📊 CRM & Sales Analysis

📕 Table of Contents

• Problem Statement
• Objective
• Tools Used
• Dataset Overview
• Key Findings
• Recommendations
• Project Presentation
• Project Learnings
• Installation and Usage
• Dashboard

❓Problem Statement

A company aims to evaluate its CRM data and sales pipeline for leads registered over the last five months. The task is to build a comprehensive analytic report that provides insights into lead distribution across countries, industries, and organization sizes. The report assesses the health of the sales pipeline, forecasts potential income over the next few months, and compares sales agent performance.

🎯 Objective

The primary objective is to provide actionable insights that will optimize sales performance, improve lead conversion rates, and enhance revenue forecasting accuracy. Specific goals include:

1. Identify top-performing sales agents and their strategies
2. Evaluate lead conversion rates across different dimensions
3. Assess sales pipeline health month-over-month
4. Analyze impact of response time on conversion rates
5. Examine trends in average deal values
6. Develop a robust revenue forecasting model
7. Investigate relationships between organization size, sales cycle, and deal value
8. Compare effectiveness of sales strategies for different product types
9. Identify factors influencing sales cycle duration
10. Analyze characteristics of lost opportunities

🛠️ Tools Used

• Analytical & Visual: Microsoft Power BI
  
• Presentation: Microsoft Power Point
  

📅 Dataset Overview

• Data source: Company CRM
• Time period: 2024
• Data size: crm_data(3000,17)
• Key columns: country, Deal Value, $, Industry, Organization size, Owner, Lead acquisition date, Product, Status, Stage, Deal Value $ ,Probability, % Expected close date Actual close date
• Calculated/Dax columns: response time, sort_index, customer_conversion_rate, win_rate, response_time, show_metrics

🔎 Key Findings

1. Total revenue: $425.37k
2. Conversion rate: 86.17%
3. Win rate: 2.77%
4. Probability of losing a customer: 40%
5. Forecasted July sales: $60.17K
6. Germany leads with 88.10% conversion rate
7. Custom solutions have highest conversion rate and average deal value
8. March saw highest number of leads
9. Jessica Martinez most accurate in forecasting
10. John Smith has highest win rate (5.02%)
11. Kevin Anderson has highest conversion rate (88.33%)

💡 Recommendations

1. Implement strategies to convert opportunities to active customers
2. Develop targeted retention program
3. Analyze and replicate top performers' sales techniques
4. Implement rapid response system for high-value opportunities
5. Replicate German market success in other countries
6. Develop strategies based on organization sizes
7. Improve win rate through targeted training and process enhancement
8. Implement comprehensive customer retention strategy
9. Conduct regular customer check-ins

📌 Project Presentation

Video Presentation

Slides

The detailed presentation slides for this project can be found here

🧠 Project Learnings

1. Data Loading and Transformations.
2. Power Query and DAX.
3. Conditional and calculated column.
4. KPI Development.
5. Dynamically switch title in visuals.
6. Implementation of dynamic filtering to analyze metrics (e.g., average deal value, total customer, probability) across different dimensions (country, industry,agent etc.)
7. Data visualization.
8. Data storytelling.
9. Sharpened analytical and problem-solving abilities.
10. ctionable Insights Generation.
11. Strengthened strategic planning and presentation skill.
12. Enhanced communication skills.

💻 Installation and Usage

• Microsoft Power BI Desktop

📈 Dashboard

• Live Dasboard

• More Images