Download CV

Xmas Gift Sales Analysis

This project inspired from a Linked In community member Anh Leimer , who took part in a coding competition and won it with this dashboard.

The dataset was provided on her Github repository.

Dataset was pretty clean and to cover up with the missing dates, a list of dates have been generated using M code in Power Query.

Since, November , December and January are considered the peak time for Christmas sales

so a separated conditional column is added to show the Xmas Seasons for each year .

undefined

1: Purpose: This dashboard provides insights into customer segments, shopping behavior, and regional performance during the holiday season.

2: Metrics and KPIs:

Key Metrics: Total Sales, Quantity Sold, and Profit for the Latest Season, including year-over-year (YoY) growth comparisons.

Customer Segmentation: Visualizes which customer age group and gender drove the most sales, with segmented pie charts.

Shopping Channels: Indicates customer preferences for in-store, online, and Xmas market shopping channels.

Top Countries: A bar chart ranks countries by sales, showing where the highest revenue was generated.

3: Key Insights:

Customer Preferences: Customers aged 18 and over were the primary segment driving sales.

Regional Success: Countries like Sweden, the Netherlands, and Germany led in sales, providing a clear target for potential marketing efforts.

undefined

1. Dashboard Focus: Pricing and promotional effectiveness on sales quantity and unit pricing.

2. Metrics and KPIs:

Price Impact: Scatter plots show the relationship between average unit price and quantity sold across different customer demographics (Adult, Children, Teen).

Quantity Sold by Channel and Month: Visualizes trends in quantity sold by channel, and highlights high-demand months.

Customer Gender & Time Analysis: Heatmaps illustrate shopping patterns based on gender, day of the week, and time of day.

3. Key Insights:

Promotional Timing: Higher sales volumes in December, with a mix of in-store and online shopping.

Gender & Time Trends: Notable concentration of shopping activity during morning hours, helping in planning promotional timings.