Manus combines code execution with research capability to deliver comprehensive data analysis — running the actual statistical analysis, generating visualizations, finding benchmarks or context online, and writing the business narrative that makes the numbers meaningful to decision-makers.
Data analysts, business analysts, operations teams, and executives who need data-driven insights without deep statistical expertise
Upload your CSV, Excel, or database export. Describe the business context: what the data is, what period it covers, and what decisions it will inform.
Specify the key questions: "identify our top performing channels", "find seasonality patterns", "calculate customer lifetime value by segment."
Python code is written and executed against your actual data — calculating statistics, building models, generating charts — with real mathematical precision.
Receive: a narrative analysis report, embedded visualizations, the underlying data as a cleaned/processed file, and code you can rerun to update the analysis.
Identifying what drives revenue
Analyze this CSV of 12 months of sales data. Calculate: revenue by region and rep, month-over-month growth trends, deal size distribution, win rate by industry vertical, and average sales cycle by deal size. Include a recommendation for where to focus Q4 sales resources.
Understanding customer retention patterns
Analyze our customer data file. Calculate cohort retention rates for the last 4 cohorts, identify which customer segments have the highest and lowest 12-month retention, find the strongest predictor of churn, and recommend 2 intervention strategies based on the data.
Process performance investigation
I'm uploading our support ticket data for the last year. Calculate: resolution time by issue type and team, first-contact resolution rate, SLA compliance rate, seasonal volume patterns, and the top 5 issue categories by volume. Identify the 3 most important process improvement opportunities.
"This is monthly revenue data for a subscription business. Churn is our biggest challenge" gives Manus the context to frame analysis toward what actually matters for your decisions.
"Don't just show the numbers — tell me what actions these findings suggest" produces analysis you can act on rather than a description of what the data shows.
"Compare our metrics against industry benchmarks from publicly available reports" adds context that shows whether your numbers are good, average, or below standard.
Ask for the Python analysis code alongside the report. This lets you re-run the analysis on next month's data without starting from scratch.