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https://github.com/mrjxtr/coffee_sales_analysis

Full data analytics process from data gathering, data processing, data visualization and reporting on a small coffee shop sales data.
https://github.com/mrjxtr/coffee_sales_analysis

dashboard data-analysis data-cleaning data-visualization kpi-report pandas python3 spreadsheet tableau-public

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Full data analytics process from data gathering, data processing, data visualization and reporting on a small coffee shop sales data.

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README

        

# SEMI-ANNUAL SALES DYNAMIC DASHBOARD (TABLEAU)
## Coffee sales Dashboard

![Dashboard 1](https://github.com/user-attachments/assets/60c9faf9-d313-4edf-b4b9-ea483c95e097)

Click the link to see it in the public tableau.

Link: [View in Tableau](https://public.tableau.com/views/Coffee_Sales_Updated/SEMI-ANNUALSALESDASHBOARD?:language=en-US&:sid=&:redirect=auth&:display_count=n&:origin=viz_share_link)

### Business Task:

- Identify the coffee type with the highest sales and strategies to boost overall sales.

- Determine the top-performing employee in terms of sales.

- Assess sales volume to optimize inventory and prevent shortages.

### Process:

- I gathered and extracted data from an **Excel workbook** using **Python Jupyter Notebooks** and processed the data to ensure it was complete, valid, and reliable. (cleaning, manipulating, and wrangling) [see code](https://github.com/mrjxtr/Semi-Annual-Coffee-Sales-Analysis/blob/12d7dbcadcfac8bfce73a245ef6caad503c7ec47/updated_coffee_sales_clean.ipynb)

- Created **Sales KPI**, a **line chart** showing the **weekly sales trend**, a **sales-by-employee bar chart** to see **top employee performers**, and a **sales-by-coffee bar chart** to see **which coffee has the most sales**. [see image](https://github.com/user-attachments/assets/60c9faf9-d313-4edf-b4b9-ea483c95e097)

- Made the dashboard **dynamic** to filter specific data in the line charts and the bar charts when *clicked*. [see dashboard](https://public.tableau.com/views/Coffee_Sales_Updated/SEMI-ANNUALSALESDASHBOARD?:language=en-US&:sid=&:redirect=auth&:display_count=n&:origin=viz_share_link)

## Sales Performance Overview (Jan-Jun 2024)

### Key Insights:

- The overall **upward trend** in sales suggests that the **business is growing**, but the **decline towards the end of June** could be a concern. Analyzing factors such as seasonality or marketing efforts during this period could provide more context.

- **Rebecca Edwards** and Diana Collins are key contributors to sales success. It may be beneficial to examine their strategies and consider ways to replicate their success across the team.

- The **popularity of Latte and Mocha** could indicate customer preference, and there might be an opportunity to further promote these products, perhaps through bundling or targeted promotions.

### Total Sales & Volume:

- **Total Cups Sold**: 2,131 cups

- **Total Sales Revenue**: $9,453.50

- **Total Number of Customers**: 200

### Sales Trend Analysis:

- The Sales by Week line chart shows fluctuating sales throughout the first half of 2024. Sales peaked in the week of May 5th, 2024, reaching a high of $458.75.

- A notable decline occurred towards the end of the period, with sales dropping to $255.00 in the week of June 23rd, 2024.

- The trend line indicates a slight overall increase in sales over the period, despite week-to-week variability.

### Employee Sales Performance:

- **Rebecca Edwards** leads with sales of $2,686.25, surpassing the average.

- **Diana Collins** follows closely with $2,499.50.

- **Kevin Swanson** generated $2,347.75, while Evelyn Grant trailed with $1,920.00.

- The disparity among employees suggests that sales techniques or customer engagement might vary, with Rebecca and Diana outperforming the others.

### Coffee Type Sales Breakdown:

- **Latte** dominates sales, accounting for $4,406.00 with 2,203 cups sold.

- **Mocha** comes in second, generating $2,568.00 from 1,284 cups.

- **Cappuccino** sales amount to $1,858.00 with 929 cups sold.

- Americano and Espresso lag behind with $412.50 and $209.00 respectively, indicating lower customer preference or possibly a pricing strategy that influences demand.