KPMG Data Analytics Virtual Experience

I recently participated in the KPMG AU Virtual Experience hosted by Forage, where I served as a data analyst for Sprocket Central Pty Ltd, a hypothetical medium-sized company specialising in bikes and cycling accessories. My primary responsibility was to assist the organisation in analysing their customer data to derive actionable insights that could enhance their marketing strategy.

Data Quality Assessment and Cleaning

To kick off the analysis, I began by assessing the data quality. Using Python, I loaded multiple Excel sheets containing customer information, transactions, and demographics. I identified several key issues, including missing values, irrelevant columns, format discrepancies, and duplicated entries.

After cleaning the data by removing unnecessary columns and standardising formats, I crafted an email to Sprocket Central outlining these issues along with proposed solutions. This step was crucial in ensuring that our analysis would be based on reliable data. (Refer to python code here).

Data Insights

Upon receiving a fresh dataset of 1,000 potential customers, each with various demographics and attributes, my goal was to extract meaningful insights. To achieve this, I developed a comprehensive approach on PowerPoint that included three phases: Data Exploration, Model Development, and Interpretation. This strategy encompassed data transformations, feature engineering, modelling, and reporting—all aimed at identifying high-value customers for targeted marketing.

To enrich our analysis further, I integrated various datasets using Python’s merging capabilities. This allowed me to create a unified view of each customer’s information.

Data Presentation

With the analysis complete, I turned my attention to presenting the insights through impactful visualisations. I used PowerBI to create an interactive dashboard that showcased key findings and analysis results. This dashboard not only highlighted which customers Sprocket Central should target from the new customer list but also identified broader market segments to consider. For example, I created bar graphs illustrating purchasing behaviours across different segments. These visualisations made it easier for Sprocket Central’s marketing team to grasp complex insights at a glance.

Conclusion

This experience with the KPMG Data Analytics Virtual Experience improved my skills in data cleaning, integration, analysis, and visualisation using Python. It reinforced the importance of thorough data preparation and demonstrated how effective data analysis can drive strategic decisions for a company. I'm excited to apply these skills in future projects!