DIGILANT INSIGHT
June 2016
A Fresh Approach To
LOOK-ALIKE
MODELING
‘A Fresh Approach To Look-alike Modeling’
Overview
Marketers are continually working towards new advertising strategies
that will reach larger audiences, but their marketing spend still needs
to be focused on reaching the right audiences. Look-alike modeling
continues to be a well-proven tool for extending reach, because it
finds new targets that look like an advertiser’s best customers, getting
returns on investment that are double or triple over using standard
targeting methods. So why are marketers not spending more money
on look-alike modeling if it’s so efficient at converting?
There are many technologies today that can help you onboard your first party
data and create an audience segmentation and targeting plan. There are also
multiple targeting approaches available:
1. Run of network (RON), which is not refined but has the largest reach.
2. Look-alike models, which produce good results but ultimately narrow down
your media buy and are predefined models that lack distinction and audience
extension capabilities.
3. Retargeting, which is both efficient and effective, but limited in scale to the
users that have shown interest in your offering.
In short, today’s mainstream audience targeting models force marketers to
choose between scale, accuracy, and customization. The ideal situation would
be to reap all the benefits of all the targeting approaches through a fresh
approach to look-alike modeling.
Digilant, an ispDigital Company
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‘A Fresh Approach To Look-alike Modeling’
Custom
Look-alike Models
The starting point: the seed
The starting point (sometimes referred to as a “seed”) for a look-alike model is
usually the advertiser’s best customers. The advertiser will choose one or two
attributes or behaviors, such as age, gender, income, or even something more
specific like runner or football fan, to create the seed for their model. The data
used for the selection of these attributes could be recent or historical, but either
way the data is static, often imprecise, and typically outdated. If an advertiser
runs a two-week promotion the results might show that the converters were a
completely different type of consumer than the original model. This means the
look-alike targeting was not as efficient as the advertiser intended. Luckily, there
are new ways to create better real-time models using all of the types of data
available to marketers (including first party data) and applying data science.
Beyond standard look-alikes
Advertisers should be taking advantage of what they can learn about their
converters or best customers. To go beyond standard look-alikes such as
‘soccer mom’ or ‘fashionista’, advertisers could collect hundreds of attributes
from their CRM or POS data, combine it with conversion data and applied data
science. Armed with this data, they can create “real-time” consumer models to
find new customers that really look like their best customers, even as those
best customers evolve. Now, instead of using the look-alikes that may or may
not look like their “real customer” anymore, marketers can create a model that
will extend their target audience, achieve results that are similar or better to
retargeting, and learn more about their best customers.
Digilant, an ispDigital Company
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‘A Fresh Approach To Look-alike Modeling’
Digilant’s
Custom Approach
Since launching our Consumer Persona product last year, Digilant has been
able to successfully extend audiences using first party data to create real-time,
custom look-alike models. Rather than creating one or two static models based
on standard attributes, Digilant uses real-time data based on hundreds of data
points and combines it with the advertiser’s own data to create new custom
segments that represent a more accurate consumer target, all done in real-time
and refreshing throughout the campaign.
The results have been exciting! By using Consumer Persona, Digilant’s advertisers
were able to create custom look-alikes —extending their reach by four or five
times and tripling their conversions. Instead of using static models to find the
same consumer types, or re-targeting the same users, our advertisers were able
to use custom, real-time look-alike modeling to its full potential, extending their
audiences and getting better results.
CON V E RT E D
CON V E RT E D
CONV ERTED
CO N VE RT E D
CONVERTED
CON V E RT E D
CO N VE RT E D
CONVERTED
CON V E RT E D
CO N VE RT E D
CON V E RT E D
Digilant, an ispDigital Company
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‘A Fresh Approach To Look-alike Modeling’
Case Study
P ROBLEM
A financial services client was looking to
expand its audience using exclusive first
party data.
SOLU TI ON
Using Digilant’s Consumer Persona, the
advertiser segmented loan application
consumers as “Accepted” or “Rejected.”
Based on this segmentation, Digilant
developed loan application audiences
that the advertiser should target. First
they eliminated all the users who were not
accepted for a loan, so that they wouldn’t
be wasting impressions on people who
couldn’t use their service. Then, from the
accepted loan users, Digilant revealed
a segment that they named ‘hedonist
credit-seeker’ (pleasure seekers who
like to travel, buy expensive gadgets
and have multiple credit cards), which
had a 62% predicted lift. In other words,
if the advertiser targeted these users
they would have a 62% higher likelihood
to convert than a Run of Network (RON)
campaign. Knowing all the attributes of
these users, Digilant then created a lookalike model to extend the advertiser’s
audience and find even more ‘hedonist
credit-seekers’.
Digilant, an ispDigital Company
R ES U LTS
Rather than retargeting users that
they already knew, the client was
able to extend the campaign even
further to get even better results.
• They extended their reach by 3.6x
• The campaign had a 182% lift
• They discovered new audiences to
target and adjusted their creatives
and targeting strategies to model
those real-time audiences.
Single family
with kids
Reach:
100.000
IT small
business males
25-35 car lovers
Hedonist credit seekers
Backpackers
25-35 car lovers
Reach:
360.000
IT small
business males
Single family
with kids
Freelancers
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‘A Fresh Approach To Look-alike Modeling’
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ABOUT DIGILANT
Digilant, a global programmatic media pioneer, partners with the world’s leading
agencies and brands to provide customized and scalable programmatic media
solutions. Powered by insightful and actionable data science, Digilant’s display,
video, mobile, and social solutions are delivered through a world-class service
offering. The company’s advanced technology platform, which includes a data
management platform (DMP), connects brands with relevant and unique audiences
by activating first party data, third party and its own proprietary data. Headquartered
in Boston, Massachusetts, Digilant has offices in Barcelona, Bogota, Lima, London,
Madrid, Mexico City, Milan, Monterrey, Santiago de Chile, and across the United States.
www.digilant.com • An ispDigital Company