# Advanced Features

## Machine Learning Capabilities

Scoop for Slack includes powerful ML features accessible through natural language:

### Pattern Discovery

Find hidden patterns in your data:

```
@Scoop what patterns exist in customer behavior?
@Scoop find segments in our user base
@Scoop discover what makes customers valuable
```

!\[Screenshot: ML pattern discovery results showing customer segments]

### Predictive Analytics

Make data-driven predictions:

```
@Scoop what factors predict customer churn?
@Scoop which deals are likely to close this month?
@Scoop forecast revenue for next quarter
```

### Segment Analysis

Automatically discover and understand groups:

```
@Scoop find customer segments
@Scoop what defines each segment?
@Scoop which segment is most profitable?
```

!\[Screenshot: Interactive segment visualization with bubble chart]

## Time-Based Intelligence

### Period Comparisons

Compare different time periods intelligently:

```
@Scoop compare this week to last week
@Scoop what changed between Q3 and Q4?
@Scoop show me year-over-year growth
```

### Anomaly Detection

Identify unusual patterns:

```
@Scoop find anomalies in the data
@Scoop what's unusual about last month?
@Scoop alert me to outliers
```

!\[Screenshot: Anomaly detection highlighting unusual data points]

### Trend Analysis

Understand directional changes:

```
@Scoop analyze trends over time
@Scoop is this growth sustainable?
@Scoop when did the trend change?
```

## Group Comparisons

### A/B Analysis

Compare different groups:

```
@Scoop compare enterprise vs SMB customers  
@Scoop analyze test group vs control group
@Scoop what's different about high-performers?
```

### Cohort Analysis

Track groups over time:

```
@Scoop analyze customer cohorts by signup month
@Scoop show retention by cohort
@Scoop which cohort performs best?
```

## Advanced Querying

### Complex Filters

Chain multiple conditions:

```
@Scoop show enterprise customers in APAC with >$100k revenue who signed up last quarter
```

### Calculated Metrics

Create on-the-fly calculations:

```
@Scoop calculate customer lifetime value
@Scoop show profit margin by product
@Scoop compute year-over-year growth rate
```

### Multi-Dimensional Analysis

Analyze across multiple dimensions:

```
@Scoop break down revenue by product, region, and customer type
@Scoop show me conversion rates by source and campaign
```

!\[Screenshot: Multi-dimensional analysis visualization]

## Integration Features

### CRM Writeback

Push insights back to your CRM:

```
@Scoop identify at-risk customers
@Scoop score leads for sales priority
@Scoop tag customers by segment
```

Process:

1. Discover pattern/score in Scoop
2. Validate results
3. Push to CRM with one click
4. Automate actions based on scores

!\[Screenshot: CRM writeback confirmation]

### Automated Monitoring

Set up intelligent alerts:

```
@Scoop alert me when metrics drop 10%
@Scoop monitor for anomalies daily
@Scoop track KPIs and notify changes
```

## Workspace Features

### Cross-Dataset Analysis

Combine multiple data sources:

```
@Scoop join customer data with support tickets
@Scoop correlate marketing spend with revenue
@Scoop combine Salesforce and Zendesk data
```

### Personal Workspace

Your private analytics environment:

* Upload multiple files
* Save favorite queries
* Build custom reports
* Test before sharing

### Team Collaboration

Advanced team features:

* Share discovered segments
* Collaborative analysis sessions
* Knowledge preservation
* Insight attribution

## Power User Tips

### 1. Query Templates

Save and reuse complex queries:

```
Daily: "show me yesterday's KPIs compared to average"
Weekly: "summarize this week's performance with ML insights"  
Monthly: "full analysis with segments and predictions"
```

### 2. Iterative Refinement

Build complex analyses step by step:

```
Step 1: show all data
Step 2: filter to key segments
Step 3: add time comparison
Step 4: apply ML analysis
Step 5: generate predictions
```

### 3. Context Preservation

Scoop remembers your analysis context:

```
You: analyze customer churn
Scoop: [shows churn analysis]
You: dig deeper into enterprise segment
Scoop: [maintains churn context, focuses on enterprise]
You: what actions should we take?
Scoop: [provides recommendations based on full context]
```

!\[Screenshot: Contextual conversation flow]

## ML Model Transparency

### Understanding Results

Scoop explains ML findings in business terms:

```
"I found 4 distinct customer segments based on behavior patterns:

1. **High-Value Engaged** (23%): Order frequently, high AOV, low support needs
2. **Price Sensitive** (31%): Wait for discounts, lower AOV, moderate frequency
3. **Support Heavy** (19%): Average orders but high support contact rate
4. **Dormant Risk** (27%): Haven't ordered in 60+ days, declining engagement"
```

### Model Details

Get technical details when needed:

```
@Scoop explain the model
@Scoop show decision tree
@Scoop what's the accuracy?
```

!\[Screenshot: Decision tree visualization]

## Advanced Workflows

### Executive Briefing Prep

```
1. @Scoop summarize last month's performance
2. @Scoop identify key wins and challenges  
3. @Scoop predict next month's outlook
4. @Scoop create executive dashboard
5. @Scoop export to PowerPoint format
```

### Customer Intelligence

```
1. @Scoop segment our customer base
2. @Scoop profile each segment
3. @Scoop predict churn risk by segment
4. @Scoop recommend retention strategies
5. @Scoop push scores to CRM
```

### Market Analysis

```
1. @Scoop analyze sales by region and product
2. @Scoop find growth opportunities
3. @Scoop compare to historical performance
4. @Scoop forecast demand by market
5. @Scoop visualize expansion priorities
```

## Performance Optimization

### Large Dataset Handling

Scoop efficiently handles big data:

* Automatic sampling for preview
* Progressive loading
* Smart aggregations
* Optimized queries

### Query Optimization

Tips for faster results:

* Be specific with filters
* Use date ranges when possible
* Start with aggregations
* Drill down as needed

## Next: [FAQ & Troubleshooting](faq.md)