Delivery Performance & Customer Impact Analysis
A Power BI dashboard exploring delivery reliability and its impact on customer satisfaction.
Embedded Files
Context
Context
The project was completed as part of a group assignment during the Generation UK Data Analyst Programme. Our team developed a collaborative Power BI dashboard to uncover operational insights and provide business recommendations. My focus was on analysing the company's delivery performance.
Aim
Aim
The aim of my analysis was to evaluate delivery reliability, compare estimated versus actual delivery times, and assess how delivery performance influences customer review scores.
Approach
Approach
This analysis followed a structured process, from preparing and manipulating the data to analysing delivery performance and customer impact.
🗂️Data Preparation
🗂️Data Preparation
Imported the CSV dataset into Power BI Desktop
Cleaned and structured the data in Power Query
Corrected data types for dates and numerical fields
Split combined date and time columns where required
Renamed columns to improve clarity and usability
🧮Data Calculations
🧮Data Calculations
Created calculated columns to measure delivery performance:
Delivery Duration (the difference between estimated and actual delivery dates)
Actual delivery time in days
Estimated delivery time in days
Delivery status classification (Early, On Time, Late)
Build DAX measures to analyse delivery trends:
Average estimated delivery time
Average actual delivery time
Month - Year time grouping for trend analysis
📦Delivery Performance Analysis
📦Delivery Performance Analysis
Analysed the number of orders delivered early, on time, and late
Compared average estimated vs actual delivery times over time
Examined the distribution of delivery durations to identify patterns and outliers
Identified gaps between expected and real delivery performance
🛍️Customer Impact Analysis
🛍️Customer Impact Analysis
Analysed customer review scores based on delivery outcome
Compared ratings for Early vs Late deliveries
Identified the relationship between longer delivery times and lower customer satisfaction
Delivery performance dashboard comparing estimated vs actual delivery times, delivery outcomes and their impact on customer satisfaction.
Key Insights
Key Insights
Delivery Estimates Are Overstated
Delivery Estimates Are Overstated
The average estimated time is consistently longer than the actual delivery time. This suggests delivery promises may be overestimated, causing most deliveries to appear earlier.
Early Deliveries Dominate
Early Deliveries Dominate
The majority of deliveries are classified as early, indicating generally reliable operations but potentially inaccurate delivery estimates.
Late Deliveries Impact Satisfaction
Late Deliveries Impact Satisfaction
Late deliveries are strongly associated with lower review scores, showing that delays negatively affect customer experience.
Delivery Times are Generally Consistent
Delivery Times are Generally Consistent
The distribution of delivery durations shows most orders fall within a narrow time range, with only a small number of outliers.
Recommendations
Recommendations
The analysis suggests several practical steps to improve delivery performance and protect customer satisfaction.
🚚Improve Delivery Estimates
🚚Improve Delivery Estimates
Review how estimated delivery windows are calculated so they better reflect actual performance. More accurate timelines can improve customer trust and reduce perceived delays.
⏰Reduce Late Deliveries
⏰Reduce Late Deliveries
Late deliveries have a disproportionate impact on customer satisfaction. Identifying common causes of delays could support targeted operational improvements.
📨Track Delivery Reliability
📨Track Delivery Reliability
Delivery performance should be monitored alongside customer review scores as a key service indicator. This allows service issues to be detected early and addressed proactively.
📦Investigate Delivery Outliers
📦Investigate Delivery Outliers
Orders with unusually long delivery times should be reviewed to determine whether they reflect data quality issues or genuine operational bottlenecks.
Reflection
Reflection
This project improved my ability to analyse operational data and translate performance metrics into meaningful business insights. I strengthened my use of DAX for time based analysis and gained experience contributing focused insights within a collaborative dashboard project using Power BI Service.
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