Insights

Insights are the primary way to create persistent visualizations and analyses in Altertable. They are saved analytics views that help you track key metrics, understand user behavior, and make data-driven decisions.

What are Insights?

Insights transform raw data into actionable visualizations by:

• Querying your lakehouse data using predefined logic
• Supporting multiple visualization types (charts, tables, metrics)
• Refreshing automatically or on-demand
• Being shareable with teams and embeddable in external tools

Prerequisites: Insights analyze data in your lakehouse. Start by tracking events and identifying users to capture the data you want to analyze.

Goals of Insights

• Track metrics over time: Monitor KPIs and business metrics continuously
• Understand behavior: Analyze user actions, conversions, and engagement
• Compare segments: Identify differences between user cohorts
• Inform decisions: Provide data-driven answers to business questions

Insight Types

Altertable offers several insight types, each tuned to a different analysis need.

1. Funnel Insights

Track user progression through sequential events to measure conversion rates and identify drop-offs.

Best for:

• Signup and onboarding flows
• Checkout and purchase processes
• Feature adoption journeys
• Multi-step workflows

Key metrics:

• Step-by-step conversion rates
• Overall funnel completion
• Drop-off points and rates
• Time to convert between steps

Common examples:

• Signup funnel: Landing page → Signup form → Email verification → Profile completed
• Checkout funnel: Product viewed → Added to cart → Checkout started → Purchase completed
• Feature adoption: Feature discovered → Feature clicked → Feature configured → Feature used

2. Retention Insights

Measure how groups that perform a starting event come back and perform a returning event across time buckets (classic cohort retention curves).

Best for:

• Onboarding and activation follow-up
• Repeat usage after a milestone event
• Comparing retention across segments defined in Segmented by

Key options:

• Measured as — return on a specific period only, or on that period or later
• Calculated by — 24-hour windows vs strict calendar day alignment

Learn more about retention insights →

3. Segmentation Insights

Group users into cohorts based on behavioral patterns and analyze differences between segments.

Best for:

• User cohort analysis
• Comparative behavior studies
• Engagement level tracking
• Comparing metrics across segments (for dedicated cohort retention curves, use Retention)

Common segments:

• By activity level (power users, regular users, casual users)
• By feature usage (adopters, explorers, unaware)
• By purchase behavior (recent, repeat, one-time)
• By acquisition channel or demographics

Example segments:

• Engagement: Users segmented by sessions per month (20+, 10-20, 1-10, inactive)
• Feature adoption: Users who adopted vs explored vs never used a feature
• Revenue: High-value, medium-value, low-value customers based on spend

Learn more about segmentation →

4. Semantic Insights

Query structured data models using business-friendly metrics and dimensions without writing SQL.

Best for:

• Business metric reporting
• KPI dashboards
• Cross-functional analytics
• Model-based queries

Key features:

• Pre-defined metrics (revenue, DAU, conversion rate)
• Reusable dimensions (region, plan type, category)
• No SQL knowledge required
• Centralized business logic

Key properties:

• Consistency: Shared metric definitions across teams
• Accessibility: Query without writing SQL
• Governance: Centralized business logic
• Reusability: Define once, reuse across dashboards and reports

Learn more about semantic insights →

5. SQL Insights

Execute custom SQL queries using DuckDB dialect for complete flexibility in data analysis.

Best for:

• Complex custom queries
• Ad-hoc analysis
• Advanced calculations (window functions, CTEs)

Capabilities:

• Full DuckDB SQL dialect support
• Cross-catalog federated queries
• Advanced analytics functions (window functions, CTEs, recursive queries)
• Complete query flexibility

Use cases:

• Custom retention calculations with specific business rules
• Complex multi-source joins (product events + CRM + revenue data)
• Advanced cohort analysis with custom time windows
• Performance analysis with moving averages and percentiles

Learn more about SQL insights →

Quick Decision Guide

Start with Funnels when:

• Tracking multi-step processes
• Measuring conversion rates
• Finding drop-off points

Use Segmentation when:

• Comparing user groups
• Analyzing cohort behavior
• Understanding engagement levels

Use Retention when:

• Measuring return after a specific starting event
• Plotting cohort survival over day or period offsets
• You need Return on vs Return on or after semantics

Choose Semantic when:

• Reporting standard business metrics
• Building KPI dashboards
• Enabling non-technical users

Go with SQL when:

• You need custom calculations
• You join multiple data sources
• You explore data ad hoc

Learn More

Use these pages to choose an insight type and configure it:

• Funnel Insights: Track user progression through sequential events
• Retention Insights: Cohort retention from a starting and returning event
• Segmentation Insights: Group users into cohorts based on behavior
• Semantic Insights: Query structured data models without SQL
• SQL Insights: Execute custom SQL queries for complete flexibility
• Product Analytics: Set up event tracking and user identification
• Analytical Database: Query your lakehouse with SQL
• Architecture: Understand Altertable's infrastructure
• AI Agents: Automate analytics with AI