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SALES STORE
Objective
The dashboard is
designed to monitor Sales Performance and Profitability across different
dimensions (time, product, category and payment method). The core goals is to
Maximizing total revenue and profit margins, Understanding customer purchasing
behavior (payment and sales channels) and Identifying top-performing products
and categories to optimize inventory or marketing.
• Data Cleaning & Transformation (Power Query)
• Dynamic Title Creation
• Custom Dashboard Layout & Design
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Tools Used:
• Power BI - Visualisation, Dax
calculations, and Data Report.
• Power Query – Data Cleaning and Transformation.
Metrics
✅ Highlight total sales and total
profit for quick performance snapshots.
✅Sales Type {Pie chart): Direct sales,
Online and Wholesaler.
✅ Payment Mode (Pie chart): Online and
Cash.
✅Top selling Product: Identify Top
Selling Product with Sales.
✅ Identify Top Category
✅ Daily Sales: Visualize daily sales by
using area chart.
✅ Monthly Sales and Profit: a visual
display of monthly sales and profit using stacked column chart.
✅ A clustered bar chart: list of
Products sales.
✅Using Treemap to visualize Categories sales.
Key Performance Indicator
|
|
|
2021 |
2022 |
Variance (%) |
|
|
Total Sales
|
$187K |
$214K |
14.4 |
|
|
Total Profit
|
$30.32K |
$38.59K |
27.3 |
|
|
Profit margin
|
16.2% |
18.0% |
1.8 |
|
|
Top Cat..volume
|
44K |
56K |
27.2 |
|
|
Top Prod..Value
|
$12.8K |
$13.89K |
8.5 |
Insight
✅Payment & Sales Channels: There is a perfect split between
Online and Cash payments 200K vs 202K. However, the Sales Type donut chart
suggests that while Online is significant, Direct Sales or Wholesale are represented
by the smaller segments which are secondary contributors to the volume.
✅Efficiency Gains: Profit grew at nearly double the rate of Sales 27%
vs 14%. This suggests better cost management in 2022.
✅Payment Trends: In 2021, Online was the leading payment mode by 52%.
In 2022, Online maintained its lead but shifted slightly to 53%, showing a
stable, digital-first customer base.
✅Seasonality: Both years show a significant performance dip in
December. However, 2022 showed much stronger mid-year performance
(August–October) compared to 2021.
✅Seasonality
(Monthly Trend): There is a massive spike in sales during January, followed by
a significant drop in February. Sales remain relatively stagnant/flat from
March through December.
✅Product Performance: While the volume of the top category increased
significantly from 44K to 56K, the Top Product unit count actually dropped from
79 to 69, even though its total value rose. This indicates a price
appreciation.
✅Daily
Volatility: The line chart shows high frequency. This suggests sales are driven
by frequent, smaller transactions rather than steady, large-scale contracts.
✅Product
Concentration: The Treemap (bottom right) shows that Category 04 and Category
02 dominate the business, while Category 01 is significantly smaller.
Strategy Recommendation
✅Analyzes on the December Drop: Both years show a sharp decline in
sales during December.
Action:
Investigate if this is due to inventory stockouts, seasonal closures, or a lack
of holiday marketing.
Goal: Implement a Year End Clearance promo to bridge the revenue gap.
✅High-Margin
Categories: Since profit percentage improved in 2022, the specific
"Category" that drove this growth is Category 04 expanded in the
treemap.
Action: Reallocate marketing budget from
lower-performing categories (like Category 05) toward the high-growth
categories identified in the 2022 treemap.
✅Optimize Top
Product Pricing: The 2022 data shows we are selling fewer units of our Top
Product but we are making more money from it.
Action: Test a bundle offer (Top Product +
a slow-moving accessory) to see if you can drive volume back up to 2021 levels of
79 units without sacrificing the new, higher profit margin.
✅Strengthen
Online Payment Incentives: With online payments consistently making up over 50%
of revenue: Action: Integrate "Buy Now, Pay Later" (BNPL) options or
loyalty points specifically for online transactions to further reduce the
friction of Cash handling costs.
To download the Report CLICK HERE
To download the Raw file CLICK HERE
To download the Readme CLICK HERE
Sales Store
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Objectives
The aim of the project is to enhance workforce retention by address the disparity between work/life balance and salary satisfaction to prevent turnover in high-demand roles. Align pay scales with market leaders (Data Scientists/Engineers) to remain competitive. Targeted Skill Development, Focus training and recruitment efforts on dominant languages like Python to match professional preferences.
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• Data Cleaning & Transformation (Power Query)
• Dynamic Title Creation
• Custom Dashboard Layout & Design
Tools Used:
• Power BI - Visualisation, Dax calculations, and Data Report.
• Power Query – Data Cleaning and Transformation.
Dashboard Overview
The dashboard provides visual insights into the
following
Key Metrics
✅Workplace
Culture: Happy with Work/Life Balance.
✅Salary
Satisfaction: Happy with Salary (Gauge).
✅Top Job Titles:
Average Salary
✅Salary gender:
Average Salary by Gender
✅Top Language:
Favorite Programming Language
✅Geography
Country of Origin
Insights
✅The Satisfaction Gap: Professionals are notably more satisfied with
their work/life balance (5.74) than their actual compensation (4.27). This
suggests that while the culture is manageable, pay may not be keeping pace with
industry expectations.
✅Python Hegemony: Python is the undisputed leader in favorite
programming languages, dwarfing R, C++, and Java. This indicates a highly
standardized technical stack across these data roles.
✅Salary Hierarchy: There is a clear descending hierarchy in pay from Data
Scientists > Data Engineers > Data Architects > Data Analysts.
Students and Database Developers represent the lower end of the earning
spectrum.
✅Gender Pay: The Average Salary by Gender donut chart shows a visible
majority (Male) in the survey pool, highlighting a continuing gender gap in the
data profession.
Strategy Recommendations
✅Compensation Realignment: Conduct a
deep-dive audit into Salary Satisfaction. Since this is the lowest-rated metric
(4.27), consider performance-based bonuses or transparent salary bands to
bridge the gap.
Goal: Focus specifically on Data Analysts and "Other"
categories where pay is significantly lower than Data Scientists.
✅Technical Stack Standardization: Invest in advanced Python training
and infrastructure. Since it is the Favorite language of the workforce,
supporting it leads to higher efficiency and developer happiness if needs be.
Goal: Phase out support for fringe languages that have low voter counts (like Java or C/C++) unless project specify.
✅Geographic
Recruitment Focus: Leverage the high concentration of professionals in the US
and India.
Goal: For US roles, focus on retention strategies (salary). for India-based roles, focus on scaling the workforce to take advantage of the large talent pool.
✅Diversity and Inclusion (D&I) Initiative Address the gender imbalance shown in the salary/participation chart.
To download the Report CLICK HERE
To download the Raw file CLICK HERE
To download Readme File CLICK HERE