MODEL-BASED ASSESSMENT AND IMPROVEMENT OF RELATIONSHIPS
The disclosed embodiments provide a system for processing data. During operation, the system obtains an engagement metric correlated with successful usage of a product by a set of customers. Next, the system identifies a threshold for the engagement metric that represents a change in customer growth for the product. The system then uses the threshold and a value of the engagement metric for a customer to characterize a revenue quality of a customer with the product. Finally, the system outputs the revenue quality and the value of the engagement metric for use in managing interaction with the customer.
1 . A method, comprising:
obtaining an engagement metric correlated with successful usage of a product by a set of customers;
identifying, by a computer system, a threshold for the engagement metric that represents a change in customer growth for the product;
using the threshold and a value of the engagement metric for a customer to characterize a revenue quality of a customer with the product; and
outputting the revenue quality and the value of the engagement metric for use in managing interaction with the customer.
2 . The method of claim 1 , further comprising:
outputting a recommended action for managing sales activity with the customer based on the revenue quality and the value of the engagement metric.
3 . The method of claim 1 , further comprising:
identifying a correlation between the engagement metric and the successful usage of the product prior to characterizing the revenue quality of the customer with the product.
4 . The method of claim 3 , wherein identifying the correlation between the engagement metric and the successful usage of the product comprises:
using a statistical model to determine correlations between a set of engagement metrics for the product and a growth metric for the product; and
selecting, from the correlations, the engagement metric that has a highest correlation with the growth metric.
5 . The method of claim 4 , wherein the statistical model comprises a regression model.
6 . The method of claim 4 , wherein the growth metric comprises an existing account growth.
7 . The method of claim 1 , further comprising:
obtaining the set of customers from a market segment for the product.
8 . The method of claim 7 , wherein the market segment comprises at least one of:
a company size;
a location;
an industry;
a company type;
a product type;
an account tier;
an acquisition channel; and
a historic spending.
9 . The method of claim 1 , wherein using the threshold to characterize the revenue quality of the customer comprises at least one of:
characterizing the revenue quality based on a comparison of the value of the engagement metric with the threshold.
10 . The method of claim 1 , wherein the engagement metric comprises a cost per action associated with successful usage of the product.
11 . The method of claim 10 , wherein the action is at least one of:
acceptance of a message;
a job application;
a page view; and
a thousand page views.
12 . The method of claim 1 , wherein the revenue quality comprises at least one of:
exceeding competition;
potential growth;
lower revenue quality; and
potential churn.
13 . The method of claim 1 , wherein the threshold represents a boundary between customer growth and customer churn in the product.
14 . An apparatus, comprising:
one or more processors; and
memory storing instructions that, when executed by the one or more processors, cause the apparatus to:
obtain an engagement metric correlated with successful usage of a product by a set of customers;
identify a threshold for the engagement metric that represents a change in customer growth for the product;
use the threshold and a value of the engagement metric for a customer to characterize a revenue quality of a customer with the product; and
output the revenue quality and the value of the engagement metric for use in managing interaction with the customer.
15 . The apparatus of claim 14 , wherein the memory further stores instructions that, when executed by the one or more processors, cause the apparatus to:
identify a correlation between the engagement metric and the successful usage of the product prior to characterizing the revenue quality of the customer with the product.
16 . The apparatus of claim 15 , wherein identifying the correlation between the engagement metric and the successful usage of the product comprises:
using a statistical model to determine correlations between a set of engagement metrics for the product and a growth metric for the product; and
selecting, from the correlations, the engagement metric that has a highest correlation with the growth metric.
17 . The apparatus of claim 14 , wherein the memory further stores instructions that, when executed by the one or more processors, cause the apparatus to:
obtain the set of customers from a market segment for the product.
18 . The apparatus of claim 17 , wherein the market segment comprises at least one of:
a company size;
a location;
an industry;
a company type;
a product type;
an account tier;
an acquisition channel; and
a historic spending.
19 . The apparatus of claim 14 , wherein the engagement metric comprises a cost per action associated with successful usage of the product.
20 . A non-transitory computer-readable storage medium storing instructions that when executed by a computer cause the computer to perform a method, the method comprising:
obtaining an engagement metric correlated with successful usage of a product by a set of customers;
identifying, by a computer system, a threshold for the engagement metric that represents a change in customer growth for the product;
using the threshold and a value of the engagement metric for a customer to characterize a revenue quality of a customer with the product; and
outputting the revenue quality and the value of the engagement metric for use in managing interaction with the customer.