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Sales Analytics and Modeling in Excel with ChatGPT
Introduction
Source Files
1. Course Requirement (1:34)
Measuring Performance with KPIs
1. Project Preview (0:57)
2. Sales Key Perfomance Indicators (Kpis) (10:16)
3. Measuring Salesperson Performance Using Kpis (5:48)
4. Marketing And Financial Kpis (6:49)
5. Customer-Related Kpis (10:20)
KPI Case Study
1. Project Preview (0:38)
2. Case Study Involving Kpis (3:09)
3. Joining Data Tables In Excel (7:28)
4. Cleaning Data Using Filters In Excel (5:19)
5. Determining Lead Conversion Time (7:18)
Analyzing Sales Data
1. Project Preview (1:19)
2. Aggregating Data By Regions, Categories, And Time Dimension (6:25)
3. Evaluating Salesperson Performance (13:59)
Visualizing Sales Data
1. Project Preview (1:18)
2. Creating Charts To Visualize Sales Data (8:08)
3. Charting Region-Wise Percentage Contribution (6:22)
4. Charting Category-Wise Average Order Value (5:47)
5. Analyzing Lead Generation Trends (7:54)
6. Analyzing Salesperson Performance (6:26)
7. Building A Sales Dashboard (6:22)
8. Additional Charts For Sales Modeling (8:33)
Sales Modeling and Prospecting
1. Project Preview (1:33)
2. Building The Whale Model (6:47)
3. Lead Segmentation Using Decision Trees (6:53)
4. Excel Preparation For Analysis (6:52)
5. Case Study On Lead Segmentation (5:05)
6. Building A Model In Excel (9:38)
7. Interpreting Results From Tree Nodes (5:25)
8. Interpreting Results Based On Classification Criteria (5:23)
9. Drawing Inferences From Model Results (5:30)
10. Making Predictions Using The Trained Model (3:19)
11. Advanced Customization Options For Models (4:37)
Maximizing Customer Value
1. Project Preview (1:05)
2. Market Basket Analysis For Cross-Selling Opportunities (6:27)
3. Predicting Values Using The Trained Model (11:01)
Sales Forecasting
1. Project Preview (3:11)
2. Modeling Trends And Seasonality (10:03)
3. Additive And Multiplicative Time Series Models (9:30)
4. Linear Regression Model For Sales Forecasting (7:43)
5. Preprocessing Data For Regression (12:14)
6. Building A Linear Regression Model (8:27)
7. Predicting Values Using The Trained Model (8:26)
8. Using Xlstat For Forecasting (7:56)
Customer Lifetime Increase
1. Project Preview (1:52)
2. Building A Logistic Regression Model For Churn Prediction (12:17)
3. Predicting Churn Probability Using The Trained Model (11:05)
4. Evaluating Model Accuracy Using A Confusion Matrix (12:28)
3. Additive And Multiplicative Time Series Models
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