Food and Beverage

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Machine Learning - Malaysia's Starbucks Customer Loyalty Prediction

Model Accuracy: 90%

Tags: #Starbucks #Classification #Prediction #FnB #Malaysia #KualaLumpur #DataVisualisation

Background

Each food and beverage business needs to track their customer base. The purpose of tracking is to give a full understanding the customer touchpoints. Businesses can then progress to better measurement and attributes to specify any kind of content, campaigns, promotion and marketing channels.

Starbucks is one of the businesses with a huge customer base. Identifying their customer segment is crucial for them to make data-driven business decisions. Moreover, based on the identification, Starbucks can do effective marketing without spending more, generate higher quality leads, and improve customer retention.

Solution

Analytico Asia used a dataset of Malaysian Starbucks customer survey responses. The objective for this dashboard is to predict the loyalty outcome using a Machine Learning (ML) model. Each customer segment provides measures like membership type, locations, age, gender, annual income, money spent on Starbucks product and time spent in their outlets, allo f which have been used in building a ML classification model.

Video Demo