The Profitability Deep Dive Prompt helps your team uncover the drivers behind profit performance using live data from your Polar Analytics workspace.
By connecting Polar to your Model Connection Protocol (MCP) tool — such as ChatGPT or Claude — you can instantly generate profitability analyses without manually exporting data or building dashboards.
This prompt is especially useful for:
Identifying which channels, products, or campaigns are driving profit (or loss).
Understanding how costs such as shipping, discounts, or ad spend affect your bottom line.
Receiving AI-generated summaries and recommendations for improving profitability.
In this article, you’ll learn:
How to use the Profitability Deep Dive prompt.
The types of insights it can generate.
Best practices for tailoring the prompt to your unique business needs.
What Is the Profitability Deep Dive Prompt?
The Profitability Deep Dive Prompt is designed to give ecommerce and marketing leaders a complete view of profit drivers across the business — in one AI-generated summary.
It pulls directly from your Polar Analytics data model, combining key metrics like:
Revenue
Cost of Goods Sold (COGS)
Shipping and handling costs
Marketing spend by channel
Gross profit and net profit margins
When used in an MCP-connected AI tool (like ChatGPT or Claude), this prompt transforms your financial and marketing data into an actionable report written in plain language.
Example Output:
“In the last 30 days, your total revenue was $210,000 with a gross margin of 68%. Meta Ads accounted for 45% of total spend but generated a 3.8x ROAS, while Google Ads had higher CAC and lower margins. Your top 3 profitable SKUs contributed 28% of total profit. Consider reducing spend on underperforming campaigns and focusing on high-margin items.”
Why it matters:
Aligns marketing and finance data in one view.
Highlights hidden costs or inefficiencies.
Provides fast, accessible profit insights for decision-making.
How to Use the Profitability Deep Dive Prompt
You can use this prompt in any AI tool connected to Polar MCP — such as ChatGPT, Claude, or Make.
Step 1. Confirm Your MCP Connection
Ensure your Polar MCP is correctly configured:
Endpoint:
https://api.polaranalytics.com/mcp
Follow setup instructions: Installing the Polar MCP.
Once connected, your AI assistant can access live profit, cost, and campaign data from your Polar workspace.
Step 2. Use the Prompt
Copy and paste the following example prompt into your MCP-connected AI tool:
Act as an ecommerce financial analyst. Using my Polar Analytics data, provide a profitability deep dive for the last 30 days. Include key metrics such as revenue, COGS, shipping costs, ad spend, and profit margins. Highlight which channels, products, and campaigns drive the highest and lowest profitability, and give recommendations to improve margins.
Step 3. Review the Results
The AI assistant will analyze your workspace data and generate a detailed profitability summary that may include:
Top-performing channels by profit margin or ROAS.
Underperforming products or campaigns eating into profit.
Operational cost breakdowns (shipping, COGS, etc.).
Strategic recommendations to optimize ad allocation, pricing, or inventory.
Example Query Variation:
“Compare my profitability by channel for Q3 vs Q2. Highlight where CAC increased and its impact on net margin.”
You can modify the time range, level of detail, or focus area (e.g., channel, SKU, region) to get more tailored insights.
Best Practices for Profitability Analysis
To get the most accurate and actionable insights from the Profitability Deep Dive prompt, follow these best practices:
1. Ensure Complete Data Connections
Connect all key data sources — including Shopify, ad platforms, and shipping or cost management systems — in Polar.
The MCP can only analyze profitability accurately if all costs (COGS, shipping, discounts, ad spend) are synced.
2. Ask Comparative Questions
Profitability trends are most useful when compared over time.
For example:
“Compare profit margin by month over the last 6 months.”
“Which campaigns had the largest decline in profitability week-over-week?”
3. Request Actionable Insights
To go beyond raw data, include calls for interpretation or recommendations in your prompt.
Example:
“Explain why gross profit decreased despite stable revenue, and suggest corrective actions.”
This ensures the AI output moves from descriptive analytics to diagnostic and prescriptive insights.
4. Align with Business Goals
Different teams focus on different profitability levers:
Finance: Overall gross and net margins.
Marketing: ROAS and CAC efficiency.
Operations: Inventory and shipping cost optimization.
Tailor your prompt accordingly to get the most relevant report for your role.
The Profitability Deep Dive Prompt gives you a fast, accurate, and contextual understanding of what’s driving (or hurting) your profits — directly from your Polar Analytics data.
By combining financial, marketing, and operational data into a single AI-generated summary, you can make faster, smarter business decisions that drive sustainable growth.
Key takeaways:
The prompt analyzes all major profit drivers: revenue, costs, and ad efficiency.
You can customize it by timeframe, focus area, or reporting tone.
Pair it with other prompts (like Executive Summary or Inventory Optimization) for a complete business overview.
For more guidance, explore: