mktinsight: 'mktinsight': A package to contextually analyze email subject...
In bziomek/mktinsight: Tool for Contextual Analysis of Email Subjects
mktinsight R Documentation
mktinsight: A package to contextually analyze email subject lines
Description
The mktinsight package provides two functions that are useful for email marketers:
titlesort and mail.subject. Together they can automate the difficult and very
manual tasks of segmenting email recipients and determining what subject lines worked best
to get readership, and why.
mail.subject
mail.subject is the core function in mktinsight. It allows marketers to analyze
the success of their email subject lines without having to resort to old-fashioned sets of rules
or leverage massive marketing toolkits. mail.subject contains three key analytical steps:
Identifying email topics (1),
identifying the topics that align with successful emails (2, as measured by open rates to senior contacts),
then finding the words that contextualize those topics most successfully (3).
To extract topics, the function generates dependency trees from email subjects and parses them
to find the most important topic. The function then calculates the odds ratio that an email with
a certain topic is opened and read versus that with a different topic, and finds words associated
with that topic to guide future email creation.
Subject Insights also has the option of determining the level of a contact using titlesort,
then presenting the top predictors of an email being disproportionately opened by senior rather
than junior contacts, to help marketers target senior customers. All this is done in a single command
aid in accelerating marketing workflows.
titlesort
titlesort is a binary classifier that takes a job title and outputs the probability that
the job title is management-level. Job titles can vary widely, and both one- and two-word as well
as very long titles are common (“Director” or “Technical Director” compared to “Vice President of
Regional Software and Patent Licensing Operations, United Kingdom, Ireland, and Nordics”). Because of this,
parsing job titles as sentences didn’t work well. Instead, titles are tokenized, stop words and punctuation
are removed, and a bag of words analysis is performed to predict title seniority.
bziomek/mktinsight documentation built on Jan. 5, 2023, 12:18 a.m.
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| mktinsight | R Documentation |
mktinsight: A package to contextually analyze email subject lines
Description
The mktinsight package provides two functions that are useful for email marketers:
titlesort and mail.subject. Together they can automate the difficult and very
manual tasks of segmenting email recipients and determining what subject lines worked best
to get readership, and why.
mail.subject
mail.subject is the core function in mktinsight. It allows marketers to analyze
the success of their email subject lines without having to resort to old-fashioned sets of rules
or leverage massive marketing toolkits. mail.subject contains three key analytical steps:
Identifying email topics (1),
identifying the topics that align with successful emails (2, as measured by open rates to senior contacts),
then finding the words that contextualize those topics most successfully (3).
To extract topics, the function generates dependency trees from email subjects and parses them to find the most important topic. The function then calculates the odds ratio that an email with a certain topic is opened and read versus that with a different topic, and finds words associated with that topic to guide future email creation.
Subject Insights also has the option of determining the level of a contact using titlesort,
then presenting the top predictors of an email being disproportionately opened by senior rather
than junior contacts, to help marketers target senior customers. All this is done in a single command
aid in accelerating marketing workflows.
titlesort
titlesort is a binary classifier that takes a job title and outputs the probability that
the job title is management-level. Job titles can vary widely, and both one- and two-word as well
as very long titles are common (“Director” or “Technical Director” compared to “Vice President of
Regional Software and Patent Licensing Operations, United Kingdom, Ireland, and Nordics”). Because of this,
parsing job titles as sentences didn’t work well. Instead, titles are tokenized, stop words and punctuation
are removed, and a bag of words analysis is performed to predict title seniority.
R Package Documentation
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We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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