Iab local targeting_guide_0922_final

Targeting Local Markets:
An IAB Interactive Advertising Guide

Released September 2010

“Targeting Local Markets” has been developed by the
IAB Local Committee.

About the IAB’s Local Committee:
The mission of the IAB Local Committee is to communicate the value of online local interactive
advertising to national and local marketers and to provide tools for publishers to effectively monetize
their local ad inventory. A full list of Committee member companies can be found at:

http://www.iab.net/local_committee

This document can be found on the IAB website at: www.iab.net/targeting_local

IAB Contact Information:
Gina Kim
Director of Industry Initiatives, IAB
212-380-4728
Gina.Kim@iab.net

Ta r g e t i ng L o ca l M a r ke t s : An I AB I n te r ac ti v e Ad v e rti s i n g Gu ide 1

Table of Contents

Introduction .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

IP-based geo-targeting .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

Search Targeting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
.

Explicit Profile Data Targeting .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6

Behavioral Targeting (Implicit Profile Data Targeting) .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8

Mobile/Location-based Targeting .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

Contextual Targeting .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

Example A: National Advertiser . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

Example B: Local Advertiser .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

Summary .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17


Introduction
Local online advertising is still a very small piece of the overall market and few tools exist to provide
local advertisers the ability to effectively purchase local impressions. Companies are increasingly
focused on the local online market. Many experts agree that local online advertising is poised for
impressive growth in the next few years as local businesses get access to better technology. Borrell
Associates forecasts the local online advertising to grow nearly 18% in 2011, from $13.7 billion to
$16.1 billion.

Online Ad Spending Forcast

$60

$50

$40
$ in billions

National
National $35.7
$30
$31.8

$20

$10 Local Local
$13.7 $16.1
$0
2010 2011

Source: Borrell Associates

“Targeting Local Markets” is an overall look at some techniques used by the online publishing
ecosystem to help drive local online advertising. Some of the techniques provide better accuracy
than others. Search is often used in local because of the intended action and ease in buying. Localized
content has also become increasingly more popular as local blogs and newspapers report on
community and neighborhood activities.

IP-based geo-targeting is commonly used for local advertisers to reach their audience; however, with
only 25-50 miles of accuracy it is really only effective for regional advertisers to reach users at a
DMA level. Similarly, Behavioral Targeting is frequently used to estimate a user’s location, but inferred
location is still far from accurate for most local advertisers.

While techniques continue to evolve one thing is certain: a huge opportunity exists for local
advertising online. With a large percentage of consumer spend happening within a few miles of the
home, there is incredible incentive for the online ecosystem to continue to search for better tools to
drive local advertising.

“Targeting Local Markets” provides example campaigns for 2 types of advertisers (one national and
one local) that utilize various techniques to reach local audiences. These examples are illustrations
of targeting technologies that marketers and advertisers can use today in their own advertising
campaigns and should not be taken as an endorsement of any one technique.

The IAB Local Committee will continue to watch the marketplace and the evolution and development
of new technology and techniques to reach local users online.


IP-based geo-targeting
Definition
IP-based geo-targeted advertising is delivered to a user’s geographic location as determined by his
or her Internet Protocol (IP) address.

Scale
Every computer connected to the internet is assigned an IP address by its ISP, so the pool of IP
targeted impressions is large. However, unlike ZIP codes or area codes, the numbering system of an IP
address has no correlation with geography. Therefore a computer’s location cannot be determined by
IP address alone.

IP intelligence firms such as Digital Element and Quova supply technology that matches IP addresses
to a physical location, providing coverage of virtually the entire internet universe with geographic
data.

However, while IP-based geo-targeting is accurate in indentifying the geography of the IP address,
there are limitations to the quality of data relative to advertiser intent.

Pricing
IP-based geo targeting is widely available with many competitive pricing options.

Accuracy vs. Precision
Given that an advertiser’s intention is to reach people, not their IP addresses, the discrepancy
between the location of an IP address and a user’s true location diminishes the precision of IP-based
geo-targeting.

