Data Analysis Copilot

Here is a real example of Data Analysis Copilot helping a SaaS company calculate key metrics and diagnose churn:

You: I have a PostgreSQL database with our SaaS product data. I need to calculate monthly recurring revenue (MRR) and churn rate for the last 12 months. Our tables are subscriptions (id, user_id, plan_id, status, started_at, cancelled_at, amount_cents) and plans (id, name, interval, price_cents).

Data Analysis Copilot: Great, let me build those queries. For MRR, we need to sum all active subscription amounts at the end of each month. According to Stripe's SaaS metrics guide, MRR should include all recurring subscription revenue but exclude one-time charges, trials, and refunds. Here is the query:

```sql

WITH monthly_periods AS (

SELECT generate_series(

date_trunc('month', CURRENT_DATE - INTERVAL '12 months'),

date_trunc('month', CURRENT_DATE),

'1 month'::interval

) AS month_start

),

active_subs AS (

SELECT

mp.month_start,

SUM(s.amount_cents) / 100.0 AS mrr

FROM monthly_periods mp

JOIN subscriptions s ON s.started_at <= mp.month_start + INTERVAL '1 month'

AND (s.cancelled_at IS NULL OR s.cancelled_at > mp.month_start)

AND s.status != 'trialing'

GROUP BY mp.month_start

)

SELECT month_start, mrr,

ROUND((mrr - LAG(mrr) OVER (ORDER BY month_start)) / LAG(mrr) OVER (ORDER BY month_start) * 100, 2) AS mrr_growth_pct

FROM active_subs

ORDER BY month_start;

```

You: Nice. Now how about churn rate?

Data Analysis Copilot: For churn rate, we want the percentage of subscribers at the start of each month who cancelled during that month. This is gross revenue churn, which Bessemer Venture Partners considers one of the most important SaaS metrics:

```sql

WITH monthly_metrics AS (

SELECT

date_trunc('month', s.cancelled_at) AS churn_month,

SUM(s.amount_cents) / 100.0 AS churned_mrr

FROM subscriptions s

WHERE s.cancelled_at IS NOT NULL

AND s.cancelled_at >= CURRENT_DATE - INTERVAL '12 months'

GROUP BY date_trunc('month', s.cancelled_at)

)

SELECT

cm.churn_month,

cm.churned_mrr,

bom.mrr AS beginning_mrr,

ROUND(cm.churned_mrr / bom.mrr * 100, 2) AS churn_rate_pct

FROM monthly_metrics cm

JOIN active_subs bom ON bom.month_start = cm.churn_month

ORDER BY cm.churn_month;

```

A healthy SaaS churn rate is under 5% monthly for SMB products and under 2% for enterprise, according to benchmarks from ProfitWell's retention research. If yours is higher, we should dig into cohort analysis to find which customer segments are churning.

You: Our churn is 7.2%. Can you help me figure out why?

Data Analysis Copilot: A 7.2% monthly churn rate means you are losing roughly half your customers every year, which ChartMogul's SaaS benchmarks confirms is well above the median. Let us build a cohort analysis. We will segment by signup month and plan type to see if churn concentrates in specific cohorts:

```sql

WITH cohorts AS (

SELECT

s.id,

date_trunc('month', s.started_at) AS cohort_month,

p.name AS plan_name,

EXTRACT(MONTH FROM AGE(s.cancelled_at, s.started_at)) AS months_to_churn

FROM subscriptions s

JOIN plans p ON p.id = s.plan_id

WHERE s.started_at >= CURRENT_DATE - INTERVAL '12 months'

)

SELECT

cohort_month,

plan_name,

COUNT(*) AS total_subscribers,

COUNT(CASE WHEN months_to_churn <= 1 THEN 1 END) AS churned_month_1,

COUNT(CASE WHEN months_to_churn <= 3 THEN 1 END) AS churned_month_3,

ROUND(COUNT(CASE WHEN months_to_churn <= 1 THEN 1 END)::numeric / COUNT() 100, 1) AS churn_pct_month_1

FROM cohorts

GROUP BY cohort_month, plan_name

ORDER BY cohort_month, plan_name;

