Starbucks Customer Segmentation Analysis with Python

Starbucks Customer Segmentation Analysis with Python

Starbucks Customer Segmentation Analysis with Python. I took on this project to understand how one could use data to inform business decisions and it was a great learning process.

There has been lots of talk around using data for marketing and you have probably heard about the high level applications of data usage on Netflix like the story of Cambridge Analytica in The Great Hack or more recently, Social Dilemma. This project is not as high level as that but in a way helps marketing teams understand their users and plan marketing campaign. I took on this project to understand how one could use data to inform business decisions and it was a great learning process.

Do you know Starbucks?

Starbucks is an American multinational Corporation with various chains of coffeehouses and roastery reserves. Seattle, Washington is its headquarter. With over 28,000 locations with an army of employees, Starbucks’ mission is “To inspire and nurture the human spirit — one person, one cup and one neighborhood at a time.”

To do this, the company has to keep making sure it targets the right customers at the right time through their customer journey. It has a diverse customer base and understanding patterns of customer behavior is a crucial need for the business. The data for this was a gotten from a software simulation of what the real customer base of Starbucks looks like and I my goal was to group customers into different demographics and then understand which offer they responded to more (offer completion).

Offer type:

  • BOGO (Buy one get one)
  • Discount
  • Informational

BOGO and discount comes with rewards but the informational doesn’t come with any. It’s more like a normal brand campaign on products it sells. I wasn’t interested in the most viewed offer but number of completed offers since companies are looking at sales and if they do complete the offer, that might signals that customers might want to see more of those campaigns.

data-scientist data-science data-visualization customer-segmentation

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