Several underlying factors can contribute to inconsistency:

• Outdated information – a small percent of IP addresses worldwide change location each month.
As IP addresses are dynamically assigned (unlike telephone numbers that are geographically
associated) reissued IP addresses can be located anywhere.

• Centrally located networks – many organizations with locations in multiple cities have their
computer networks mapped to a centrally located IP address. This explains why, for example,
someone working in the Los Angeles office of an Ohio based company may see advertising
targeted to Cleveland.

• Proxy Servers – some ISPs use a single, or a small number of servers for all users thereby having all
their users assigned to few IP addresses. In an oft-cited example, several years ago most AOL users
appeared to come from Reston, Virginia regardless of where they lived.

• The more granular the targeted geography, the less precise IP-based geo targeting becomes.
When evaluating IP-based geo-targeting, advertisers should consider their campaign’s geographic
objectives and requirements, and tolerance for inexactness.

• Generally speaking, IP geo-targeting to the state or DMA level will be more effective that targeting
a specific neighborhood. For example, a retailer with multiple locations in the New York City market
may regard IP geo-targeting more favorably than one with a single store serving the SoHo area.


Search Targeting
Definition
Local search targeting helps advertisers target users when they look for places, businesses, housing,
entertainment, etc. in specific geographies using a search engine (such as Google or Bing). This allows
advertisers to present highly relevant localized offers and advertisements to users.

This is an effective form of advertising because the user may enter his or her location in the search
query. For example, if a user is searching for “San Francisco condos” an advertiser may purchase
those words or phrase and an ad for “restaurants in San Francisco,” may appear on the search results
page. The user is likely to take interest in this targeted type of ad over a generalized advertisement.

This is different from IP-based targeting because where the user is querying from and what the user
is querying for can be very different. For example, a user might be in New York City but looking for a
sunny vacation in Miami. An advertiser in this case could place localized listings for hotels in Miami
instead of less relevant hotels in New York if IP-based targeting were used.

Scale
Sites with local, searchable content can usually provide a highly targeted audience of users that are
performing local searches. The number of page views and users is typically on par with IP-based
targeting.

Pricing
Because of the explicit statement of interest by the user, local search advertising typically performs
well and has high value. At the same time, the inventory is more finely sliced and limited. Usually, this
type of targeting commands a premium.

This type of targeting is very accurate because the user is explicitly stating his or her interests when
entering a search query.


Explicit Profile Data Targeting
Definition
Explicit data is “registration quality data” collected either online or offline. For online registration
data, the user has certain attributes in his or her registration profile at a particular site or service,
and that data is associated with the user’s Web cookie or some sort of audience database when
the user next logs in. Offline registration data includes the sorts of data held in the massive offline
direct response industry databases built up over the last several decades. These are then matched
to a user online when that user logs in somewhere that is a partner of the data company. The site at
which the user logs in, usually an online mail or similar site, sends the name/email combination to the
data company, which then makes the match and sends back data. Ethical data providers do not put
personally-identifiable data into the cookie or audience database, but rather anonymize the data
(e.g., “male” rather than a name or address).

The data collected is not about where the user has visited, but what they have said about themselves.
Much of this data is demographic or purchase history data.

Not all “intent” data is explicit. When data collected is based on visitation to certain types of sites,
that is implicit data, even if the site is a detailed listing site. An example of implicit intent data is when
a user has visited a car listing on an auto site. An example of explicit intent data is when a user in his or
her registration preferences states an expectation to buy a car in the next 90 days.

Explicit data can either be first party (garnered by the site/service in which it is used) or third party
(garnered from an external source).

Scale
Scale is the primary concern with explicit data. Of all the targeting types, explicit data have the
smallest pools. While it allows the conclusion “this person definitively is in this category”, there are 2
prerequisites: the user has provided data in the past (online or offline) and the data has been matched
with an online user. Therefore, the pool will never be as large as the actual population. For example,
even if the number of males in this country is over 100 million, the number of explicit online data
males, across all data providers combined, is likely only 60-70% of that total. Any one data provider
has only a fraction of that.

