I delivered this project as part of my Data Analytics Career Accelerator at the London School of Economics (LSE).

I was provided with a csv file of marketing and customer information, and asked to explain to the company what the data was telling them, and make recommendations to grow the business.

Using Excel and SQL I cleaned and restructured the data (every analyst’s favourite task!) into easily understandable demographics (eg age, marital status, location), spending habits and conversions by advertising channel (eg print media, social media, radio).

I then grouped the customers into understandable groups including:

• Wealthy supporters (high spend & high income)

• Bargain hunters (frequent visits and effective use of the customer loyalty scheme)

• Frustrated online shoppers (multiple visits, but few purchases)

And used this process to identify the company’s best customers, best products, best locations, and best advertising channels, alongside areas for growth and deprioritisation.

I used Tableau to illustrate these findings, and made evidence based recommendations for business growth, including:

• Focusing on:

  • Key customer groups (eg families with children)

  • Key growth locations (eg Macedonia, Germany)

  • Key products (eg liquor, meat)

• While dropping ineffective advertising channels (eg brochures) and suggesting next steps to use the available data more effectively.

I later returned to the project, and used Robyn (a Marketing Mix Modeling analysis) in R, to identify how to maximise the marketing budget, including examining which channels were returning the best marginal return on investment, and could therefore be expanded, and which were under-performing or at capacity.

Don’t hesitate to get in touch at ciaran.osborne@gmail.com if you need help understanding your data, and growing your business!