Questions tagged [churn]
The churn tag has no usage guidance.
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Is this Dataset somehow skewed?
I am working on a dataset that has 100K points, it's about Customer churn. So I don't know whether this dataset is skewed, incomplete or what. I tried doing some feature engineering on it but couldn't ...
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Predicting Year-End Outcome from Monthly (and Annual) Data
I have data on customers' usage of various product features over time. Each month, a customer can choose to use a feature or not.
I want to create a live system that produces the probability of a user ...
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Audience Segmentation question - Choosing the population to judge a new model through an A/B test
Would really appreciate your thoughts on the below problem.
There was an old model which gave an output of 10M emails of people who are expected to churn along with their churn propensity.
A new model ...
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Integrating time context in a machine learning model
Basically, what I'm curious about, are there any methods in machine learning to make the model take into account events that happen in real time that affect the data points during that time period. ...
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Training Dataset preparation for customer Churn at a specific Month
I have dataset of customers from 2019-2022 . My goal is to predict customer Churn at a specific point in time , say exactly 3 months from the observation point ...
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How to predict customer churn by a certain date?
I have a dataset of inactive users for the last 365 days, with columns:
When subscribed (e.g. 10.10.2022)
When unsubscribed (e.g. 12.10.2022)
and client info
I also have a set of data about active ...
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43
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BG/NBD to predict customer churn
I am using lifetimes package to fit BG/NBD model. The fitter has "weights" hyperparameter. According to official documentation the weights parameter is Number of customers with given ...
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Should I use "Recency" as an predictor for churn if I want to catch churners early?
I want to build a customer churn prediction model that predicts probability of churn the next day and I'm looking for some features that might be important for the target variable which has outcomes ...
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More representative data set OR higher model evaluation metrics?
A colleague and I are working on a churn model and reached an impasse:
Our data set is for a global product. We've been asked to look at the US market only.
When we subset the data to the US only, the ...
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What to do when one feature has very large importance/weight?
I am new to Data Science and currently am trying to predict customers churn for a company that offers of subscription-based bookings management software. Its customers are gyms.
I have a small ...
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Logistic Regression for prediction
I would like to ask about the theoretical approach of using Logistic Regression for customer data and more specifically Churn Prediction (in BigQuery and Python).
I have my customer data for an online ...
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Data for churning model
I am thinking to improve the imbalanced dataset for my churning model, as most people recommend like over/under sampling. I am wondering if using past customer churn data would be helpful. Say that I ...
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What sort of analysis should be done in order to define our target outcome for modelling customer lapse?
I am trying to build a model to predict customer lapse and am required to define the target lapse definition myself.
What sort of customer behavioural analysis should I do in order to define my ...
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Churn prediction model doesn't predict good on real data
I am working currently on churn prediction problem.
As an input I use data from date warehouse for a period 082016 - 032021(one row per month for each customer).
Based on this data I have created a ...
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What is the best way to model survival when the hazard rate decreases over time?
The standard survival analysis model - for example the model which forms the basis for the proportional hazards model - assumes the hazard rate is constant. In many applications this would be the ...
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How do you effectively predict the top 20% most likely customers to churn from a dataset?
I am looking to work out that if I have a dataset with 100,000 existing customers who didn't churn and 20,000 previous customers that churned in the past and the business objective is to target the 20%...
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Expected Lifetime: Churn Formula vs. Experience Data
I am analyzing data for a subscription based company. I.e they sell a service in exchange for monthly payment. I would like to conduct an analysis and come up with an estimate of the average lifetime (...
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How to predict churn events that may happen within a period of time?
I am trying to build a model that predicts churn events in the future. The business wants to be able to identify which customers are likely to terminate the services within a month. "Within a ...
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Should I perform customer segmentation before performing churn prediction?
Imagine a company with multiple lines of revenues coming from diferent products, but all customer can access these different products through the same account and the same online platform.
My goal is ...
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How to use multiple cross-section observations per subject for churn prediction?
Recently I have started to teach myself about machine learning and I have ran into a dataset, which got me a bit confused.
Dataset: The subjects of the dataset are university students (student ID == &...
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284
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Student Churn prediction
I am working on an ML model for student churn prediction. It is a classification problem if some student will churn or not. I have a lot of data like the student data and the activities of the student....
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Parameters to build a churn prediction engine
Let us assume that I’m working for some company A, and my manager has asked me to build a churn prediction engine given that the product to which the churn prediction is to be done is unknown. The ...
