Data Science

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?
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:
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.