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:

Discover pattern/score in Scoop
Validate results
Push to CRM with one click
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