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Can I apply survival analysis to predict if a user will revisit the website?
I have one business problem in hand which is to predict if a user will revisit the website or not within 6 months. I need to majorly understand what are the factors which make the user return and also ...
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Predicting churn - deal with missing dates in time series and improve modelling result
This is the follow up question for General approach on time series for customer retention/churn in retail.
I have a time series of data in the following form:
...
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General approach on time series for customer retention/churn in retail
I have a time series of data in the following form:
...
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127
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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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Churn Prediction Training Set
I don't understand how to form my dataset from activity(logins etc.) and characteristic(location, age etc.) raw user data.
Ultimately, each row of the training set will have N activity features for a ...
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380
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How to predict whether the client will renew the subscription or not based on given data structure
I have a requirement where I want to predict whether the client will renew the subscription or not.
And the data is something like below.
Basically client's subscription end date can be anything. And ...
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217
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Churn Prediction Model based on Customer Activities
I am new to data science, so forgive me if i have not done my research well.
I want to build a system that calculates the churn scores for each customer and hence try to prevent it. I just want to ...
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753
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Cohort analysis over 3 months
I am trying to look at the customer retention & churn by using cohorts for an e-commerce usecase. From a business perspective, a client is defined as churned if it hasn't performed any ...
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How to most effectively utilize historical data to train churn model
Suppose we have some historical data of users activity on a website and we want to build a churn prediction model (let's say we want to predict churn in a 2 month window). The usual approach, as I ...
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How to predict whether or not a customer will renew
I have a dataset of customer contracts that specify a start date and if applicable an end date. Each month a customer is up for renewal. Below is an example of how the data is organized in excel:
<...
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How to represent a user who hasn't churned in training data
I am building a file with sample data that has a bunch of variables:
date, customer_id, ...
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107
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Behavioural data required to predict churn
I am trying to build a predictive churn model that will identify customers who are likely to churn. I am defining a churned user as someone who hasn't transacted within 60 days. 90% of all ...
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Decision tree to get difference in rates in two groups?
I have two sample groups of customers, each customer has 100s of features. For a single sample, i would use Decision Trees to find sub-groups that have a high churn rate. Thats easy.
However, my ...
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Database on time to churn in telecomunication
I would like to research about time to churn1 in the telecommunication market. Does anyone have a link to such a database? The only ones I found did not include the time of churn, but only if a ...
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380
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How to handle NaNs for ratio feature for binary classifier?
I'm creating a churn model and would like to create a ratio (# customers / total transaction) for each merchant. About 70% of the data are NaNs (zero/zero).
I was wondering what I should impute for ...
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How would you deal with inf. or NA for rate or ratio as a feature variable
I'm trying to create a feature for a churn model (binary classifier).
The feature is mean of sales growth rates for several months. But if I just take the mean of sales for several months, I often ...
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Churn Prediction problem [closed]
I am a data science neophyte struggling to solve a churn prediction problem, i would be grateful to have someone help with the regression model
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Causes for churn
I have a model which calculates churn probabilities for e-commerce customers based on their historical activity data (no. of sessions, page views, purchases etc) using supervised learning with an AUC ...
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Predicting contract churn/cancellation: Great model results does not work in the real world
I'm busy with a supervised machine learning problem where I am predicting contract cancellation. Although a lengthy question, I do hope someone will take the time as I'm convinced it will help others ...
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Your Prediction Model works. What to do after?
One of the most crucial skills of a data scientist is not only to be able to build an accurate predictive algorithm but to suggest a set of actions based on that to enhance the goal ratios.
I have ...
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Understanding churn prediction model [closed]
guys!
I have a dataset with a bunch of costumer-behavior features and the output being "Churned"/"Not churned". I applied a simple Random Forest Classifier and got a nice performance.
With this, I ...
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Opposite of churn?
I'm trying to build a model in R that predicts when a customer will purchase a product again.
However, I don't know what kind of model can handle time and predict this kind of outcome. I'm wondering ...
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364
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How to define user churn
I'd like to define our app user churn, Generally, people will define their app user churn by some simply way, like: if the user do not logon continually 7 days or 1 month, we will define them churn ...
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Dividing data between test, learn and predict
I was posting on stats.stackexchange but perhaps I should be posting here.
Context. Subscription business that charges users a monthly fee for access to the service. Management would like to predict "...
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Data driven approach to define a churn user
I'm trying to define a churn prediction model for an online service (betting/gambling). A lot of papers talk about churn analysis/prediction for telco companies where defining a churn user is ...
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