```

We should also check the time-to-churn distribution. If most cancellations happen within the first 30 days, that signals an onboarding problem. If they happen at months 3 to 6, it is a value realization issue. Research from the Product-Led Growth Collective shows that 40-60% of SaaS trial users never return after their first session, making early engagement the most critical retention lever. For deeper product analytics, our AI & ML Copilot can help you build predictive churn models.

SQL query writing is the most common starting point. The copilot handles everything from basic SELECT statements to complex analytical queries using window functions, recursive CTEs, and lateral joins. It writes queries for PostgreSQL, MySQL, BigQuery, Snowflake, and Redshift with dialect-specific syntax. According to the Stack Overflow Developer Survey, SQL is the third most commonly used programming language, yet most developers are self-taught and miss advanced optimization techniques like partition pruning, predicate pushdown, and materialized view strategies.

A/B test analysis is where many teams make costly mistakes. The Harvard Business Review reports that most companies that run A/B tests make at least one critical statistical error. The copilot ensures you calculate proper sample sizes before running tests (avoiding underpowered experiments that waste weeks of traffic), use the correct statistical test (chi-squared for conversion rates, t-test for continuous metrics, Mann-Whitney for non-normal distributions), and interpret results correctly including confidence intervals and practical significance versus statistical significance.

Data visualization guidance helps you choose the right chart type for your data and audience. The IEEE VIS community and researchers like Edward Tufte have established principles for effective data visualization that the copilot applies: bar charts for comparisons, line charts for trends, scatter plots for correlations, heatmaps for matrices. The copilot provides code for Matplotlib, Seaborn, Plotly, or guidance for Tableau, Looker, and Metabase. For database performance behind your dashboards, the Database Copilot optimizes the queries powering your visualizations.

Step 1: Share Your Data Context. Describe your database schema, data sources, or paste sample data. The copilot understands table structures, column types, and relationships. You can share CREATE TABLE statements, CSV headers, or just describe what data you have in plain English. Research from Google's Data Analytics Certificate program emphasizes that clearly defining your data context is the first step in any analysis project.

Step 2: Ask Your Business Question. Frame what you want to learn: "Which marketing channels have the highest LTV-to-CAC ratio?" or "Is our new pricing page actually converting better?" The copilot translates business questions into analytical approaches, selecting the right methods and metrics. According to the Data Science Association, the most valuable data analysts are those who can translate between business language and technical methodology, which is exactly what this copilot does.

Step 3: Get Code and Analysis. Receive ready-to-run SQL queries, Python scripts, or R code along with explanations of the methodology. The copilot explains statistical assumptions, potential pitfalls (like Simpson's paradox or survivorship bias), and how to interpret results. Every piece of code includes comments explaining the logic and handles edge cases like NULL values, timezone conversions, and data type mismatches.

Step 4: Iterate on Results. Paste your query results back and the copilot helps you interpret findings, suggests follow-up analyses, identifies anomalies, and helps you build a narrative for stakeholders. The MIT Sloan Management Review reports that data-driven decision making is only as good as the narrative that accompanies the analysis. Data analysis is iterative, and each answer usually generates better questions. Visit our How It Works page to learn more about the technology behind all our copilots.

Generic AI tools write SQL that looks correct but often fails on edge cases: NULL handling, timezone conversions, duplicate records, and dialect-specific functions. They might use DATEADD in a PostgreSQL query (which requires interval arithmetic) or ignore the difference between COUNT(*) and COUNT(DISTINCT column). A study by Towards Data Science found that over 30% of AI-generated SQL queries produce incorrect results due to subtle logic errors that are hard to spot.

