The dataset represents a comprehensive sales pipeline for a company specializing in computer hardware. It consists of four key tables:

1. Accounts: Information about customer accounts, including their size, location, and other demographic details.
2. Products: Details of the hardware products offered, including specifications and categories.
3. Sales Teams: Information about the company’s sales representatives and their regional or team assignments.
4. Sales Pipeline: Tracks sales opportunities, including deal stages, values, and timelines.

Additionally, a Meta Data table provides descriptions and context for the columns across all tables, ensuring clarity in data interpretation. This dataset supports insights into sales performance, customer segmentation, and team effectiveness.

Dataset Availability is on kaggle

https://www.kaggle.com/datasets/agungpambudi/crm-sales-predictive-analytics

DAX Expressions used in the project

Conversion Percentage = 
DIVIDE(
    COUNTROWS(FILTER('TableName', 'TableName'[Deal Stage] = "Won")),
    COUNTROWS('TableName')
) * 100
To calculate the average closed deal value in Power BI, you can use the following DAX formula. This assumes that your dataset has a column representing the deal value (e.g., Deal Value) and a column for the deal stage (e.g., Deal Stage), where closed deals are marked as "Won".
Avg Closed Deal Value = 
AVERAGEX(
    FILTER(
        'TableName',
        'TableName'[Deal Stage] = "Won"
    ),
    'TableName'[Deal Value]
)
Churned Customers = 
COUNTROWS(
    FILTER(
        sales_pipeline,
        sales_pipeline[Deal Stage] = "Lost"
    )
)
Average Close Days = 
AVERAGEX(
    FILTER(
        sales_pipeline,
        sales_pipeline[Deal Stage] = "Won"
    ),
    DATEDIFF(sales_pipeline[Start Date], sales_pipeline[Close Date], DAY)
)
# calculated column
Account Size = 
SWITCH(
    TRUE(),
    accounts[employees] <= 10, "Micro",
    accounts[employees] >= 11 && accounts[employees] <= 200, "Small",
    accounts[employees] >= 201 && accounts[employees] <= 500, "Medium",
    accounts[employees] >= 501 && accounts[employees] <= 1000, "Large",
    accounts[employees] > 1000, "Enterprise",
    "Unknown"
)