CUSTOMER SATISFACTION & LOYALTY ANALYSIS PROJECT

PROJECT DESCRIPTION

This Power BI project analyzes customer satisfaction and loyalty using survey feedback, demographic data, and behavioral metrics. The goal is to:

• Identify regional strengths and pain points
• Segment customers by loyalty (NPS categories) and demographics
• Provide actionable insights to improve retention, experience, and long-term loyalty.

Technologies & Tools

• Data Preparation & Cleaning: Microsoft Excel + Power BI (Power Query)
• Modeling & Calculations: Power BI (DAX)
• Visualization: Power BI Desktop
• Dataset Format: Excel (.xlsx)

Project Steps

1. Data Loading & Cleaning (Power Query)

   i. Load the Excel file into Power BI Desktop.
   ii. Open **Power Query Editor**.
   iii. Check for and handle:
       - Duplicates → Remove duplicates
       - Empty/missing values → Fill using average where appropriate (Fill Up/Down)
   iv. Close & Apply changes.

2. Calculated Columns (DAX)

   - **NPS Category** (Promoters, Passives, Detractors):

     ```dax
   NPS_CATEGORY = 
   SWITCH(
   TRUE(),
   'DataDNA Dataset Challenge - Cus'[Satisfaction_Score] >= 9, "PROMOTER",
   'DataDNA Dataset Challenge - Cus'[Satisfaction_Score] IN {7,8}, "PASSIVE",
   'DataDNA Dataset Challenge - Cus'[Satisfaction_Score] <= 6, "DETRACTOR",
   "INVALID"
 )

   Age Group (binned via Group By in Power Query):
   25–36
   37–48
   49–60

3. Key DAX Measures

   DAX
   Count of Customers = COUNT('DataDNA Dataset Challenge - Cus'[Customer_ID])

   Total Satisfaction Score = SUM('DataDNA Dataset Challenge - Cus'[Satisfaction_Score])

   Average Satisfaction Score = AVERAGE('DataDNA Dataset Challenge - Cus'[Satisfaction_Score])

   Satisfaction Rate % = 
   DIVIDE(AVERAGE('DataDNA Dataset Challenge - Cus'[Satisfaction_Score]), 7, 0) * 100

VISUALIZATION

* Step 1: Distribution by Age,Gender and Location <Clustered Bar Chart>
          Each bar shows the count of individuals in a specific age group split by gender and location.

* Step 2: Percentage of Customers in each Loyalty Level<Donut Chart>
          Each Pie slice represents the percentage of customer in each loyalty level, with the largest slice indicating the most common tier.

* Step 3: Satisfaction rate in % <Gauge>
          The filled portion of the gauge represents the percentage of satisfied customers, with the arc pointing to the value.

* Step 4: Customer Distribution by Group and Location <Funnel>
          The funnel chart explains the percentage of customers in each group per location. This distribution helps us to understand where different
            types of customers are concentrated and informs targeted strategies.

* Step 5: Satisfaction Score by Age Group <Stacked Column Chart>
          Satisfaction Score measures how satisfied users are with a product or service. I segmented these scores by age group to identify trends and
          tailor improvements for different demographics.

* Step 6: Correlation between NPS Category <Pie Chart>
          Net Promoter Score(NPS) measures customer loyalty based on their likehood to recommend a product or service, rated on a scale from 0(not likely)
          to 10(extremely likely). Responses are grouped into three categories:
          **Promoters**(9-10): Loyal customers who are highly likely to recommend a product.
          **Passives** (7-8): Satisfied but unenthusiastic customers who may not actively promote a product.
          **Detractors** (0-6): Unhappy customers who may spread negative feedback about a product.
          
          NPS is calculated as:
          NPS=% Promoters - % Detractors

ADD INTERACTIVITY WITH SLICER

Slicers added for interactivity:
- Age Group
- Gender
- NPS Category
These allow dynamic filtering across all visuals.

KEY INSIGHTS & FINDINGS

Austin, TX → Highest proportion of Promoters → Strong loyalty and satisfaction in this region.
Phoenix, AZ → Highest share of Detractors → Indicates potential issues with product/service/experience; also has many Passives (balanced but not strongly loyal).
Geographic variation is significant → Supports targeted regional strategies (e.g., retention campaigns in Phoenix, loyalty reinforcement in Austin).
Age and gender segments reveal additional patterns for personalized improvements.

How to Use / Explore This Project

1. Clone or download the repository.
2. Obtain the original Excel dataset (if you have access to the DataDNA challenge file or equivalent).
3. Open CUSTOMER SATISFACTION & LOYALTY PROJECT.pbix in Power BI Desktop.
4. If prompted, update the data source path to point to your local .xlsx file.
5. Refresh the data and interact with slicers/visuals.

CONCLUSION

This interactive Power BI dashboard reveals clear geographic and demographic trends in customer satisfaction and loyalty. 
It highlights Austin, TX as a loyalty stronghold and Phoenix, AZ as an area needing focused improvement efforts. 
These insights can directly inform marketing, product, and customer support strategies to boost retention and overall experience.