Data Science

Data Science #

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Churn Management

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:

Churn Problem Framing

Churn - Problem Framing #

Churn reporting #

  1. 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)

  2. Why did it happen?

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.

Pivot transformations and Data federation #

We select specific columns in order to create transitional tables with the desired pivot columns.

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.
  • Collective outliers: Collective outliers can be subsets of novelties in data such as a signal that may indicate the discovery of new phenomena.

Most common causes of outliers on a data set #

  • Data entry errors (human errors)
  • Measurement errors (instrument errors)
  • Experimental errors (data extraction or experiment planning/executing errors)
  • Intentional (dummy outliers made to test detection methods)
  • Data processing errors (data manipulation or data set unintended mutations)
  • Sampling errors (extracting or mixing data from wrong or various sources)
  • Natural (not an error, novelties in data)

Hint

SNA