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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Best practices on next 30 days Churn Prediction using ML Classification

I have developed a few ML classification models as Logistic Regression, Random Forest, XGBoost... on a dataset previously separated randomly on Train and Test sample. Some of the selected variables ...
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Correlation between Customer Experience vs Churn

I am doing an analysis for my company to see how important Customer Experience (CE) is to churn. We have been collecting CE over the last few years with the volume of around 2,000 data per month (out ...
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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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How to convert multiple rows into single rows in Python for prediction for next t days?

I have time-series data. I have taken the dataset from Kaggle [https://www.kaggle.com/code/kp4920/s-p-500-stock-data-time-series-analysis/comments]. So, how can I bring multiple rows into single rows ...
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Duration in Survival Analysis for churn prediction

I'll be running survival analysis to help predict churn risk in a telecom company. However, I've some questions regarding the best way to compute the 'duration' target feature in my dataset: I have ...
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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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Use the same user as both positive and negative observation in a churn model

Can I use the same user as a positive observation and a negative observation in a churn model? For example, if a subscriber churned at some point, his label is one. If he returns and is an active ...
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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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2 votes
1 answer
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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 ...
2 votes
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345 views

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 ...
2 votes
1 answer
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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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Discussion on building logic for Churn for monthly renewal [closed]

I have a subscription based business dataset which looks like this: ...
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What are paralell training and attention mechanism?

I read a quite interesting paper here: http://hanj.cs.illinois.edu/pdf/kdd18_cyang.pdf Accordingly, the basic idea is to combine clustering and churn prediction so that it can imply some insight from ...
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How to predict customer churn (attrition) one month after start date? [closed]

I am trying to understand the strength of impact of variables on customer churn (attrition). I want to predict the probability that a customer will churn after time period t (after 1 months, after 3 ...
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Too many False Positives with Unbalanced Data

I am trying to predict customer churn in a telco company, using R.The dataset is very unbalanced, the target is around 0.6% of the base. 8,746 Customers will Churn 1,396,664 Customers do not churn I ...
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3 votes
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Longitudinal panel data classification

My problem context specifically lies in churn modeling, where accounts have account-specific attributes (like industry, number of employees, etc), but also have longitudinal yearly data (product usage ...
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Churn prediction on a highly passive and imbalance dataset

I'm trying to create a model to predict churn in the insurance industry. The objective will be to ' predict the probability of each member that will churn next month' i created a one row per member ...
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