Pricing
In general, first party data commands a far more variable premium than third party data. First party
data is often more trustworthy, as there are often far fewer steps between the data-collecting party
and the ad impression. Additionally, first party data, since by its nature is being used in ads sold by
the data collecting party, does not incur additional cost in the process of serving the ad. It is, instead,
a differentiator for the media outlet selling the ad. Third party data is usually available in much larger
quantities, and yet there is often a fee of anywhere between $0.50 to $2.00 or more paid to the data
provider by the ad seller – thus increasing the cost of goods sold (COGS) on the ad, and therefore
increasing the price.


Explicit data is both highly accurate, as the data has been freely given by the user about them, and
can be highly precise. The precision is based on the level of the questions asked by the data provider.
Some data, especially offline, is very precise. For example, there are data providers that know what
car is in an individual’s driveway, household income, past purchase history, and similar information
based on surveys.

Several underlying factors can contribute to inconsistency:

• Outdated information due to clearing of cookies
• Veracity of user registration data


Behavioral Targeting (Implicit Profile Data Targeting)
Definition
Behavioral Targeting is the ability to serve online advertising based on profiles that are inferred from
an individual user’s technical footprint and viewing behavior. As opposed to Explicit Profile Data
Targeting, Behavioral Targeting uses implicit data collected on a user’s Web browsing actions, usually
through the use of “cookies.”

The implicit data collected is with reference to where the user has been online and what activities he
or she has completed – such as specific pages visited, searches made, or clickthroughs to specific
content or ads.

Online publishers and advertisers use this information on its own or often in conjunction with explicit
profile data and/or contextual data to display more relevant advertising specific to the interests of
the user.

Scale
Data can be collected on many levels. Originally implicit data was gathered from surveys, opt-in
forms and public records. However, now data is also gathered from the digital footprint left by the
individual user through raw data feeds. This digital footprint has become ubiquitous, not only through
more sophisticated targeting, but also through the increasingly intimate use of the Internet as a vehicle
for information searches, information gathering and transactions. As the medium has grown from a
“browsing” experience to interactional so have the levels of information gathered. Newer forms of
information include the data collected about influences, social preferences through social networks
and an individual user’s content created online.

The data is often gathered in real-time and can be used for real-time decision-making so that relevant
advertising can be delivered dynamically to an individual user during their online session.

Individuals do have some control over the amount of information they share and the more technically
proficient can limit certain information gathered by disabling various file, cookies and history
settings, thus reducing the scope of the individual’s data footprint. Individuals may also elect not to
register at a Web site that offers such an option.

Pricing
Pricing for behaviorally targeted advertising commands higher ad rates than less targeted advertising
that lacks profile information about the individual user. Behaviorally targeted advertising commands
a higher price because of targeted placement versus general run-of-site (ROS) advertising.

By virtue of the targeted nature the digital footprint provides, an advertiser can request that their
advertising be placed in the appropriate places where it will be seen by the users that meet their
customer profile.

Cost Per Action (CPA), or Pay Per Click (PPC), pricing methods are often the basis for the ad serving
structure because of the ability to deliver higher results based on the higher probability of a user’s
interest.

Behavioral Targeting can be highly accurate when the user is leaving a digital footprint of their
activities as they move through the Web. Problems can arise if the computer is in public use, shared by
other people (libraries, households with shared computers, etc.), or if the amount of data collection
settings has been reduced by the user (clearing cookies).


Mobile/Location-based Targeting
Definition
Mobile/location-based targeting refers to a way to target advertisements on mobile devices such
as smartphones or feature phones, GPS receivers, tablets (such as iPads) and soon on many mobile
laptops. On phones and tablets, such advertisements can appear in a mobile Web browser or within
an app. Geographic targeting information can come in the form of either a confirmed location or a
derived location.

