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Recipes · Legacy self-service product

Understanding Recipes

Pre-built analytics workflows that turn raw data into actionable insights in minutes

You're viewing documentation for the legacy Scoop self-service product. For the current product, see What Is Scoop.

Recipes in Scoop Analytics are pre-built, customizable analytics workflows that transform raw data into complete, ready-to-use dashboards and presentations. Instead of building analysis from scratch, start with a proven template and customize it to your needs.

What Are Recipes?

Recipes are complete analytics packages that include:

Component Description
Data Requirements What fields your data needs
Transformations Calculations and aggregations applied
Visualizations Charts, tables, and KPIs pre-configured
Canvas/Presentation Ready-to-use dashboard layout
AI Insights Automated analysis and recommendations

How Recipes Work

Your Data → Recipe → Complete Analytics
   ↓           ↓            ↓
 Raw CSV    Template    Dashboard + Insights
   or       with        ready in minutes
 API Data   logic

The Recipe Process

  1. Select a recipe matching your analysis goal
  2. Connect your data (file upload or app connection)
  3. Map fields to recipe requirements
  4. Generate complete canvas with visualizations
  5. Customize as needed (optional)

Available Recipe Categories

Sales & Pipeline

Recipe What It Does Data Source
Sales Forecasting Pipeline analysis, forecast accuracy, deal velocity CRM opportunities
Pipeline Waterfall Track adds, removes, wins, losses, resizes CRM opportunities
Rep Performance Individual and team sales metrics CRM opportunities + users
Win/Loss Analysis Patterns in won vs. lost deals CRM opportunities

Marketing

Recipe What It Does Data Source
Cost Per Lead Marketing efficiency by channel/campaign Marketing + Finance
Campaign Performance Campaign ROI and attribution Marketing platform
Lead Conversion Lead to opportunity conversion rates Marketing + CRM
Funnel Analysis Stage-by-stage conversion rates CRM leads/opportunities

Customer Success

Recipe What It Does Data Source
Customer Health Health scores and risk indicators Product usage + CRM
Churn Analysis Patterns in churned vs. retained customers Subscription data
Support Operations Ticket metrics and agent performance Support platform
NPS Analysis Score trends and driver analysis Survey data

Operations

Recipe What It Does Data Source
Inventory Analysis Stock levels, turnover, reorder alerts Inventory system
Order Fulfillment Order processing times and bottlenecks Order management
Resource Utilization Team workload and capacity Project management

Why Use Recipes?

Benefit Description
Speed Complete analytics in minutes, not days or weeks
Best Practices Built on proven analytical frameworks
Consistency Standardized approach across teams
Accuracy Pre-tested calculations reduce errors
Customizable Adjust anything after generation
Reusable Apply same recipe to updated data

Recipes vs. Building from Scratch

Aspect Recipe From Scratch
Time to value Minutes Hours to days
Expertise needed Minimal Moderate to high
Best practices Built-in Must know them
Customization After generation During build
Maintenance Recipe updates available Manual updates

Using a Recipe: Step-by-Step

Step 1: Choose Your Recipe

Navigate to Recipes in Scoop and browse available options:

  • Filter by category (Sales, Marketing, etc.)
  • Read recipe descriptions
  • Check data requirements

Step 2: Connect Data Source

Each recipe specifies required data:

Connection Type Example
SaaS Connector Salesforce, HubSpot, Jira
File Upload CSV or Excel with required columns
Existing Dataset Previously loaded Scoop data

Step 3: Map Fields

Match your data fields to recipe requirements:

Recipe Expects Your Field Name
Opportunity ID deal_id
Amount contract_value
Stage sales_stage
Close Date expected_close

Scoop automatically suggests mappings based on column names.

Step 4: Generate Canvas

Click Generate and Scoop creates:

  • All calculated columns and metrics
  • Configured visualizations
  • Laid-out canvas with interactive elements
  • AI-generated insights

Step 5: Review and Customize

The generated canvas is fully editable:

  • Move or resize visualizations
  • Add additional charts
  • Modify calculations
  • Apply your visual theme

Recipe Deep Dive: Sales Forecasting

The Sales Forecasting recipe demonstrates how recipes work in practice.

Required Data

A single CRM export with these fields:

Field Purpose Example
Opportunity ID Unique identifier for snapshotting OPP-001
Opportunity Name Human-readable reference Acme Corp Deal
Amount Deal value $50,000
Stage Current pipeline stage Proposal
Owner Assigned rep Jane Smith
Expected Close Forecast date 2024-03-15
Created Date When opened 2024-01-01

What the Recipe Generates

Visualizations

Chart Shows
Pipeline Waterfall Adds, wins, losses, resizes
Stage Funnel Conversion rates between stages
Forecast vs. Actual Prediction accuracy over time
Deal Velocity Time in each stage
Rep Performance Pipeline by rep

Calculated Metrics

Metric Calculation
Win Rate Won / (Won + Lost)
Average Deal Size Sum(Amount) / Count(Won)
Average Sales Cycle Days from Create to Close
Pipeline Coverage Pipeline / Quota

AI Insights

  • Deals at risk of slipping
  • Stages with bottlenecks
  • Forecast accuracy trends
  • Rep performance patterns

Customizing After Generation

Once generated, you can:

  • Add filters for specific segments
  • Include additional metrics
  • Blend with financial data
  • Create derived analyses

Customizing Recipes

During Setup

Customization How
Rename fields Map any column name to requirements
Filter data Include only relevant records
Select date range Focus on specific period

After Generation

Everything in the generated canvas is editable:

Element Customization Options
Charts Change type, colors, labels
Calculations Modify formulas, add new metrics
Layout Move, resize, add elements
Filters Add interactive filter controls
Theme Apply your brand colors

Extending Recipes

Recipes are starting points. Extend them by:

  • Blending additional data sources
  • Adding calculated columns
  • Creating new visualizations
  • Building custom KPIs

Creating Custom Recipes

For recurring analysis patterns:

  1. Build your analysis using standard Scoop tools
  2. Document requirements (data fields, calculations)
  3. Save as template for reuse
  4. Share with team for standardization

Recipe Best Practices

Data Preparation

Practice Why
Clean data first Recipes work best with quality data
Include all fields More fields enable more analysis
Use consistent naming Easier field mapping
Check data types Dates as dates, numbers as numbers

Field Mapping

Tip Description
Review suggestions Scoop auto-maps similar names
Map all required fields Don't skip required fields
Include optional fields Enables additional analysis

After Generation

Tip Description
Review metrics Verify calculations match expectations
Check visualizations Ensure data displays correctly
Test interactions Confirm filters and drills work
Save before editing Preserve original as backup

Troubleshooting

Recipe Won't Generate

  • Verify all required fields are mapped
  • Check data has rows matching requirements
  • Ensure date fields are recognized as dates

Results Look Wrong

  • Review field mappings for accuracy
  • Check for data quality issues
  • Verify expected values in source data

Missing Visualizations

  • Some charts require specific data patterns
  • Check if required fields have data
  • Review minimum data requirements

Related Topics

Updated October 19, 2026