Upsell Propensity Scorer

Sales OpsAdvanced Monthly

Mission Overview

Matches historical upsell data to current customer profiles (Employee Growth + Usage) to predict expansion.

BLUEPRINT.md

100% Text-Only (.md, .csv)

Bundle Contents:

expansion-propensity-scorer.md customer_growth.csv README.txt

# Agent Configuration: The Account Management Ops

## Role
You are a **Account Management Ops**. Matches historical upsell data to current customer profiles (Employee Growth + Usage) to predict expansion.

## Objective
Predict next-month expansion revenue.

## Capabilities
*   **Predictive Scoring:** Growth signals.
*   **Lead Gen:** Internal upsell leads.

## Workflow

### Phase 1: Initialization & Seeding
1.  **Check:** Does `customer_growth.csv` exist?
2.  **If Missing:** Create `customer_growth.csv` using the `sampleData` provided in this blueprint.
3.  **If Present:** Load the data for processing.

### Phase 2: The Loop
1.  **Read:** `customer_growth.csv`.
2.  **Score:** (Emp_Growth + Usage_Growth) / 2.
3.  **Rank:** Descending.
4.  **Output:** Save `propensity_to_buy.csv`.

### Phase 3: Output
1.  **Generate:** Create the final output artifact as specified.
2.  **Summary:** detailed report of findings and actions taken.

How to Run This

1Get the files

Download the Bundle ZIP above. It contains the blueprint and any required files.

2Run in Terminal

Universal: These blueprints work with any agentic CLI.

Gemini CLI

gemini "Read @expansion-propensity-scorer.md and use the sample file to execute the workflow"

Why use blueprints?

Blueprints act as a "Mission File". Instead of giving your AI dozens of small, confusing prompts, you provide a single structured document that defines the Role, Objective, and Workflow.