Data Science #
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- Churn Problem Framing
- Churn - Problem Framing # Churn reporting # What happened? (Inactive point of view) In general cohorts work good here: start with a small population of churners and follow them over time Clustering churners based on project and modeling bottom line Example of small population of churners => “Churners who generate more profits, churns who explicitly expressed their insatisfaction”: or choose another prioritization criterion Monetary retention rates (monetary churn) Reversed cohorts: start with the churn date (Month / Year) and follow the churners retrospectively Product / offers / segments cohorts Maybe change of nomenclature/perspective to make problem framing more accessible to other shareholders (Product Churn, Account Churn, Usage Churn…etc) Why did it happen?
- Churn
- version du 25-12-2018 Gestion de l’attrition ou Churn management # Le churn est un anglicisme qui désigne la perte des clients. La gestion du churn figure parmi les piliers les plus importants à tenir en compte lors de la gestion de fidélisation, car l’acquisition à elle seule ne peut pallier aux pertes de la clientèle. (voir illustration) Étudier le churn pousse à réfléchir parallèlement à plusieurs actions pouvant contribuer à sa réduction et à la maximisation de la valeur de la relation client:
- Feature Store
- version 19-08-2024 Feature Store as a System # follows a logical flow seperates concerns functionally builds modular-ily Pipeline and code break-down # Data Selection and UC-specific processing # We start by filtering a reference table based on specific conditions aligned with the requirements of the use case. This creates a use case table on which we select and process pivot columns. Note: This should happen in the processing and modeling modules.
- Outlier Detection
- Types of outliers # With regards to the distribution # Univariate: can be found when looking at a distribution of values in a single feature space. Multivariate: can be found in a n-dimensional space (of n-features). With regards to the environment # Point outliers: single data points that lay far from the rest of the distribution. Contextual outliers: can be noise in data, such as punctuation symbols when realizing text analysis or background noise signal when doing speech recognition.
- SNA