Smart copy optimization in customer acquisition and customer management platforms
In some examples, special-purpose machines are provided that facilitate smart copy optimization in a network service or publication system, including software-configured computerized variants of such special-purpose machines and improvements to such variants, and to the technologies by which such special-purpose machines become improved compared to other special-purpose machines that facilitate adding the new features. Such technologies can include special artificial-intelligence (AI), machine-learning (ML), and natural-language-processing (NLP) techniques.
1 . A copy optimization tool comprising:
one or more computer processors, configured to execute instructions programmed using a set of machine code, wherein the instructions, when executed by the one or more computer processors, cause the one or more computer processors to perform operations comprising:
extracting a text input from a candidate copy content;
using a trained model, accessing comparable historical copy content to generate historical comparison content;
profiling a historical performance of the historical comparison content based on one or more of following metrics:
a frequency of use of a historical subject line and comparable copy content; and
a recency of use of a historical subject line and comparable copy content;
matching the text input from the candidate copy content with the comparable copy content based on one or more matching algorithms;
identifying historical matches of comparable copy content matching the text input of the candidate copy content;
ranking the historical matches based on one or more of following rules:
a quality of a match;
a volume of historical subject lines and comparable copy content;
a word count; and
a recency or frequency of a historical subject line or comparable copy content;
applying a weighting factor to the historical matches based on past seasonal performance patterns of the comparable copy content associated with the ranked historical matches;
determining a confidence range of the performance of the candidate copy content based on a weight-adjusted standard deviation interval of the ranked historical matches; and
transmitting, based on the determined confidence range of the performance of the candidate copy content, the candidate copy content over a network for deployment in a marketing campaign.
2 . The copy optimization tool of claim 1 , wherein the operations further comprise:
identifying a plurality of drivers in the extracted text using the trained model based on the profiled historical performance.
3 . The copy optimization tool of claim 2 , wherein the plurality of drivers include positive and negative drivers.
4 . The copy optimization tool of claim 3 , wherein the operations further comprise changing at least one of the negative drivers to a positive driver and transmitting a modified candidate copy content having the changed driver.
5 . A method for copy optimization, comprising:
extracting a text input from a candidate copy content;
using a trained model, accessing comparable historical copy content to generate historical comparison content;
profiling a historical performance of the historical comparison content based on one or more of following metrics:
a frequency of use of a historical subject line and comparable copy content; and
a recency of use of a historical subject line and comparable copy content;
matching the text input from the candidate copy content with the comparable copy content based on one or more matching algorithms;
identifying historical matches of comparable copy content matching the text input of the candidate copy content;
ranking the historical matches based on one or more of following rules:
a quality of a match;
a volume of historical subject lines and comparable copy content;
a word count; and
a recency or frequency of a historical subject line or comparable copy content;
applying a weighting factor to the historical matches based on past seasonal performance patterns of the comparable copy content associated with the ranked historical matches;
determining a confidence range of the performance of the candidate copy content based on a weight-adjusted standard deviation interval of the ranked historical matches; and
transmitting, based on the determined confidence range of the performance of the candidate copy content, the candidate copy content over a network for deployment in a marketing campaign.
6 . The method of claim 5 , further comprising:
identifying, with the processor, a plurality of drivers in the extracted text using the trained model based on the profiled historical performance.
7 . The method of claim 6 , wherein the plurality of drivers include positive and negative drivers.
8 . The method of claim 7 , further comprising changing at least one of the negative drivers to a positive driver and transmitting a modified candidate copy content having the changed driver.
9 . A non-transitory computer-readable medium storing instructions that, when executed by one or more computer processors of a computer, cause the computer to perform operations comprising:
extracting a text input from a candidate copy content;
using a trained model, accessing comparable historical copy content to generate historical comparison content;
profiling a historical performance of the historical comparison content based on one or more of following metrics:
a frequency of use of a historical subject line and comparable copy content; and
a recency of use of a historical subject line and comparable copy content;
matching the text input from the candidate copy content with the comparable copy content based on one or more matching algorithms;
identifying historical matches of comparable copy content matching the text input of the candidate copy content;
ranking the historical matches based on one or more of following rules:
a quality of a match;
a volume of historical subject lines and comparable copy content;
a word count; and
a recency or frequency of a historical subject line or comparable copy content;
applying a weighting factor to the historical matches based on past seasonal performance patterns of the comparable copy content associated with the ranked historical matches;
determining a confidence range of the performance of the candidate copy content based on a weight-adjusted standard deviation interval of the ranked historical matches; and
transmitting, based on the determined confidence range of the performance of the candidate copy content, the candidate copy content over a network for deployment in a marketing campaign.
10 . The computer-readable medium of claim 9 , wherein the operations further comprise:
identifying a plurality of drivers in the extracted text using the trained model based on the profiled historical performance.
11 . The computer-readable medium of claim 10 , wherein the plurality of drivers include positive and negative drivers.
12 . The computer-readable medium of claim 11 , wherein the operations further comprise changing at least one of the negative drivers to a positive driver and transmitting a modified candidate copy content having the changed driver.