Propensity score predictor helped our client predict the customers likelihood to purchase a product
Client is a leading e-commerce company selling baby nutrition products. They want to leverage the user’s website behavior.
The client runs various marketing campaigns online and their marketing team would like to have information on their customer profile.
The client can then run targeted marketing campaigns to the selected customer which matches their profile
Absence of a proper mechanism to perform customer profiling
Lack of real-time data
Absence of a proper solution to consume the data.
Ameex developed a solution which extracted data from Google Big Query and stored in Cloud Storage in a structured manner. The analytical layer consists of the model to generate the propensity score, which indicates the likelihood of a customer to purchase the product. This also gives the flexibility to run targeted campaigns to appropriate groups of customers.
Data from Big Query is fetched and moved to Google Cloud Storage
Dask Parallel processing handles data processing tasks like handling missing values, categorical variables etc…
Last 30 days data was taken and feature engineering was performed on it
Model is developed using LighGBM and trained using Cross-validation to prevent overfitting.
Features: This application includes
Option to enter the customer Number and view the propensity score.
Option to view the list of customers within a specific score range
A solution which gives the propensity score and determines the likelihood to purchase a product
An easier consumption mechanism which uses real time data of enrolled and non-enrolled users
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