Rows transforms how teams work with data: it's powered by AI, connected to your business data, and built to be shared and consumed. Whether you're a beginner who needs quick answers or an expert data-curious seeking code-level insights,
It's no surprise that tens of thousands of businesses around the world use Rows every day.
Used by companies of all sizes
From startups to enterprises, Rows is the spreadsheet of choice for data-driven teams. Some of the companies using Rows include:
- L'Oréal: Uses Rows to track the SEO performance of 374 websites across 13 brands. They master SEO reporting across a global beauty empire
- Mercado Libre: They automated Facebook Ads analytics for millions of products with secure 30-minute data pulls across 35+ campaigns.
- Brrrr: They automate mortgage rate comparisons, improving its efficiency and win more business. This makes loan comparison faster - more flexibly with Rows
- Monta: Rows powers Monta's CRM reports and make it fully automated
...and many more.
Built for teams of all skill levels
Rows is designed to support everyone—from those new to spreadsheets to seasoned analysts:
For teams with little spreadsheet experience: No need to learn formulas, AI takes care of the technical part so you can focus on what matters. Just ask questions in plain English and get quick answers—forecasts, summaries, and insights.
For spreadsheet experts: With Python built in, you can access code-level analytics and advanced data science capabilities within a familiar spreadsheet interface and without writing a line of code.
Global reach
Rows has customers across the globe:
- USA: Marketers and GTM teams use Rows for reporting and automation, especially with integrations like Google Ads, Meta, and HubSpot.
- Europe: Product, finance, and operations teams rely on Rows to automate workflows and centralize data, with strong adoption in Germany, France, the UK, and Portugal.
- Latin America: Rows is popular with agencies and e-commerce businesses for blending data from CRMs, payment providers etc.
How Rows transforms your data work
Rows makes it easier to go from raw data to insights people actually use. It covers the full workflow in three steps:
1. Bring your data in: Connect to over 50 popular tools including Google Ads, HubSpot, Stripe, and more. You can also make custom HTTP requests to any API endpoint, or import data directly from PDFs and images—like receipts, invoices, or screenshots. Discover all our integrations.
2. Turn raw data into insights: Use plain language instead of formulas to analyze your data. Rows AI can generate pivot tables, add calculated columns, create charts, and even merge datasets automatically. You can also run forecasts, detect anomalies, and perform advanced analytics using Python—no coding required. Discover more at rows.com/ai .
3. Share insights people actually use: Build live dashboards and reports that update automatically and are easy to share with your team. No more static spreadsheets or outdated charts—just clean, up-to-date views that anyone can access.
Use cases mapping by persona
Here are how AI in Rows can help with common spreadsheet tasks roles — each table below shows practical spreadsheet use cases tailored to a specific persona.
General business analyst
| Use Case | Input Data | User Prompt |
|---|---|---|
| Extract information from text | A column with messy text, like Order #12345 by customer XYZ. | “Extract the invoice numbers from the description into a new column.” |
| Automatically categorize items based on keywords | A list of products with descriptions (e.g., Red Running Shoes). | “Categorize these products into ‘Office equipment’, ‘Computers’, and ‘Accessories’ based on Product name and Description.” |
| Find the most frequently occurring value (mode) in a column | A list of purchase categories for a set of customers. | “What's the most common category in this list.” |
| Filter based on multiple conditions | A dataset with sales transactions, including amount, region, and date. | “Filter this table to show only transactions above $500 from Australia in the last 3 months.” |
| Generate a summary of key statistics | A table with numerical data (e.g., sales figures per region). | “Summarize this data by showing the total, average, min, and max for each region.” |
| Identify outliers in a dataset | A list of sales transactions with unusually high or low amounts. | “Identify any transactions that are significantly higher or lower than average.” |
