Analyzing the "Sales of Financial Products for XYZ Company"

Dataset: "Product_Sales.csv" file

Link to report on Looker Studio: https://lookerstudio.google.com/s/jJ1yV3S4wNc

1. Understand the Dataset

The dataset contains 7 columns and 9,939 observations. It provides details about calls made by agents to customers and the sales generated.

Insights I Derived

1. Agent Performance:

• Total products sold by each agent.
• Conversion rate per agent.
• Average duration of calls leading to a sale.

2. Customer Behavior:

• Pickup rate (calls answered by customers / total calls).
• Average number of products sold to each customer.

3. Call Performance:

• Average call duration.
• Call success rate (pickup rate and product sold rate combined).

4. Overall Business Metrics:

• Total products sold.
• Total calls made.
• Correlation between call duration and sales.

5. Decision-Making Insights:

• Identify top-performing agents for promotion or salary increase.
• Determine areas for training (e.g., agents with low conversion rates).

2. Perform Data Analysis

Step 1: Data Cleaning Using Python

• Import lbraries
• Load the dataset
• Check for missing values
• Handle missing values
• Verify data types
• Check for duplicate values & handle them
• Standardize columns

Step 2: Data Transformation and Analysis

1. Created new dimensions/fields such as, PickedUp_Numeric, SuccessfulSale_Numeric, and SuucessfulSale_Duration
2. Calculated metrics (Pickup rate, conversion rate, average duration for successful sale, & overall call success rate)
3. Found the correlation between call duration and sales

Step 3: Data Visualization Using Google Looker Studio:

1. Import the cleaned dataset (saved as a CSV or Excel file) into Google - Sheets or BigQuery.
2. Connect Looker Studio to your data source.
3. Create the following visuals:

• Bar Chart:
• KPI Cards / Score cards
• Tables
• Scatter Plot: Correlation between call duration and products sold per agent.
• Filters: Enable filters for other visuals by AgentName.

3. Case Study: XYZ Financial Services

Company Background

XYZ Financial Services specializes in offering tailored financial products. The sales team makes outbound calls to customers, introducing them to new financial products and services.

Insights for Stakeholders

1. Agent Insights:

• Top-performing agents (e.g., agents with the highest total sales and conversion rates).
• Agents with low performance requiring training.

2. Customer Insights:

• High-value customers (customers purchasing the most products).
• Average pickup rates to assess customer engagement.

3. Call Insights:

• Calls with the highest success rates.
• Optimal call durations for sales conversions.

Actionable Recommendations

• Promote or reward agents with consistently high performance.
• Provide additional training to agents with low conversion rates.
• Optimize call durations to improve efficiency and conversion rates.

4. Decision-Making for Promotions or Salary Increases

1. Use the Agent Performance Table to rank agents by:

• Total products sold.
• Conversion rate.
• Average call duration.

2. Reward top-performing agents with promotions or salary increases. Example criteria:

• Agents with sales in the top 10% and conversion rates above the median.
• Recognize agents reducing call durations without impacting sales.

5. Step-by-Step Workflow Summary

1. Data Cleaning:

• Remove duplicates, handle missing values, and standardize data formats.

2. Data Transformation:

• Create aggregated metrics for agents and customers.

3. Exploratory Data Analysis:

• Use Python to calculate key metrics and correlations.

4. Visualization:

• Use Google Looker Studio to create an interactive dashboard.

5. Insights and Storytelling:

• Present findings and recommendations to stakeholders.

By following this process, I was able to derive actionable insights that help XYZ Financial Services make data-driven decisions regarding agent performance, customer behavior, and call strategy.

Dashboard 👇

Full report 👇