

Identify unusual patterns in this data:
- Unusually high/low revenue days
- Products with sudden sales spikes or drops
- Customers with abnormal buying patterns

For each anomaly, suggest possible explanations.