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Starbucks Challenge

Starbucks Challenge

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RECORDS

Starbucks Customers Segmentation and Prediction Models

  • Analysing the data set for STARBUCKS Customers and building a Model that predicts whether or not someone will respond to and complete an offer.
  • Analysing the demographic features for the customers(17,000 unique customers and 306,534 transactions) ; their gender , age , income , the membership period and the type of offers which they are interested.
  • Data analysis and data visualization have been done to understand the role of the features which controlling our model ,such as : Customers ‘s gender , Customers ‘s age ,customers ‘s membership , Customers ‘s income , offer duration …..etc.
  • Tracking the amount of money which has been spent by customers within the offer period and till the offer completed , to predict the profits that can be gained by other customers with the same features.

Raw Data for 17,000 Customers contains their Age, Gender, Income and Membership year.

Raw Data for 306,534 transactions contains Customers Id’s , Type of offer, offer Type (Bogo, Discount, informational),value spent

Many Question have been answered by BAYANAT:

How many offer received , viewed and completed by Gender, age , and Membership year?

What are the Top Customers of receiving , viewing and completeing the offers?

How much Profit forecast to be gained by Gender ,age, Membership year.

BAYANAT delivers a Prediction Models to Predict the customers who will complete the offers and the expected income related to them by implementing machine learning and artificial intelligence algorithms.

  • 65% reduction in lost sales due to inventory out-of-stock situations.
  • Warehousing costs decrease by around 10 to 40%.
  • 20% reduction in forecast error and a 30% reduction in lost sales.
  • 1-3% increase in Labours productivity.
  • Delivering defect detection algorithm to classify surface defects on a steel sheet by predicting the location and type of defects found in steel manufacturing by implementing cutting Edge Artificial Intelligence Algorithms.