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.
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Correct way to select data for (probabilistic) churn model?
Context: The team I work for would like to model the probability that a customer will churn given the specific covariates of the customer. We have two main ideas: a model using survival analysis and a ...
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Surival Prediction - Train/Test data vs Production data
I have a need to create a churn prediction model and it seems like a survival model fits the bill since my data is right-censored (there are many customers who have yet to churn, or in other words, ...
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Re-calculate accuracy, precision and recall after treatment effect in a model
Working in a churn-prediction model where the goal is to detect the players that have a high chance to churn from the site and send those players an offer to keep them in site.
In the initial training ...
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Daily monitoring of churn prediction model
I've written and trained a churn model that is scheduled to run every day and make new predictions for the probability of each customer to churn within coming 365 days, from the day the scoring is ...
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Logistic Regression Pattern in Deviance Variance Across Variables
I fitted a Logistic Regression model for a Customer Churn dataset with the following results
I tested this model with a validation set and calculated the ROC AUC score, which was approximately 0.85 – ...
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Can Shapley Value Analysis help with this problem?
Consider a service like Netflix. A drop in user engagement is a leading indicator of churn (users unsubscribing). They try various things to keep people hooked. Other than making engaging content ...
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Is the right set of customers to use in my churn model?
I am creating a churn model in python. The full dataset has around 90k records stretching back many years. I'm using a subset of the full dataset. This subset only includes clients where we've worked ...
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Cox Regression used for business churn/ lifetime value
Cox regression seems useful as a broad strokes model for measuring survival/ churn of a subscription-based product/ account lifetime estimate -- and the broad factors that might influence the survival ...
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Churn prediction model
I've constructed a training dataset by creating monthly timestamps. However, a significant issue has arisen: there's a substantial data imbalance (240,000 rows for active clients versus only 500 for ...
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Survival curves, counter-intuitive results for customer churn -- lower risk of churn for users who've previously churned? [closed]
I'm trying to work out some explanation for a result I'm seeing where I have the Kaplan-Meier plots for monthly customer churn risk. In aggregate, it looks fine, but I've broken it down into subgroups ...
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How to better model customer attrition?
I've just started practicum work for a SaaS company, and trying to build a customer attrition (binary classification) model for enterprise SaaS product with a target variable indicating whether or not ...
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To check if the churn probability score from old and new model is similar
I have calculated the churn probability score for every customer id using glm model. So, I have a data frame with every customer id and its churn probability score. Ex cust_id 1 has a score of 0.11 ...
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Calculating after how many purchases a customer becomes loyal (sticky)
I have a dataset of customer purchase history and whether or not they have churned.
This is live data so for the customers who are still active we don't know if/when they will eventually churn.
I been ...
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Churn Risk Modeling without ML
Is it readily possible to do predictive churn analysis (i.e., associating a churn risk with every individual/customer) using statistical tools (e.g. in Excel) not involving the use of machine learning ...
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Can I calculate Churn Rate from Retention Rate?
I am working on a model where some of the numbers need to derive from various data points.
I want to confirm if the churn rate can be calculated by the following formula:
Churn Rate = 1 - Retention ...
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Churn model- how to handle new users without enough historic data?
I'm making a churn model.
My observation window (historic data) length is 3 weeks.
There are some users that are not been registered to the app that I'm analyzing for three weeks, and as a result, I ...
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Evaluating a survival analysis model against a binary classifier
I'm new to survival analysis and looking to use it to help a telcom company better identify clients at risk of churning. Their current model predicts risk of churning in time windows (1month, 2months, ...
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Rule based label - For attrition risk
I have 3 domains of supplier data (Jan 2017 to Jan 2022) and they are as follows
a) Purchase data - Contains all the purchase (of product) data made by the suppliers with us. It contains columns such ...
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Predict churn in a range of time after observation window is finished
I'm building a churn model. Each user's historic data (observation window) is a constant period, but each observation window contains different dates.
For example the next figure:
Let's say, that the ...
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Should the observation window include the same dates for all the users?
I'm building a churn model that is trained on users' historic data(observation window).
For this example, let's say that I want to train the model on the last week of use of each user.
Is it fine ...
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The tradeoff between a small and big performance window
I'm developing a churn model and I don't know which size of performance window to choose.
My intuition is that if its size is too large (for example two years) the model will not succeed to predict it....
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what is it an activity window in churn model?
I know that in a churn model many times you define an observation window (historic data) and a performance window (also dependent window, or response window).
I have read an article that the authors (...
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Does multicollinearity affect EDA?
