In this Kaggle competition, the objective is to 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.
Data for this project can be found at the following location : https://www.kaggle.com/c/ga-customer-revenue-prediction/data. Input features:
The channel via which the user came to the Store.
The date on which the user visited the Store.
The specifications for the device used to access the Store.
This section contains information about the geography of the user.
Engagement type, either "Socially Engaged" or "Not Socially Engaged".
This section contains aggregate values across the session.
This section contains information about the Traffic Source from which the session origi$
An identifier for this session. This is part of the value usually stored as the _utmb c$
The session number for this user. If this is the first session, then this is set to 1.
The timestamp (expressed as POSIX time).
This row and nested fields are populated for any and all types of hits. Provides a reco$
This section contains any user-level or session-level custom dimensions that are set fo$
This set of columns mostly includes high-level aggregate data.