One of the major EU-based manufacturing companies tasked Auriga to develop a solution analyzing visitor’s behavior on the web portal and purchase history to predict prospective purchase, increase engagement and boost sales.
55K users records, 7K web-pages, 5K positions in store.
Churn prediction: refactoring and improvement of existing approach.
Data processing transferred from Azure + BigQuery to AWS Redshift.
Accuracy for recommender system increased (0.83).
Data processing accelerated by 10x (1 hour -> 5 minutes).
Data inconsistency decreased (16% -> 5%).
Fully automated cloud CI/CD.
Recommender system: developed from the scratch in 3 months from start.
Increased level of customer’s engagement in sales process and communications (>10%).
Increased sales turnover irrespective of seasonal and other effects.
Scheduling and data processing pipeline fully automated.
Notification system launched.
Python (pandas, sklearn, catboost)
AWS and Azure environments
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