Douglas Woollam

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SQL & Power BI | Revenue & Target Analysis Dashboard

Made for Mallow - Liscarroll Landscaping in my role as a Data Analyst. Recreated with artificial data.I built the a BI stack for a landscaping business I co-founded.
Developed an SQL script and Power BI report to replace manual processes. Increased profit by 20%, identified high value towns to focus marketing and service checkups and saved 1.5hrs weekly of reporting time.


These are the questions I was interested in answering:1) Which customers are below or on target of €15 per hour?2) What is our earnings potential and recommended price?3) Which towns bring in the most revenue, profit & profit/hr?4) Is there towns where marketing and work should be focused on?Here are my key takeaways:1) Increased profit 20% by correcting prices to hit target revenue per job.2) Flagged towns with high value customers, prioritised marketing and proactive service checkups in those areas.3) Towns farther from the business had low Avg profit/hr, so pricing is now adjusted for distance.4) Weekly reporting time dropped by 75% using this data pipeline.


I took the following steps to create my analysis:Excel for data input, Python script to convert excel to .csv, .csv to input data for PostgreSQL database server, to a Power BI dashboard.Python Script:
My python code was used to convert individual Excel pages into individual CSV files to import data into my Postgres SQL Server.View Python Script on my Github

SQL Script:
170 line SQL script used to Build tables from the CSV files. Defines keys and data types, NULL/ empty value handling. CTE for dashborad metrics.View SQL Script on my GithubBelow is the base CTE on fact_work_order, normalises service_charge nulls to 0, rounds to two decimals, and outputs dashboard metrics: operating_cost, total_profit, profit_per_person, total_time, and profit_per_hour.184660545

Power BI Dashboard:

Overview tab:This dashboard includes slicers for below or on target jobs, year and job type. The map and revenue by town bar chart visual uses a field parameter for toggling between revenue, profit or profit/hr for Flagging high value towns. The column chart & line chart can be drilled up or down for time series analysis.

Report tab:The report page presents a job level table with key metrics such as work date, customer, duration, distance, crew size, revenue, profit, profit per hour, operating costs, target revenue, earnings potential, and price status. All slicers are synced with the summary page.

DAX Measures:

18 DAX measures were created for both tabs in the dashboard.DAX: Target Revenue

DAX: Revenue

DAX: Profit/hr

Here is how the DAX measures appear in the measure selection pane.

A loom video of the dashboard in action

Conclusion
Overall, this project demonstrates how a data pipeline built with Microsoft Excel > Python > SQL > Power BI can transform raw operational data into actionable insights that increase profit, identify high value clients, and support smarter business decisions.

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Tableau | Christmas Sales Analysis Dashboard

Made for Apple in my role as a Data Analyst. Created with artificial data.Why this project?Christmas is a major time for sales around the world, and making the most of the holiday rush can be a bit challenging. In this task, I helped Apple analyse Christmas data to uncover insights into customer behaviors during the festive season.The dataset provided includes anonymized Christmas sales data across various product categories, customer demographics, and marketing campaigns.


These are the questions I was interested in answering (Click links below for details): Customer Segment
1) Which customer segments, partner leads and Sales Leads drive the highest share of Christmas sales? Best Performing Products
2) Which products perform best, and how have their sales changed over time? Geography
3) Which countries and Store Leads contribute the most revenue, and where are the biggest growth/decline trends? Covid
4) How did Covid impact Christmas sales, and has the market recovered since? Pricing
5) How does unit price affect sales quantity across different customer segments? Shopping Timing
6) Do shopping patterns differ by month, gender and day of the week?


Here are my key takeaways:1) Children (ages 1–11) contributed nearly 40% of total Christmas sales, with Smartphones and in-store shopping being their top preferences.2) The top 5 best-selling products, all for children, declined by up to 15% over four years — highlighting changing consumer trends.3) United States led with €6.2M in sales and US Apple Stores leading with €2.5M in sales, while Netherlands grew 14%, and Canada declined by 12% since 2018/2019. Irish Store Select (Compu B) increased sales by 19.3%, which could be targeted for future sales leads.4) Covid caused a -3.2% dip in 2020, followed by a +1.9% recovery in 2021, though sales hadn’t fully rebounded.5) Customers tend to buy 1–5 products per transaction, but when making bulk purchases, they preferred 5, 10, 15, or even 30 items. This suggests an opportunity for special pricing on bulk purchases to encourage upselling.6) December accounted for 77% of in-store sales, showing a strong opportunity for seasonal promotions. Shopping timing varied — females preferred Sundays and Mondays, while males leaned towards Thursdays for gift buying.
Power BI Dashboard:

Customer Segment tab:This tab helped answer the questions on Customer Segments, Best Performing Products, Geography, and Covid. It revealed that Children (ages 1–11) contributed nearly 40% of sales, highlighted the decline of top-performing products, mapped regional performance, and showed how sales dipped during the pandemic.

Pricing & Promotion tab:This tab helped answer the questions on Pricing and Shopping Timing. It analyzed how price variation affected sales, identified bulk promotion opportunities, and pinpointed December’s 77% in-store sales share as the key window for promotions.

Data CleaningInstead of looking at the data by calendar year, data was grouped into Christmas Seasons to get a better understanding. For example, November 2018, December 2018, and January 2019 are combined into Xmas 2018/2019, considering these three months as one season for retailers and shoppers alike.

• To make things more business-friendly, some fields were given new names. For example, Purchase Type (In-store, Online, Xmas Market) is now Shopping Channel.• Since most of the data is from EU countries, I assumed Euro as the currency. In the real scenario, this needs to be clarified with the business.• A Calendar Table was added to enhance time-related analysis.