Confirmed Location. A phone, mobile operator network, or a user may provide confirmed-location
data. One method of confirming a location is by utilizing a phone’s GPS capabilities to produce a set
of latitude and longitude (“lat/long”) coordinates. GPS-based data may be provided by the phone
directly in some cases, or may be aggregated and brokered to third parties by the mobile operator.
User-provided confirmed-location data provide not only a specific location but also intent. For
example, users may type in a city or ZIP code into a publisher website or application, indicating either
where they are now, or where they are going in the future. In another instance, users may “check in” at
a location they specify. Ads can then be tailored to where the user has interest.

Derived Location. A mobile web or app publisher can provide derived-location data. For instance,
a publisher may derive what the likely location of the user is by the nature of its content or audience
demographics. In another instance, mobile network operators may derive a DMA location of the
user by matching the cell tower being used to the tower’s known geographic location. Some network
operators offer this derived-location data to mobile web or app publishers. This derived location
will typically be more precise in urban areas, where cell towers cover smaller areas, and less precise
in rural areas. As a third example, for devices that connect via wi-fi, a number of vendors map the
locations of hotspots and use that data to derive the approximate location of users connecting via
that technology.

Scale
The total mobile inventory in existence remains a fraction of what’s available on the Web. This
inventory is by far mostly mobile Web (~70%) rather than application inventory (~30%). In other
words, most ads are shown in a phone’s Web browser rather than within an app running on the phone.

Consequently, most available mobile ad inventory has only DMA-level targeting because mobile
Web publishers generally have access to only derived-location data. Large scale campaigns
consequently cannot be fulfilled when only targeted on neighborhood levels or sometimes even city
levels. A minority of mobile ad inventory can be layered with lat/long coordinates but not enough to
fulfill large scale campaigns trying to only reach a very specific part of a DMA and not the rest.

Pricing
Mobile-targeting pricing varies depending on the granularity of the target info -- the more finely
sliced, the more expensive the inventory. Mobile in general tends to be a more intimate experience
because there are fewer ads on screen at any given time and ads can sometimes be delivered while
a user is physically near a location to take action there. Consequently, mobile inventory may carry a
premium over its Web counterpart for a location-focused advertiser.


Confirmed-location targeting is extremely accurate. Such targeting will rarely deliver an ad for the
wrong location.

However, the vast majority of mobile inventory lacks that granularity. DMA-level targeting from
derived-location data remains the most prevalent and easily implemented mobile targeting.
Campaigns purchased to reach a sub-section of a DMA can certainly expect high precision for the
DMA but will expend money to reach areas and audiences not of interest.

Mobile targeting based on location data derived by broad demographics of a publisher’s audience
or the publisher’s content is prone to the same inaccuracies as traditional media. This targeting
relies on generalized demographic information about the target audience, but some users may fall
outside the normal. In the best cases, the publisher itself is location-specific and would have wastage
comparable to a city-magazine when considering how many users would read the publication but not
fit the target geographic market.


Contextual Targeting
Definition
Contextual targeting serves advertising messages based on content being viewed on an individual
Web page. In contrast to audience targeting, contextual targeting leverages information inferred
about the user mindset at the time an ad is being viewed. There is a visible trend towards hyper-local
targeting (ZIP code, neighborhood, etc.), which, most often leverages local content that is highly
relevant to the user. Contextual targeting does not only apply to geo-targeting, but also is available
for personal interests (travel, golf, medical condition, etc.) This form of geo-targeting can be used to
reach users at various ZIP codes, cities, DMAs, states, regions or countries. Examples of contextual
local content can include:

• Events
• Local news
• School information
• Weather: ZIP code, city, state, country
Scale
Based on the geography and the target, Contextual targeting can range from niche to mass. Typically,
content sites in the news and information category specialize in contextual geo-targeted inventory.
Naturally, portals and ad networks can also target contextually.

