Use Python libraries like Pandas for data handling, NumPy for numerical operations, and Matplotlib/Seaborn for visualization of call data.
Import call tracking data in formats like CSV or Excel using Pandas. Use pd.read_csv() or pd.read_excel() to load the dataset into a DataFrame.
Handle missing or duplicate entries using dropna(), fillna(), and drop_duplicates(). Ensure consistent formatting for analysis.
Calculate key metrics like call duration, frequency, peak times, and success rates. Use groupings (groupby) for detailed insights.
Create visualizations like bar charts, pie charts, or time series plots using Matplotlib or Seaborn to identify patterns in call behavior.
Identify actionable insights, such as improving response rates during peak times or optimizing underperforming agents based on the data.