I have been working on a dataset pertaining to 'churn analysis'. I have been trying to demonstrate whether the customers that are being charged more are also the ones that churn more or not. My ...
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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, ...
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A question about precision
We have 2 Classification models (Random forest on balanced Data Set), the first one classify a Bank's client as a Churner (closing his account) or an active client, the second one classify a Bank's ...
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How to: survival analysis study design
I'm struggling to understand what the correct start date would be for my analysis.
I have cross-sectional data for an insurance company and the goal is to perform survival analysis to understand churn ...
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Base rate calculation for customer conversion
Question: What is the base rate of conversion for mobile versus desktop sites?
Total no of customers: 590381 Out of 590381, the Total no of customers that were converted: 701
These customers used ...
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Many Outlier Handling in Logistic Regression
I am working on Telcom data for Churn modelling. I have 18 categorical and 2 numeric variables (total charges and monthly charges) in my data set. After handling the missing values, I checked the ...
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Why is AUC so often use to compare performance of different models in churn prediction task?
I have to build model to predict churn and when reading related work on the internet I have realized that in most of the cases the AUC is used as a metric to compare different models. That's ...
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What is the best cross-validation scheme for churn prediction with time varying data per client?
I am looking for the best cross-validation strategy to test the performance of a churn prediction machine learning model (classification).
The model predicts if a client is going to churn in the next ...
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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 ...
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BTYD R package. Why dropping the first puchase in the BG/NBD model?
I am using the BTYD package on R to estimate the probability of a customer churning after my calibration period. At the moment, I have been following the walkthrough provided on the web which you ...
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Fixed vs. flexible observation window in churn forecasting
I want to build a binary classification model for predicting churn users(assume the user is a subscription).
In many articles like the next figure, I have seen that the observation window (customer ...
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Partitioning data set for training, testing, and production deployment of machine learning churn classifier
I am new to machine learning. I am working on churn prediction for a customer. I am wondering how best to partition the data for training/test/production deployment. My thinking is that churning ...
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Approach(es) to discover "conditional churn propensities"?
I'm aware of a variety of approaches to discovering “stand-alone” churn probabilities.
But I haven't been able to find by searching any info on “conditional” churn probabilities.
Use case: I'm a ...
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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). ...
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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 ...
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Interview Question at Gaming Company
I came across the following interview question :
In an online gaming company, customer churn is defined in terms of the
number of days of continuous inactivity of the player. So how will you
...
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Deploying a churn prediction model
When deploying models such as churn prediction models how do you deal with the fact the you are constantly predicting on observations you previously trained on?
I have an employee churn prediction ...
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Using probabilities on churn and death
I've been assigned with 2 models that forecast probabilities of death and churn for some clients. For every client, a 1 is assigned if the person left the company, or if he-she died, thus the task is ...
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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 ...
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Employee churn as time-to-failure/survival analysis?
I have an employee churn problem where I have data for every three months of employees in a company ranging from 2015-2019. Does it make sense to model this problem as a time-to-failure/survival ...
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Building a Classification model for predicting Customer Churn [closed]
I am currently building a Customer Churn Prediction model and the project is in the process of development of models. The client has given data till Sep 2019 and wants to check if the model is able to ...
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How should time series data be grouped?
I'm fairly new to time series classification. I have an employee churn problem where I have quarterly data of the employees available.
Should I group my observations by time (example in table A) or ...
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Unsure how to continuously train a churn model after my model has gone live
I'm having trouble describing this properly so I'll provide as many details as possible. First, here are a few details on the model that I am building:
I have built a classification model that ...
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Will historical data lead to target leakage?
I'm bulding a employee churn model. I've employee data from 2016 to 2019 (of people who stayed/left the company), my goal is to train using data from 2016 to 2018 and predict on 2019.
Since there's ...
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How to take advantage of historic data while doing churn prediction?
The problem: Predict customers who will downgrade their bank account category 2 months in advance.
The data: 100's monthly variables for each customer for the last year.
At first, I thought I could ...
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Churn Model Performance Measurement Metrics & KPI For Business
I have a customer churn model, which classifies people who are going to leave (Yes) from those who are staying ...
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User Churn - is it possible to find the moment that they will churn?
Hi experts out there -
I have a user behavior log (e.g. # of logins, send post..etc) and trying to come up with a churn prediction model. A part of the request that I was asked was to find the value, ...
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Survival analysis for fixed time period licencing
I need to predict attrition of customers who use product that has fixed time period (1 year) licence. I have monthly feature usage data, for example the product has 5 features f1, f2, f3, f4 and f5, I ...
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