by James
Fussy
When you
commit valuable advertising dollars towards a direct marketing campaign, it’s
crucial that you’re confident in the accuracy of the data that fuels it. In
today’s digital world, many direct marketing campaigns are charged by online
programmatic data, making it easy to forget about the industry pioneer: offline
marketing. Direct mail is still a customer acquisition and retention powerhouse,
heavily supported by the polished offline data that is available for use during
predictive modeling.
What
Is Offline Data?
Offline
data is sophisticated consumer data collected from an offline source, such as
proprietary customer data or publicly available information. These data types
are traditionally sorted into three categories: first-party (your data),
second-party (someone else’s data), and third-party (aggregated) data. Due to
the assortment of consumer data available, there are several ways to build a
strong performing mailing list relative to your campaign goals and budget.
4
Reasons Offline Data Delivers
1. It
is dependable and accurate.
Offline
data is anchored to an individual’s name and physical address, a simple yet
vital element in any direct mail campaign. The reliable demographic,
behavioral, and even psychographic data available from offline sources ranges
from life stage and homeowner status to hobbies, interests, and past purchases.
These valuable and quantifiable variables are used during audience segmenting
and predictive modeling to ensure your message is both relevant and appropriate
for each consumer on your list.
Online
data, on the other hand, is typically anchored to browsing history or search
intent, making it difficult to accurately identify an individual consumer’s key
data points. For example, if you have a visitor at your house and they connect
their device to your internet, their digital activity is now associated with
your online footprint. Additional consumer privacy protections, such as the
withdrawal of Facebook’s third-party data and the impending elimination of
cookie-based advertising, continue to tighten up online data gathering and
usage, making offline data even more attractive to marketers.
2.
It allows for precise targeting.
One of
direct mail’s unique attributes is that it is hand-delivered to the list of
recipients who are most likely to respond, a list that’s derived using
predictive modeling. Predictive modeling is a process that leverages key
customer insights and data science to identify the prospects who are most
likely to engage based on your campaign goals. First, the attributes of your
best customers are used to build an ideal customer profile (or purchase
algorithm), then those attributes are paired up with third-party data to build
a hyper-targeted mailing model.
The two
most popular modeling processes are “lookalike” and “two-stage.” The lookalike
model identifies which non-customers look most like your current customers.
Two-stage, or logistic regression modeling, is more predictive as it identifies
prospects that both look like your previous customers and have a positive response history with direct
mail.
3.
It is scalable.
Credible
direct mail agencies can match, append, and test data from an array of
third-party sources to create relevant and diversified mailing models to
continually expand the audience. Traditionally, when a mailing list is created,
all the consumers on the list are ranked from ‘most likely to buy’ to ‘least
likely to buy’ based on your purchase algorithm. As your campaign grows and new
data sources are introduced, you mail deeper into the model to optimize
campaign performance.
Through
complex analytics and campaign tracking, data scientists can evaluate and
transform buyer variables from the original database to create new attributes
to model against. Another option is a proprietary machine learning algorithm.
This scaling technique is even more powerful than AI or human scientists, as it
can constantly review, sort, transform, and update variables to forge new data
points.
4.
It can boost online campaign performance.
Combining
offline attributes with online intent not only provides deeper insights into
consumer preferences, but it is proven to increase response rates and improve
customer retention, while reducing the customer acquisition costs (CACs). The
comprehensive prospect models that were built with offline
data can be onboarded into online environments (i.e. social
media and data management platforms) to create a synchronized omnichannel
experience for your prospects. An integrated online and offline strategy can be
implemented throughout the customer journey — from priming the prospect
digitally prior to the mail drop, to sending a direct mail piece after a
digital interaction to recover an abandoned cart. This coordinated strategy
will ensure you reach the right prospects, at the right time, in the right channel,
without wasting ad spend.
When
considering the versatility and precision of offline data, it’s obvious why so
many brands rely on it to effectively and efficiently reach their audience both
offline and online. Regardless of your data source or strategy, it’s important
to continually test models throughout the campaign lifecycle against new,
high-performing lists to optimize your program for continued conversions and
scale.
James
Fussy is the Vice President of Data and Analytics at SeQuel Response,
an award-winning direct response agency based out of Minneapolis, MN. With more
than 20 years of experience, James has a diverse and accomplished background in
customer data management, marketing list strategy and modeling. You can connect
with James at sequeldm.com,
email him at jamesf@sequeldm.com,
or find him on LinkedIn.
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