Data Analysis AI prompts

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Create an interactive financial panel in Excel that consolidates revenue, expenses, and contribution margin data with automatic updates via pivot tables.

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Build an ETL (extract, transform, load) workflow in Excel Power Query to clean, standardize, and consolidate datasets affected by common real-world corporate data problems.

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Develop an Excel or Google Sheets inventory control system that classifies products using the ABC curve, calculates reorder points, and generates automatic visual alerts.

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Create a set of VBA macros that automate data consolidation from multiple sheets, generate standardized charts, and export reports as PDF for executive distribution.

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Build a financial projection model in Google Sheets that allows simulation of 3 different scenarios, with adjustable assumptions and sensitivity analysis for decision-making.

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Create a set of reusable SQL queries that analyze sales by period, product, seller, and region using CTEs (Common Table Expressions) and window functions for rankings and comparisons.

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Create a complete relational model for a recurring subscription system, including tables, indexes, constraints, and ready-to-use queries for calculating MRR, churn, and LTV.

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Learn to identify SQL query performance bottlenecks using EXPLAIN ANALYZE, create efficient indexes, and rewrite queries to reduce response time.

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Develop SQL stored procedures that calculate sales commissions considering tiered quotas, product mix bonuses, cancellation clawbacks, and team splits.

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Build conversion funnel analysis queries in BigQuery (Google Analytics 4 export) to identify drop-off points, optimize conversion rates, and attribute revenue to acquisition channels.

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Create a quick-reference guide to help analysts choose the right chart (among 20+ types) based on analysis objective, number of variables, and target audience.

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Create the functional and technical specification for a Power BI dashboard for the commercial/sales department, including Star Schema data model, advanced DAX measures, and visual interactions.

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Build a digital marketing report in Looker Studio (formerly Data Studio) that combines data from multiple Google sources, with automatic updates and stakeholder sharing.

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Apply data storytelling techniques to create presentations that influence decisions, combining narrative structure, slide design, and insight sequencing.

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Implement an operational dashboard in Metabase (open source) that monitors KPIs in real time, sends automated alerts when metrics exceed thresholds, and enables self-service exploration for the team.

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Create a hierarchical KPI framework for e-commerce that links business metrics (revenue, margin) to operational metrics (conversion, AOV, CAC), with targets grounded in industry benchmarks.

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Build a complete OKR system (Objectives and Key Results) covering definition, cascading, weekly tracking, and quarterly scoring, with BI integration for automated progress monitoring.

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Create a simplified, actionable Balanced Scorecard for SMBs with specific metrics for each perspective (financial, customers, processes, learning) suited to your tax and operational context.

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Create a complete SaaS metrics hierarchy that differentiates leading indicators (which predict the future) from lagging indicators (which confirm the past), enabling intervention before results worsen.

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Create a logistics metrics system covering the full supply chain (receiving, storage, picking, packing, shipping, transportation, last mile) with measurable SLAs and exception management.

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Create a monthly executive report template that presents results clearly for leadership, with variance analysis, root causes, and recommendations prioritized by impact.

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Build a Python script that automates the entire report generation pipeline: data extraction from a database, processing with Pandas, chart creation, export to PDF/Excel, and automatic email delivery.

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Develop a detailed cohort analysis that reveals behavioral patterns over time, identifies the critical churn window, and quantifies the financial impact of retention.

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Create a variance analysis framework that breaks down differences between budgeted and actual results into their components (price effect, volume effect, mix effect, exchange rate effect) to enable precise corrective action.

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Write the analytical narrative for the annual report, turning tables and charts into a story that connects past results with future strategic direction, in language appropriate for diverse stakeholders.

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Develop a production-ready set of DAX measures that solve the most common financial BI calculations: YoY, YTD, PMPM, margin calculation by segment, indirect expense allocation, and linear forecasting in Power BI Desktop.

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Create a complete Python predictive analytics pipeline — from data preparation to generating forecasts consumable by the operations team — using linear regression, decision trees, and Facebook Prophet for time series analysis.

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Identify which marketing channels have the highest correlation with conversions and revenue, build a multi-touch attribution model, and distribute media budget based on actual performance data—replacing last-click attribution.

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Implement cohort analysis across three dimensions—usage retention, cumulative revenue, and feature adoption—to identify which customer cohorts are most valuable, what differentiates users who stay from those who churn, and where to focus product and marketing efforts.

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Develop a robust LTV model accounting for real market conditions—interest rates, inflation, segmented churn, and currency effects—enabling the growth team to make acquisition budget, pricing, and product decisions based on actual customer value per cohort.