MODELING CUSTOMER ACQUISITION PROPENSITIES FOR EDUCATIONAL TECHNOLOGY PRODUCTS
The disclosed embodiments provide a system for processing data. During operation, the system obtains a set of features associated with a customer, wherein the set of features includes profile data from an online professional network. Next, the system uses a statistical model and the features to predict a likelihood of acquiring the customer for an educational technology product. The system then uses the likelihood to generate output for use in targeting the customer with the educational technology product.
1 . A method, comprising:
obtaining a set of features associated with a customer, wherein the set of features comprises profile data from an online professional network;
using a statistical model and the features to predict, by one or more computer systems, a likelihood of acquiring the customer for an educational technology product; and
using the likelihood to generate, by the one or more computer systems, output for use in targeting the customer with the educational technology product.
2 . The method of claim 1 , further comprising:
obtaining a market segment for the customer; and
modifying the output based on the market segment.
3 . The method of claim 2 , wherein modifying the output based on the market segment comprises:
applying a threshold for the market segment to the likelihood prior to generating the output.
4 . The method of claim 2 , wherein modifying the output based on the market segment comprises:
tailoring the output to the market segment.
5 . The method of claim 1 , wherein the set of features comprises:
one or more network engagement features;
one or more marketing engagement features; and
one or more account features.
6 . The method of claim 5 , wherein the one or more network engagement features include at least one of:
a measure of engagement with content in the online professional network;
a measure of engagement with electronic communications from the online professional network;
a number of premium subscribers in a second-degree network of the customer;
a number of recommended connections;
a recency of a profile update; and
a page view metric.
7 . The method of claim 5 , wherein the one or more marketing engagement features include at least one of:
a click-through rate for marketing emails.
8 . The method of claim 5 , wherein the one or more account features include at least one of:
a potential spending; and
a metric representing a quality of the customer as a sales lead.
9 . The method of claim 1 , wherein the output comprises a marketing email for the educational technology product.
10 . The method of claim 1 , wherein acquiring the customer for the educational technology product comprises activating a free trial of the educational technology product.
11 . 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 a set of features associated with a customer, wherein the set of features comprises profile data from an online professional network;
use a statistical model and the features to predict a likelihood of acquiring the customer for an educational technology product; and
use the likelihood to generate output for use in targeting the customer with the educational technology product.
12 . The apparatus of claim 11 , wherein the memory further stores instructions that, when executed by the one or more processors, cause the apparatus to:
obtain a market segment for the customer; and
modify the output based on the market segment.
13 . The apparatus of claim 12 , wherein modifying the output based on the market segment comprises:
applying a threshold for the market segment to the likelihood prior to generating the output.
14 . The apparatus of claim 12 , wherein modifying the output based on the market segment comprises:
tailoring the output to the market segment.
15 . The apparatus of claim 11 , wherein the set of features comprises:
one or more network engagement features;
one or more marketing engagement features; and
one or more account features.
16 . The apparatus of claim 15 , wherein the one or more network engagement features include at least one of:
a measure of engagement with content in the online professional network;
a measure of engagement with electronic communications from the online professional network;
a number of premium subscribers in a second-degree network of the customer;
a number of recommended connections;
a recency of a profile update; and
a page view metric.
17 . The apparatus of claim 15 , wherein the one or more marketing engagement features include at least one of:
a click-through rate for marketing emails.
18 . The apparatus of claim 15 , wherein the one or more account features include at least one of:
a potential spending; and
a metric representing a quality of the customer as a sales lead.
19 . A system, comprising:
an analysis module comprising a non-transitory computer-readable medium storing instructions that, when executed, cause the system to:
obtain a set of features associated with a customer, wherein the set of features comprises profile data from an online professional network; and
use a statistical model and the features to predict a likelihood of acquiring the customer for an educational technology product; and
a management module comprising a non-transitory computer-readable medium storing instructions that, when executed, cause the system to use the likelihood to generate output for use in targeting the customer with the educational technology product.
20 . The system of claim 19 , wherein the non-transitory computer-readable medium of the analysis apparatus further stores instructions that, when executed, cause the system to:
obtain a market segment for the customer; and
modify the output based on the market segment.