Wonka Candy Sales Performance Analysis
Analyzing mock sales performance data to identify strategic growth opportunities using SQL and Tableau.
Project Role - Creator
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Project Year - 2025
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Project Type - Personal Project
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Project Location - Sacramento, CA​​​
Links
Project Summary
This Project Addresses a Critical Business Problem for the Wonka Company
​A fragmented and disconnected view exists for Wonka's sales data that has hindered strategic decision-making. To solve this, I developed a unified analytical framework by simulating a real-world analyst workflow. Using SQL for in-depth data analysis and Tableau for dynamic visualizations, I transformed raw data into actionable insights on divisional performance, regional trends, product profitability, and more which enables leadership to diagnose underperformance and capitalize on growth opportunities
Key Demonstrations
Section 1 - Dashboard
(1) - Interactive overview comparing actual Wonka divisional sales with division targets.
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(2) - KPIs display most recent years' total sales, total target, and overall attainment. ​​​​​
Section 1 - SQL
(1) - Joins sales dataset with target dataset.
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(2) - Extracts year from order dates within the data.
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(3) - Calculates total divisional sales, target percent attainment and difference from sales goals.
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(4) - Buckets each of the years' divisions based on target attainment.
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(5) - Groups results by year, division, target value.
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(6) - Orders by year, target, division. ​​​
Section 3 - Dashboard
(1) - Overview displaying total performance by product and by state.
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(2) - Includes filters allowing for analysis by various metrics and year(s).
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(3) - KPIs display total sales, gross profit, best-selling product, best-selling state, and lowest-selling state. ​
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SQL - Section 3
(1) - Extracts year, states, product names from sales dataset.
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(2) - Calculates total sales & total gross profit.
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(3) - Groups and orders data by year, state, then product name.​
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Section 6 - Dashboard
(1) - Dashboard displays each product's profitability and designates each into profitability quadrants.
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(2) - KPIs display highest product profit margin, average profit margin, and lowest profit margin.
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(3) - Quadrants (premium products, action required, cash cows, volume plays).​
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Section 6 - SQL
(1) - Extracts product names, unit price, unit cost from product pricing dataset.
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(2) - Calculates profit margin for each product.
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(3) - Uses CASE statements to group products in 3 different profitability buckets.
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(4) - Groups by product name, unit price, and unit cost.
Key Findings & Insights
Company Performance is Driven Entirely by the Chocolate Division
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The company is heavily dependent on its Chocolate division which consistently exceeds sales targets. Its strong performance masks the critical underperformance of the other two divisions, especially the Sugar division.​​
The Sugar Division is Failing Due to a Lack of Sales Volume, Not Poor Profitability
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The Sugar division is the primary cause for Wonka missing its overall targets, never achieving more than 1.4% of its goal in four years. The core issue is a severe lack of sales volume, with products like Kazookles contributing almost nothing to the bottom line.
Sales are Geographically Concentrated in High-Population States​
A few key states like California, Texas, and New York drive the vast majority of Wonka's sales. This strong correlation between population and sales highlights dependence on these markets. A significant opportunity and potential risks are present due to lack of market diversification.
Lickable Wallpaper is a High-Revenue Anomaly with Low Customer Loyalty​
As a non-chocolate product, Lickable Wallpaper stands out by generating substantial revenue from a high price point rather than high sales volume. Data shows it is treated as a novelty item with an extremely low repeat purchase rate (4.55%) indicating customer loyalty challenges.
Distinct Seasonal Trends and Customer Segments Offer Clear Growth Opportunities​
Wonka's sales consistently peak in the third and fourth quarters, presenting a clear opportunity for targeted holiday marketing and inventory planning. Customers can be segmented into distinct groups like "Champions" and "Bulk Buyers" which allows for tailored marketing strategies to raise value.
Tools & Process
Preparation & Data Setup​
I begun by sourcing the mock dataset from Maven Analytics, organizing the files, and uploading them into a pgAdmin 4 database for querying.
SQL Query Development​​
I wrote over 30 exploratory SQL queries to investigate the data, ultimately refining them into seven key queries to power the analysis and visualizations.
Visualization in Tableau​
The outputs from the SQL queries were imported into Tableau, where I designed, built, and refined a series of interactive dashboards to clearly present the findings.
Insights & Reporting​
I carefully reviewed each dashboard to extract key insights, which were then compiled into a comprehensive report detailing the methodology, findings, and recommendations.
Publishing & Integration​
Finally, the interactive dashboards were published on Tableau Public, and the complete report and SQL code were shared on GitHub and integrated into my portfolio website.