Data Analysis Copilot writes queries that handle real-world data messiness. It accounts for NULLs with COALESCE, handles timezone conversions with AT TIME ZONE, warns you about integer division truncation (5/2 = 2 in SQL, not 2.5), and flags join fanouts that inflate your numbers. It also validates statistical assumptions before applying tests -- checking for normality with Shapiro-Wilk, equal variances with Levene's test, and independence of observations, following guidelines from the American Statistical Association.

For a comprehensive comparison across all domains, see how Copilotly compares to ChatGPT.

Product Managers who need to measure feature adoption, build user funnels, analyze retention cohorts, and present data-driven cases to leadership without waiting weeks for the data team's queue. According to Amplitude's Product Report, product teams that self-serve their analytics ship features 2x faster than those dependent on centralized data teams.

Marketing Analysts running campaign performance analysis, attribution modeling, A/B tests on landing pages, and ROI calculations across channels like Google Ads, Meta, and email campaigns. The Data & Marketing Association reports that data-driven marketing campaigns generate 5-8x ROI compared to campaigns based on intuition alone.

Startup Founders building investor dashboards with MRR, churn, LTV, CAC, and runway metrics. Investors expect data literacy, and the copilot helps you speak their language with accurate numbers. Y Combinator's essential startup metrics provide the framework, and this copilot helps you calculate each metric correctly.

Business Analysts creating reports, automating data pipelines, transitioning from Excel to SQL/Python, and building self-serve analytics capabilities for their organizations. The International Institute of Business Analysis (IIBA) notes that business analysts who can write SQL and basic Python are 40% more productive than those relying solely on spreadsheets.

Students and Career Changers learning data analysis skills with real-world examples rather than textbook exercises. The Bureau of Labor Statistics reports the median salary for data scientists at $103,500 per year, making data skills among the most valuable career investments. The copilot teaches by doing, explaining concepts in the context of actual analysis problems.

Explore specialized copilots that complement your data analysis work:

• Database Copilot - Schema design, query optimization, indexing strategies, and database performance tuning to make your analytical queries run faster
• AI & ML Copilot - When your analysis evolves into predictive modeling, classification, recommendation systems, or natural language processing
• Cloud Architecture Copilot - Set up data warehouses (Snowflake, BigQuery, Redshift), ETL pipelines, and analytics infrastructure on AWS/Azure/GCP
• DevOps Copilot - Automate data pipelines with Airflow, dbt, and CI/CD for analytics code with proper testing
• Cybersecurity Copilot - Data privacy compliance (GDPR, CCPA), PII handling, and secure data practices for sensitive datasets
• Math Copilot - Advanced mathematical concepts behind statistical models, linear algebra for ML, and probability theory

Looking for help in a different area? Browse our complete copilot directory or see how Copilotly compares to ChatGPT across all domains.

Free Plan: Ask basic data analysis questions, get help with simple SQL queries, and learn fundamental statistical concepts. Great for exploring what the copilot can do. No credit card required.

Pro Plan ($29/month): Unlimited analysis sessions covering complex SQL, Python data pipelines, statistical testing, visualization code, and methodology guidance. A single hour with a data consultant costs $100 to $250, making the Pro plan equivalent to about 15 minutes of professional time. You also get context retention across sessions so the copilot remembers your schema and metrics definitions.

Enterprise Plan: Custom pricing for teams needing shared analytics contexts, integration with internal data dictionaries, and support for proprietary data platforms. Includes team collaboration features and priority support. Contact us for pricing.

The ROI of Better Data Decisions: McKinsey research estimates that data-driven organizations generate 5-6% higher output and productivity than their competitors. A single well-designed A/B test can increase conversion rates by 10-30%, translating to thousands or millions in additional revenue. A properly optimized SQL query can reduce infrastructure costs by eliminating unnecessary compute. Data Analysis Copilot pays for itself with the first insight that drives a better business decision.

Your data is already telling a story. Data Analysis Copilot helps you read it. See all pricing details or get started for free.