This project involves analyzing sales data provided by a start-up that sells various products through multiple channels. The goal is to create a dashboard that provides insights on a weekly basis to inform strategy. The analysis focuses on identifying key trends, patterns, and potential areas for improvement. Additionally, the project aims to identify gaps and additional data to track for making meaningful decisions at the executive level.

Data Sources

The project uses the following tables:

Accounts:  account, sector, year_established, revenue, employees, office_location, subsidiary_of

Sales_Teams: sales_agent, manager, regional_office

Products: product, series, sales_price

Sales_Pipeline: opportunity_id, sales_agent, product, account, deal_stage, engage_date, close_date, close_value

Calculate total sales revenue over time.

Analyze the distribution of deal stages to understand the sales pipeline's health.

Calculate win rates for different products, sales agents, and accounts.

Identify the most successful sales agents and accounts based on closed deals.

2. Sales Performance Analysis:

Calculate total sales revenue over time.

Determine the average sales cycle length.

Measure revenue generated by each product.

Track revenue by region.

3. Product Performance Analysis

Evaluate the performance of each product in terms of revenue generated and number of deals closed.

Analyze the distribution of deal stages for each product to identify bottlenecks.

Identify cross-selling opportunities by analyzing which products are often sold together.

4. Sales Team Analysis:

Assess the performance of sales agents in terms of revenue generated and deals closed.

Analyze the distribution of deal stages for each sales agent to identify areas for improvement.

Identify top-performing sales agents and provide insights into their successful strategies.

5. Account Analysis:

Segment accounts based on industry, size, and location to identify high-value segments.

Analyze the distribution of deal stages for each account segment to understand conversion rates.

Identify key accounts with high potential for upselling or cross-selling.

6. Trend Analysis

Analyze sales performance over time to identify seasonal trends or fluctuations.

Identify any sudden changes or anomalies in sales data that may require further investigation.

7. Additional Data Requirements

Track customer demographics, marketing campaigns, customer feedback, and competitor activities to enhance analysis and strategic decision-making.

SQL Queries

SELECT product,

QUARTER(close_date) AS quarter,

COUNT(*) AS number_of_deals

FROM Sales_Pipeline

WHERE deal_stage = 'Won'

GROUP BY product, QUARTER(close_date)

ORDER BY product, quarter;

Revenue per Product

SELECT product,

SUM(close_value) AS total_revenue

FROM Sales_Pipeline

WHERE deal_stage = 'Won'

GROUP BY product

ORDER BY total_revenue DESC;

Regional Revenue per Product

SELECT p.product,

st.regional_office,

SUM(sp.close_value) AS total_revenue

FROM Sales_Pipeline sp

JOIN Sales_Teams st ON sp.sales_agent = st.sales_agent

JOIN Products p ON sp.product = p.product

WHERE sp.deal_stage = 'Won'

GROUP BY p.product, st.regional_office

ORDER BY p.product, total_revenue DESC;

Some DAX Measures

NumberOfDealsClosed =

CALCULATE(

COUNT(Sales_Pipeline[opportunity_id]),

Sales_Pipeline[deal_stage] = "Won"

)

Top 10 Performing Agents

Top10Agents =

TOPN(

10,

SUMMARIZE(

Sales_Pipeline,

Sales_Pipeline[sales_agent],

"TotalRevenue", SUM(Sales_Pipeline[close_value])

),

[TotalRevenue], DESC

)

Difference Between Engage Date and Close Date

EngageToCloseDuration =

DATEDIFF(Sales_Pipeline[engage_date], Sales_Pipeline[close_date], DAY)

Percentage Lost

PercentageLost =

DIVIDE(

CALCULATE(

COUNT(Sales_Pipeline[opportunity_id]),

Sales_Pipeline[deal_stage] = "Lost"

),

COUNT(Sales_Pipeline[opportunity_id])

) * 100

Implementation Notes

Ensure that all necessary data preprocessing steps are performed in Power Query before loading the data into Power BI.

Use the DAX measures to create visuals in Power BI that reflect the key performance indicators and insights identified in the objectives.

For detailed analysis and cross-selling opportunities, consider creating additional calculated columns and tables in Power Query.

This project aims to provide a comprehensive analysis of the sales data to derive meaningful insights that can drive strategic decision-making at the executive level. The use of SQL and DAX enables efficient data processing and visualization, facilitating informed decisions based on accurate data insights.