Post by account_disabled on Dec 13, 2023 4:42:25 GMT -5
True Positives (TP) : is the number of customers who were predicted to be Churn and who were actually Churn. And from being able to identify customers who will be Churn, we can find the right CRM to maintain satisfaction. or reduce the risk of customers in this group. True Negatives (TN) : is the number of customers who were predicted to not churn and who really did not churn, which are customers who still use the service normally. False Positives (FP) : is the number of customers who were predicted to churn but actually did not churn. This is an error of the ML Model and this part has a negative effect. It may cause unnecessary expenses in retaining this group of customers. False Negatives (FN) : is the number of customers who were predicted not to churn but actually churn. Nick personally views this as the most serious model mistake.
Because it makes us miss the opportunity to organize Whatsapp Number List a campaign. or promotions to retain this group of customersTrue Positives (TP) : is the number of customers who were predicted to be Churn and who were actually Churn. And from being able to identify customers who will be Churn, we can find the right CRM to maintain satisfaction. or reduce the risk of customers in this group. True Negatives (TN) : is the number of customers who were predicted to not churn and who really did not churn, which are customers who still use the service normally. False Positives (FP) : is the number of customers who were predicted to churn but actually did not churn. This is an error of the ML Model and this part has a negative effect. It may cause unnecessary expenses in retaining this group of customers. False Negatives (FN) : is the number of customers who were predicted not to churn but actually churn. Nick personally views this as the most serious model mistake. Because it makes us miss the opportunity to organize a campaign. or promotions to retain this group of customers.
True Positives (TP) : is the number of customers who were predicted to be Churn and who were actually Churn. And from being able to identify customers who will be Churn, we can find the right CRM to maintain satisfaction. or reduce the risk of customers in this group. True Negatives (TN) : is the number of customers who were predicted to not churn and who really did not churn, which are customers who still use the service normally. False Positives (FP) : is the number of customers who were predicted to churn but actually did not churn. This is an error of the ML Model and this part has a negative effect. It may cause unnecessary expenses in retaining this group of customers. False Negatives (FN) : is the number of customers who were predicted not to churn but actually churn. Nick personally views this as the most serious model mistake. Because it makes us miss the opportunity to organize a campaign. or promotions to retain this group of customers
Because it makes us miss the opportunity to organize Whatsapp Number List a campaign. or promotions to retain this group of customersTrue Positives (TP) : is the number of customers who were predicted to be Churn and who were actually Churn. And from being able to identify customers who will be Churn, we can find the right CRM to maintain satisfaction. or reduce the risk of customers in this group. True Negatives (TN) : is the number of customers who were predicted to not churn and who really did not churn, which are customers who still use the service normally. False Positives (FP) : is the number of customers who were predicted to churn but actually did not churn. This is an error of the ML Model and this part has a negative effect. It may cause unnecessary expenses in retaining this group of customers. False Negatives (FN) : is the number of customers who were predicted not to churn but actually churn. Nick personally views this as the most serious model mistake. Because it makes us miss the opportunity to organize a campaign. or promotions to retain this group of customers.
True Positives (TP) : is the number of customers who were predicted to be Churn and who were actually Churn. And from being able to identify customers who will be Churn, we can find the right CRM to maintain satisfaction. or reduce the risk of customers in this group. True Negatives (TN) : is the number of customers who were predicted to not churn and who really did not churn, which are customers who still use the service normally. False Positives (FP) : is the number of customers who were predicted to churn but actually did not churn. This is an error of the ML Model and this part has a negative effect. It may cause unnecessary expenses in retaining this group of customers. False Negatives (FN) : is the number of customers who were predicted not to churn but actually churn. Nick personally views this as the most serious model mistake. Because it makes us miss the opportunity to organize a campaign. or promotions to retain this group of customers
