Dashboard Portifolio

Daniel Iglesias C. Melo

Data Analyst | Data Scientist

Hi! I am Daniel, I graduated in Engineering Bsc., at Federal University of Pernambuco (UFPE) and delve right after into the Data & Analytics field, with a focus on Data Analysis and Data Science. My experience encompasses mathematical and statistical analysis, Machine Learning Modeling, and Data Visualization Reports

Projects

Here is a quick view of what will be presented in this portifolio

OBS: Some data had to be simulated, as it contains sensitive data from real companies.

Marketing Dashboard

The 'Online Retail Dashboard' is a well-detailed report, built with over +500 thousand invoice data records from a web-based retail channel.

It gives a clear view of the main KPIs related to digital marketing and enables you to deep dive to extract insights and provide data-driven decision-making.

Industrial-Quality Dashboard

Have you ever had the feeling that your system is on track and then it fails again? This solution was thought of because the company needed a digital transformation to deal with the fast-paced industrial environment.
This tool is a combination of Power Apps, Power Automate, and Power BI functionalities to architecture a broad quality-check system, with low maintenance and ease to use.

What makes them unique?

Every Dashboard has its own complexity and tests a new capability. Some require complex DAX functions, such as virtual tables, changing relationships or contexts, etc. Some require ETL knowledge to make Power Query independent, and some require good ERD design skills.

1.1 Online Retail Dashboard

This Dashboard combines UI/UX design build on Figma and Storytelling skills to provide the company with a full understanding of the data to build their marketing strategy. It combines metrics such as:

conversion rate: The percentage of leads who took the desired action, such as making a purchase.
Click-through Rate (CTR): The percentage of people who clicked on an ad or link in the campaign.
customer acquisition cost (CAC): The total cost of acquiring a new customer, including sales and marketing expenses, divided by the number of new customers acquired.
revenue per customer: The total revenue generated by the average customer.

By aggregating the data by customer ID, product, and purchase date. Additionally, can also segment the data by region or customer segment to gain deeper insights into the performance of specific campaigns or tactics.

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