MCP For Claude Marketplace
Average Order Value Analytics

Average Order Value Analytics MCP Connector for Claude

A+

Calculate and analyze Average Order Value (AOV) across business segments and timeframes.

4 tools Official Updated Oct 1, 2026 Official Vinkius Partner
More Details Connect to Claude

This MCP server provides specialized analytics tools to monitor Average Order Value (AOV), a critical KPI for understanding customer spending behavior. By connecting your AI assistant to this server via Vinkius Edge, you can perform temporal analysis, segmentation, and growth comparisons. Use get_global_aov to see the big picture, get_segmented_aov to drill down into specific product categories or customer types, get_aov_trend to visualize changes over time, and compare_aov_periods to measure performance shifts between different timeframes.

aovkpirevenueecommerce-analyticsbusiness-metrics

4 tools expose this connector's capabilities to your AI agent.

get_aov_trend

Provides a chronological series of AOV values to visualize how order value changes over time

get_global_aov

Retrieves the total average order value for the entire business over a specified period

get_segmented_aov

Calculates the average order value for specific customer segments or product categories

compare_aov_periods

Compares the average order value between two distinct timeframes to measure growth or decline

See how to talk to your AI agent using Average Order Value Analytics.

What was our average order value between 2024-01-01 and 2024-01-31?

The average order value for January 2024 was $54.20, with a total revenue of $54,200 from 1,000 orders.

Show me the AOV trend for the last month on a weekly basis.

In the last month, the weekly AOV trend was: Week 1: $45.00, Week 2: $48.50, Week 3: $52.00, Week 4: $50.50.

How does the AOV for the 'electronics' category compare to the global average for Q1 2024?

The AOV for the electronics category in Q1 2024 was $120.00, while the global average for the same period was $55.00.

AOV is calculated by dividing the total revenue by the total number of orders within a specific timeframe.

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