https://github.com/omkarpattnaik8080/saas_sales_insights

https://github.com/omkarpattnaik8080/saas_sales_insights

data-science data-visualization machine-learning msexcel mysql python

Last synced: 5 days ago
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• Host: GitHub
• URL: https://github.com/omkarpattnaik8080/saas_sales_insights
• Owner: omkarpattnaik8080
• License: mit
• Created: 2024-07-14T00:28:19.000Z (4 months ago)
• Default Branch: main
• Last Pushed: 2024-07-14T00:36:20.000Z (4 months ago)
• Last Synced: 2024-07-14T01:31:35.029Z (4 months ago)
• Topics: data-science, data-visualization, machine-learning, msexcel, mysql, python
• Homepage:
• Size: 9.77 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# SaaS_Sales_Insights

Problem Statement:

"Increasing customer acquisition and retention rates through data-driven insights is crucial for our SaaS business. However, we lack a structured approach to analyze sales data effectively, hindering our ability to identify growth opportunities and optimize our sales strategies."

Key Performance Indicators (KPIs):

1.Customer Acquisition Cost (CAC): Measure the cost associated with acquiring each new customer.

2.Customer Lifetime Value (CLTV): Evaluate the total revenue expected from a customer throughout their relationship with the company.

3.Churn Rate: Track the rate at which customers stop subscribing to the service.

4.Conversion Rates: Analyze the percentage of leads that convert into paying customers.

6.Average Revenue Per User (ARPU): Calculate the average revenue generated by each customer.

7.Sales Pipeline Velocity: Measure the speed at which opportunities move through the sales pipeline.

8.Lead Response Time: Monitor how quickly sales teams respond to leads generated.

To visualize and analyze the data for the given problem statement of a SaaS company, several types of charts and graphs can be useful. These visualizations help in understanding trends, relationships, and patterns in the data. Here's a list of recommended charts along with the aspects of the dataset they can address:

 Recommended Charts and Their Uses

1. Line Chart for Monthly Revenue Over Time

   - X-axis: Date (Time)

   - Y-axis: Monthly Revenue

   - Use: Track revenue trends over months to identify seasonal patterns or growth trends.

3. Bar Chart for Subscription Plans and Count of Customers**

   - X-axis: Subscription Plan

   - Y-axis: Count of Customers

   - Use: Compare the popularity of different subscription plans among customers.

4. Pie Chart for Churn Rate

   - Sections: Churned vs. Active Customers (based on the churned column)

   - Use: Show the proportion of customers who have churned versus those who are still active.

5. **Scatter Plot for CLTV vs. CAC**

   - **X-axis**: Customer Acquisition Cost (CAC)

   - **Y-axis**: Customer Lifetime Value (CLTV)

   - **Use**: Analyze the relationship between CAC and CLTV to determine if customer acquisition costs are justified by the lifetime value of customers.

6. **Stacked Bar Chart for Conversion Rates by Lead Source**

   - **X-axis**: Lead Source

   - **Y-axis**: Conversion Rate

   - **Stacked bars**: Differentiate between conversion rates for each lead source

   - **Use**: Compare conversion rates across different lead sources to identify the most effective channels.

7. **Histogram for Lead Response Time**

   - **X-axis**: Lead Response Time (binned into intervals)

   - **Y-axis**: Count of Leads

   - **Use**: Understand the distribution of lead response times to identify opportunities for improving response efficiency.

8. **Gantt Chart for Sales Pipeline Status and Velocity**

   - **Y-axis**: Sales Pipeline Status

   - **X-axis**: Time (Sales Pipeline Velocity)

   - **Use**: Visualize the progress of leads through the sales pipeline over time to track efficiency and identify bottlenecks.

9. **Box Plot for ARPU Distribution**

   - **X-axis**: Subscription Plan

   - **Y-axis**: Average Revenue Per User (ARPU)

   - **Use**: Compare the distribution of ARPU across different subscription plans to understand revenue generation patterns.

### Additional Considerations

- **Dashboard**: Consider aggregating these charts into a comprehensive dashboard for a holistic view of sales performance and customer metrics.

- **Interactivity**: Depending on your tools (like Tableau, Power BI, or custom web applications), add interactivity for drill-down capabilities and dynamic filtering.

These charts and visualizations will help stakeholders in the SaaS company to gain actionable insights from the dataset, enabling informed decision-making and strategic planning based on data-driven analysis.