Predict how much GStore customers will spend
The 80/20 rule has proven true for many businesses–only a small percentage of customers produce most of the revenue. As such, marketing teams are challenged to make appropriate investments in promotional strategies. Analyze a Google Merchandise Store (also known as GStore) customer dataset to predict revenue per customer. Hopefully, the outcome will be more actionable operational changes and a better use of marketing budgets for those companies who choose to use data analysis on top of GA data.
In this project, the aim is to analyze Google Merchandise Store (also known as GStore) customer dataset to predict revenue per customer.
The data is used from kaggle's Google Analytics Customer Revenue Prediction.
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link to the dataset : https://www.kaggle.com/competitions/ga-customer-revenue-prediction/data There are main two datasets:
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train.csv - the updated training set - contains user transactions from August 1st 2016 to April 30th 2018.
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test.csv - the updated test set - contains user transactions from May 1st 2018 to October 15th 2018.
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baseline.csv - Prepared submission file that predicts revenue generated per customer.
The evaluation metric used for this project is RMSE (Root Mean Squared Error) For more info on evaluation check on : https://www.kaggle.com/competitions/ga-customer-revenue-prediction/overview/evaluation