PREDICTING CHURN RISK ACROSS CUSTOMER SEGMENTS

1 . A method, comprising:

inputting a set of features for a customer of a product into a first statistical model, wherein the set of features comprises a company segment of the customer;

using the first statistical model to predict, by one or more computer systems, a churn risk of the customer; and

when the churn risk exceeds a first threshold for the company segment, outputting a notification of a high churn risk level for the customer on the one or more computer systems.

2 . The method of claim 1 , further comprising:

using a second statistical model to obtain one or more risk factors associated with the churn risk; and

outputting an additional notification of the one or more risk factors one the one or more computer systems.

3 . The method of claim 2 , wherein using the second statistical model to identify the risk factor associated with the churn risk comprises:

comparing a feature in the set of features with a second threshold for a risk factor associated with the churn risk; and

when the feature does not meet the second threshold, including the risk factor in the one or more risk factors.

4 . The method of claim 1 , further comprising:

using the first statistical model to determine the first threshold for the company segment.

5 . The method of claim 1 , further comprising:

when the churn risk exceeds the first threshold, transmitting a communication comprising content for reducing the churn risk to the customer.

6 . The method of claim 1 , further comprising:

selecting the first statistical model based on the company segment and a stage of a renewal sales cycle for the customer.

7 . The method of claim 1 , wherein the set of features further comprises:

an account feature;

a usage feature; and

a spending feature.

8 . The method of claim 7 , wherein the account feature is at least one of:

a potential spending amount;

a number of recruiters;

a number of talent professionals; and

a number of new hires.

9 . The method of claim 7 , wherein the usage feature is at least one of:

a number of profile views;

a number of job listings;

a number of hires through the job listings;

a number of new hires;

a number of visits to an online professional network;

a number of searches;

a number of messages; and

an engagement score.

10 . The method of claim 7 , wherein the spending feature is at least one of:

a renewal target amount;

a number of purchased recruiting spots;

a number of purchased job posting slots;

a discount rate;

a spending amount; and

a spending growth.

11 . The method of claim 1 , wherein the company segment comprises at least one of:

a company size;

a location; and

a company type.

12 . The method of claim 1 , wherein the product is associated with use of an online professional network.

13 . An apparatus, comprising:

one or more processors; and

memory storing instructions that, when executed by the one or more processors, cause the apparatus to:

input a set of features for a customer of a product into a first statistical model, wherein the set of features comprises a company segment of the customer;

use the first statistical model to predict a churn risk of the customer; and

when the churn risk exceeds a first threshold for the company segment, output a notification of a high churn risk level for the customer.

14 . The apparatus of claim 13 , wherein the memory further stores instructions that, when executed by the one or more processors, cause the apparatus to:

use a second statistical model to obtain one or more risk factors associated with the churn risk; and

output an additional notification of the one or more risk factors.

15 . The apparatus of claim 14 , wherein using the second statistical model to identify the risk factor associated with the churn risk comprises:

comparing a feature in the set of features with a second threshold for a risk factor associated with the churn risk; and

when the feature does not meet the second threshold, including the risk factor in the one or more risk factors.

16 . The apparatus of claim 13 , wherein the memory further stores instructions that, when executed by the one or more processors, cause the apparatus to:

use the first statistical model to determine the first threshold for the company segment.

17 . The apparatus of claim 13 , wherein the memory further stores instructions that, when executed by the one or more processors, cause the apparatus to:

select the first statistical model based on the company segment and a stage of a renewal sales cycle for the customer.

18 . The apparatus of claim 13 , wherein the set of features further comprises:

an account feature;

a usage feature; and

a spending feature.

19 . A system, comprising:

an analysis non-transitory computer-readable medium comprising instructions that, when executed by one or more processors, cause the system to:

input a set of features for a customer of a product into a first statistical model, wherein the set of features comprises a company segment of the customer; and

use the first statistical model to predict a churn risk of the customer; and

a management non-transitory computer-readable medium comprising instructions that, when executed by one or more processors, cause the system to output a notification of a high churn risk level for the customer when the churn risk exceeds a first threshold for the company segment.

20 . The system of claim 19 , wherein the analysis non-transitory computer-readable medium further instructions that, when executed by the one or more processors, cause the system to:

use a second statistical model to obtain one or more risk factors associated with the churn risk; and

output an additional notification of the one or more risk factors.