Portfolio
Independent projects
Self-directed and practical projects I've built to apply marketing, data analysis and reporting skills to business questions. The portfolio includes completed work as well as projects that are still being developed.
Marketing Campaign A/B Test
To test whether an advertisement campaign produced a different conversion rate from a Public Service Announcement (PSA) campaign, and determine whether the observed difference was statistically significant.
A customer-level marketing experiment containing 588,101 observations. Python was used for data cleaning, exploratory analysis, visualisation and a two-proportion Z-test; Power BI was used for the interactive dashboard.
Overall conversion rates, conversion by campaign group, conversion patterns by weekday and hour, and the statistical significance of the difference between the two campaign groups.
The advertisement group recorded a 2.55% conversion rate compared with 1.79% for the PSA group. A two-proportion Z-test produced a Z-statistic of 7.37 and a p-value below 0.001, indicating a statistically significant difference in this experiment. The project recommendation was to prioritise the advertisement campaign while continuing to monitor performance through further experimentation.
Does the difference in campaign conversion performance provide enough statistical evidence to inform future campaign investment and testing?
Omnichannel Marketing Campaign & Consumer Behaviour Analysis
To turn customer and campaign data into a cleaner analytical dataset and use it to explore campaign performance, customer demographics, purchasing behaviour and omnichannel shopping patterns.
A dataset of approximately 2,205 customer records. Google BigQuery and SQL were used to clean and transform the data, and Microsoft Excel was used for reporting, PivotTables, PivotCharts and slicer-based analysis.
Customer demographics, household characteristics, campaign responses, purchasing behaviour and patterns across shopping channels.
The analysis was used to identify customer segments and purchasing patterns that could inform campaign targeting and marketing decisions. The project produced business recommendations based on the observed campaign and customer patterns.
Which customer characteristics, behaviours and campaign responses can help inform more targeted marketing decisions?
This was one of my earlier Excel projects. As I have developed my Excel and data-analysis skills further, I plan to revisit the project, deepen the analysis and improve the reporting/dashboard presentation rather than treating the original version as the final iteration.
Superstore Sales Analysis
To examine retail sales performance and identify revenue trends and seasonal patterns in a multi-year Superstore dataset.
Approximately 9,800 retail sales records analysed in Microsoft Excel using PivotTables, charts and spreadsheet-based calculations.
Annual sales performance, monthly revenue trends and month-over-month revenue growth.
The analysis showed an upward revenue trend across the years in the dataset, with 2018 recording the strongest annual performance. The project documented the findings and methodology for business reporting.
How did sales performance change over time, and what patterns are visible in the historical retail data?
This was an earlier Excel project from my portfolio development journey. Now that I have built stronger skills in Excel, data preparation and visualisation, I plan to revisit the workbook, extend the analysis and develop a more comprehensive dashboard.
Borokini Couture — Digital Marketing & Operations
To strengthen a bespoke fashion business's digital presence, improve online presentation and introduce a more structured process for recording customer-order information.
Supported website development, TikTok setup and content, Pinterest setup and publishing, branding and content-direction recommendations, business-account setup, visual product presentation and an Excel-based customer-order template.
Pinterest reached approximately 16,000 views during a period of active content publishing. A structured customer-order process was also introduced, but the available order data was not sufficient to support reliable customer, sales or revenue analysis.
How can a small business establish a stronger digital foundation while also creating a more structured basis for future reporting and analysis?
Lumiere Beauty Co. — Marketing Data Cleaning & Analysis
To clean and consolidate Q1 2026 marketing, customer and transaction data so campaign and customer performance can be analysed consistently ahead of a Q2 budget discussion.
Microsoft Excel. The workbook contains campaign reference data, monthly campaign performance for January–March 2026, customer records and transaction records.
The data-cleaning and validation stage has been completed. I am currently working through the analysis and have not yet finalised the findings or recommendations.
Campaign performance by channel and month, CTR and conversion rate by campaign, ROAS, customer counts and revenue by region and loyalty tier, and repeat-customer behaviour. The planned final output includes PivotTables, PivotCharts, slicers and an executive dashboard.