Statistical Analysis Stack Exchange
Statistical Analysis Stack Exchange

Questions tagged [churn]

Churn rate is used in business as a measure of the process of losing customers: see http://en.wikipedia.org/wiki/Churn_rate.

83 questions
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Survival Model for Predicting Churn - Time-varying predictors?

I am looking to build a predictive model for predicting churn and looking to use a discrete time survival model fitted to a person-period training dataset (one row for each customer and discrete period they were at risk, with an indicator for event…
B_Miner
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Modeling customer churn - Machine learning versus hazard/survival models

Is their any rational (theoretical, substantial, statistical) to opt for either machine learning or hazard models when modeling customer churn (or more general, event occurences)?
majom
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7
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Machine learning for activity streams

My data takes the form of a stream of events for each customer in my sample. For a given customer, the stream takes the form of a list of events over time: At T1, customer C1 bought 1 unit of product X At T2, customer C2 bought 1 unit of product…
akbertram
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What's the probability a rabbit will return to a (certain) forest?

Let's assume we have a forest. And there is a breed of rabbits that is visiting that forest all the time. It is possible to distinguish every individual rabbit. There are devices in that forest that count the visits of every individual rabbit on a…
Raffael
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4
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Splitting between train/test for customer churn survival models

I am a bit confused on how data can be split between train/test and "live" data for predicting churn using survival models such as the one in RandomForestSRC package. Goal of the model is to predict how long a currently active customer will remain a…
jjreddick
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4
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Predicting customer churn - train & test sets

I'm struggling with a problem where I'm trying to predict customer churn. I have monthly snapshot data going back several years, and tags for whether a customer left during a given month. My main question is whether I should be using the entire…
Some Guy Using R
3
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1 answer

Carrying Out Interventions Based on ML "Feature Importances"

Recently, I have been studying causal inference and have come to a bit of a crossroads with respect to making decisions based on the analysis of data (especially in a business/industry setting). Specifically, I am referring to common problems like…
aranglol
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survival analysis unobserved customers

I have got an extensive dataset of customers in a certain industry. I will build a survival model of churn on the customers. Some customers data back to 1990 and are still, as of 2020, customers in the dataset. The dataset was constructed in 2012. I…
Cardinal
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Low probability levels when doing logistic regression

I am building a Logistic regression model for a churn problem. When I scored the out of sample data set, I find very low probability levels as the output probability. Conventionally, I would look for .5 as the cut off but this scored population…
ayush
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What kind of model should I use for churn risk prediction?

I have a data set containing many client's id, and its behavior characteristics measured each month before churn or censored. Data looks like: id || lifetime period || folow-up time before churn of censores || churn or censored || large list of…
Kg_1
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Reasonable approach for modelling churn (survival) and choice of intervention campaign (multinomial regression)?

I've only recently moved into customer analytics, and would love to get some advice around designing a reasonable approach to modelling my data. I want to be able to predict customer churn (that is, predict if individual customers are going to leave…
Meep
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Predicting customer churn

I'm trying to decide how to go about this problem. I have a large database of customers, both who have churned at some point, and who are current. I'm not sure how to create test/train sets from this. I would like to make a model that can predict…
Grant McKinnon
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Many-to-many LSTM with large number of (correlated timestamps)

We are considering building a set of CRM tools (i.e. churn) using LSTM network for our online store. LSTM is chosen since it can handle naturally sequential nature of our (i.e. transactional) data, and since we can interpret the hidden states as…
Mark
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Predicting churn in non-contractual setting - correlation problem

Im trying to predict customer churn in a non-contractual setting, which mean we cannot see exactly when the customer is churning. Therefore we have created our Y variable (churn) by saying: if days_since_last_purchase > 365 days then 1 (churned)…
Søren Therkildsen
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How to label Churn/Not Churn and perform Survival Analysis on Transactional Data without Subscription

I have longitudinal transaction data of a retail store where each row is a transaction done by an individual. I would like to perform a survival analysis to analyse how long a customer will transact before churning. For CoxPH model, it requires…
krishna koti
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