| Pivoting data | Dataset with campaign, month, device, and KPIs | “Give me a view of Purchases per campaign per month, only for mobile users.” |
| Sort based on multiple conditions | Product usage and billing data. | “Sort this table by number of active days first (descending), and by pricing plan (A to Z) second.” |
| Apply conditional formatting | Marketing campaigns per campaign, month, and device type. | “Mark all rows with a CTR >1% red.” |
| Check if a value from Sheet1 is in Sheet2 | Two sheets with emails | “Check if the emails in Sheet1 exist in Sheet2. Add a column indicating if they are found.” |
| Merge two tables based on a common key | Customer orders and customer details | “Merge these two sheets on Customer ID and create a single table.” |
| Automated Data Cleaning & Enrichment | Raw, unstructured, or incomplete datasets | “Standardize customer data by normalizing names, removing duplicates, and filling missing values.” |
| Automated Report Generation | Data from multiple departments | “Generate a weekly executive summary report with insights on key business metrics.” |
| Aggregate data conditionally | Marketing campaigns per month | “Give me the total revenue of the 'Summer Sale' campaign.” |
Marketing analyst
| Use Case | Input Data | User Prompt |
|---|---|---|
| Compare Campaign Performance across channels | Multi-platform ad metrics | “Compare last quarter’s campaign performance across all channels. Which campaign had the highest ROI?” |
| Ad Performance Benchmarking | Internal or external benchmarks | “Benchmark our latest ad campaign against last year and industry standards.” |
| A/B Testing Performance Analysis | A/B test CTR, revenue impact | “Compare the results of our A/B test and suggest the best performing one.” |
| Attribution Modeling Comparison | Clicks, conversions, revenue | “Compare attribution models for the different channels in this dataset.” |
| Funnel Drop-off Analysis | Website or app analytics | “Identify where users drop off in our funnel and suggest improvements.” |
| Ad Spend Efficiency Analysis | Ad spend, impressions, ROI | “Analyze our ad spend efficiency and recommend budget reallocation.” |
| Budget Allocation Insights | Historical marketing ROI data | “Given our past performance, how should we allocate next quarter’s $100k budget?” |
| Growth Experiment Tracking | Campaign and test data | “Track our growth experiments and summarize high-impact ones.” |
| Customer Segmentation | CRM or customer behavior data | “Segment our customers based on behavior and demographics.” |
Finance analyst
| Use Case | Input Data | User Prompt |
|---|---|---|
| Investor & Board Financial Reporting | Financial metrics from Stripe/banks | “Generate a monthly financial report with key trends.” |
| Runway & Burn Rate Analysis | Company expenses, revenue, funding | “Calculate our current burn rate and runway.” |
| Cash Flow Forecasting | Transactions, revenue, and expenses | “Forecast our cash flow for the next 6 months.” |
| Revenue Recognition & MRR Analysis | Subscription payments | “Calculate MRR, ARR, and deferred revenue.” |
| Financial Anomaly & Fraud Detection | Bank and Stripe payments | “Identify unusual or suspicious transactions.” |
| Accounts Receivable & Payable Tracking | Invoice data and bank transactions | “Summarize overdue invoices and upcoming payments.” |
| Unit Economics & Profitability Analysis | Stripe revenue and cost data | “Analyze our unit economics and most profitable services.” |
| Revenue Forecasting (Time Series) | Historical revenue and seasonality | “Forecast the next 6 months of revenue with seasonality.” |
Startup founder
| Use Case | Input Data | User Prompt |
|---|---|---|
| Investor Metrics Dashboard | Company growth and financials | “Create a dashboard with MRR, churn, CAC, LTV for investors.” |
| Hiring Needs Forecasting | Team size, budget, and growth targets | “Estimate our hiring needs for the next 12 months.” |
| Growth Experiment Tracking | Campaigns and A/B tests | “Track and summarize our growth experiments.” |
| Customer cohorts | Transaction export | “Create a table with monthly cohorts and retention.” |
| Automated Report Generation | Data from multiple departments | “Generate a weekly summary for the leadership team.” |
| Revenue Forecasting (Time Series) | Historical revenue and seasonal factors | “Forecast our revenue for the next 6 months.” |
| Budget Allocation Insights | ROI data from past campaigns | “Distribute our $100k budget by channel to maximize ROI.” |
| Unit Economics & Profitability Analysis | Revenue and costs | “Analyze profitability per product or user segment.” |