How to most effectively utilize historical data to train churn model
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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 understand it, is to take a slice of that historical data at time t and see which users churn in the time interval (t, t + 2 month), so we take some features at time t and train our model.
However, that way we only use a small part of our data, feeding our model only users who were active at time t. But what if we want to use all historical data?
One way that comes to mind is take a lot of slices of our data at times t_1, t_2, t_3 ... and just merge them in one dataset, however different slices could have the a lot of same users even if we take these slices very far apart from each other. So our model could potentially learn that if a particular set of features occurs many times in our data set, then the user with these features is less likely to churn, (e.g. if we take two time slices at t_1 and t_2>1 then, if a user is presented in both of these slices, he can't churn at least at time t1). So it doesn't seem to be the right way to do it...
How can I extract as much information as possible from a historical data over large period of time without spoiling the model?
churn
asked 3 mins ago
Vitaly ManoshinVitaly Manoshin
1
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$begingroup$
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 understand it, is to take a slice of that historical data at time t and see which users churn in the time interval (t, t + 2 month), so we take some features at time t and train our model.
However, that way we only use a small part of our data, feeding our model only users who were active at time t. But what if we want to use all historical data?
One way that comes to mind is take a lot of slices of our data at times t_1, t_2, t_3 ... and just merge them in one dataset, however different slices could have the a lot of same users even if we take these slices very far apart from each other. So our model could potentially learn that if a particular set of features occurs many times in our data set, then the user with these features is less likely to churn, (e.g. if we take two time slices at t_1 and t_2>1 then, if a user is presented in both of these slices, he can't churn at least at time t1). So it doesn't seem to be the right way to do it...
How can I extract as much information as possible from a historical data over large period of time without spoiling the model?
churn
asked 3 mins ago
Vitaly ManoshinVitaly Manoshin
1
New contributor
Vitaly Manoshin is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
add a comment |
$begingroup$
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 understand it, is to take a slice of that historical data at time t and see which users churn in the time interval (t, t + 2 month), so we take some features at time t and train our model.
However, that way we only use a small part of our data, feeding our model only users who were active at time t. But what if we want to use all historical data?
One way that comes to mind is take a lot of slices of our data at times t_1, t_2, t_3 ... and just merge them in one dataset, however different slices could have the a lot of same users even if we take these slices very far apart from each other. So our model could potentially learn that if a particular set of features occurs many times in our data set, then the user with these features is less likely to churn, (e.g. if we take two time slices at t_1 and t_2>1 then, if a user is presented in both of these slices, he can't churn at least at time t1). So it doesn't seem to be the right way to do it...
How can I extract as much information as possible from a historical data over large period of time without spoiling the model?
churn
asked 3 mins ago
Vitaly ManoshinVitaly Manoshin
1
New contributor
Vitaly Manoshin is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
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 understand it, is to take a slice of that historical data at time t and see which users churn in the time interval (t, t + 2 month), so we take some features at time t and train our model.
However, that way we only use a small part of our data, feeding our model only users who were active at time t. But what if we want to use all historical data?
One way that comes to mind is take a lot of slices of our data at times t_1, t_2, t_3 ... and just merge them in one dataset, however different slices could have the a lot of same users even if we take these slices very far apart from each other. So our model could potentially learn that if a particular set of features occurs many times in our data set, then the user with these features is less likely to churn, (e.g. if we take two time slices at t_1 and t_2>1 then, if a user is presented in both of these slices, he can't churn at least at time t1). So it doesn't seem to be the right way to do it...
How can I extract as much information as possible from a historical data over large period of time without spoiling the model?
churn
churn
asked 3 mins ago
Vitaly ManoshinVitaly Manoshin
1
New contributor
Vitaly Manoshin is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 3 mins ago
Vitaly ManoshinVitaly Manoshin
1
New contributor
Vitaly Manoshin is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 3 mins ago
Vitaly ManoshinVitaly Manoshin
1
New contributor
Vitaly Manoshin is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 3 mins ago
Vitaly ManoshinVitaly Manoshin
1
asked 3 mins ago
Vitaly ManoshinVitaly Manoshin
1
1
New contributor
Vitaly Manoshin is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
New contributor
Vitaly Manoshin is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
Vitaly Manoshin is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
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