Power BI Report on Customer Spending Behavior

Research Questions for Power BI Report on Customer Spending Behavior Open Dataset

Your dataset includes the following columns:

• CustomerID
• Gender
• Age
• Annual Income (k$)
• Spending Score (1-100)

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Research Questions and Report Steps

1️⃣ Research Question: How does spending behavior vary across age groups?

📌 Steps to Generate the Report:

1. Transform Data in Power Query:
   • Create a new column Age Group to categorize customers.
   • Use M Query:

     AgeGroup = 
         if [Age] < 20 then "Below 20"
         else if [Age] >= 20 and [Age] < 30 then "20-29"
         else if [Age] >= 30 and [Age] < 40 then "30-39"
         else if [Age] >= 40 and [Age] < 50 then "40-49"
         else "50+"
     

2. Visualize in Power BI:
   • Bar Chart: X-axis → Age Group, Y-axis → Average Spending Score
   • Pie Chart: Show distribution of customers in different age groups

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2️⃣ Research Question: What is the relationship between annual income and spending score?

📌 Steps to Generate the Report:

1. Transform Data in Power Query:
   • Create a new column Income Category to classify income levels.
   • Use M Query:

     IncomeCategory = 
         if [Annual Income (k$)] < 30 then "Low Income (<30k)"
         else if [Annual Income (k$)] >= 30 and [Annual Income (k$)] < 60 then "Middle Income (30-59k)"
         else if [Annual Income (k$)] >= 60 and [Annual Income (k$)] < 100 then "High Income (60-99k)"
         else "Very High Income (100k+)"
     

2. Visualize in Power BI:
   • Scatter Plot: X-axis → Annual Income, Y-axis → Spending Score
   • Bar Chart: Compare average spending scores across Income Categories

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3️⃣ Research Question: Do males and females have different spending patterns?

📌 Steps to Generate the Report:

1. Transform Data in Power Query:
   • Standardize gender data (ensure consistency: "Male" & "Female").
2. Visualize in Power BI:
   • Bar Chart: X-axis → Gender, Y-axis → Average Spending Score
   • Pie Chart: Show percentage of male vs. female customers

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4️⃣ Research Question: Who are the high-value customers?

📌 Steps to Generate the Report:

1. Transform Data in Power Query:
   • Create a new column Customer Category based on spending score.
   • Use M Query:

     CustomerCategory = 
         if [Spending Score (1-100)] > 75 and [Annual Income (k$)] > 70 then "High Value"
         else if [Spending Score (1-100)] >= 50 then "Medium Value"
         else "Low Value"
     

2. Visualize in Power BI:
   • Pie Chart: Show proportions of High, Medium, and Low-Value customers
   • Table View: Display list of high-value customers

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Additional Research Questions for M Query-Based Columns

1️⃣ How can we create a column to classify customers into different spending categories?
M Query:

SpendingCategory = 
    if [Spending Score (1-100)] >= 80 then "Luxury Spender"
    else if [Spending Score (1-100)] >= 50 then "Regular Spender"
    else "Budget Conscious"

Usage: Use it in bar charts to compare spending categories.

2️⃣ How can we classify customers based on a combination of income and spending?
M Query:

CustomerSegment = 
    if [Annual Income (k$)] >= 60 and [Spending Score (1-100)] >= 80 then "Elite Customer"
    else if [Annual Income (k$)] < 60 and [Spending Score (1-100)] >= 80 then "Impulsive Spender"
    else if [Annual Income (k$)] >= 60 and [Spending Score (1-100)] < 50 then "Cautious Spender"
    else "General Customer"

Usage: Use it in stacked bar charts to compare customer segments.