Pricing
Contextual targeting is one of the more accurate forms of geo-targeting and therefore typically
commands higher prices than other forms of geo-targeting. Pricing for contextual geo-targeting
varies based upon the degree of local targeting (i.e. zip code will be more expensive than state).

There is a direct correlation between accuracy of geo-targeting and price. While contextual
targeting is among the most accurate, it has exceptions. On some sites, non-local users might be
viewing travel content. For example, frequent leisure travel cities like Las Vegas and Orlando may
have a higher concentration of travelers vs. locals. It is important to check with the site on its method
to deal with these exceptions. Oftentimes, that may mean the addition of an IP layer on top of the
contextual geo-targeting.


Example A: National Advertiser
Advertiser: National Hardware Retailer (DIY Hardware)

Objective: Online promotion to increase sales and collect explicit profile data
DIY Hardware is seeking to increase the sale of Acme power tools using a promotion aimed at
DIY’s (do-it-yourselfers). The promotion is for the prize of a “Wreck” room makeover based on
users voting for the worst-looking recreation or family room based on user-photo submissions.
Acme is sponsoring the promotion along with regional sponsorship by local market furniture
stores (handled by DIY Hardware). Information gathered at contest registration will include
e-mail, age, gender, # of years in current home, # of people in household, income, and address.

IP-based Geo-Targeting
IP targeting will be done to ensure coverage of outlying markets such as suburban and rural
areas.

Search Targeting
Search targeting will be used to capture users that may be interested in purchasing power tools.
Key words such as “powertools” “electric/battery drill” and other power tool descriptions will
be used. In addition, words related to the contest such as “rec room” “remodeling” and “wreck
room” will be used.

DIY Hardware has an e-mail list of customers containing their past purchase and demographic
information. This information is deemed good because it is supplied by the consumers
themselves. The list has been actively maintained and is considered fresh. Special e-mails will be
sent to customers who are homeowners and who have indicated they have lived in their house
for more than five years. Further sorting will be done to target households that include children
and young teens. The mailing will be divided by gender and have different subject lines and
content for females and for males.

Behavioral Targeting (Implicit Profile Date Targeting)
DIY Hardware will use behavioral targeting, which collects information on a user’s Web
browsing actions, usually through the use of “cookies,” to track all their advertising and visitors
to the power tools section of their Web site. Information gathered on the user’s footprint will be
used to refine the targeting of display ads and to better place new advertising online.

Location Based Mobile Targeting
DIY Hardware employs location based targeting to reach consumers via their mobile decides.
Utilizing a phone’s GPS capabilities, DIY Hardware will send messages inviting mobile users to
vote in-store at a kiosk for the ugliest rec room or to upload their own pictures that they can take
from their cellphone. Registration can be done on a mobile browser through a special mobile site.

DIY Hardware will place a series of online display ads for the contest on specific home
decorating and interior design DIY Web sites such as Better Homes and Gardens (BHG.com) and
diynetwork.com. These ads will be placed in the areas associated with home improvement and
decorating of family rooms and recreation rooms.


Objective: Circulars
DIY Hardware is challenged by rising newsprint costs and a large shift of media time away from
newspapers to Internet. How will they evolve their circular budget to meet this challenge?

IP target users by their Designated Market Area (DMA), to synchronize digital creative with the
weekly newspaper insert, local broadcast schedule, and in-store circular to ensure that the user
receives a sales message appropriate to their local area stores. In addition to ensuring that the
national media appears localized, IP targeting can be used when DIY wants to complement their
national media with additional media weight in certain co-op markets.

Search Targeting
Search targeting allows DIY Hardware to align their own store product needs with the needs
of their consumers. For example, DIY can purchase search terms like “Chicago Gardening”,
“Richmond Paint Store” or “San Jose Plumbing” to support product sales that exist across their
desired geographies. Doing this allows them be relevant at very opportune times.

DIY Hardware will use its segmented email list to target appropriate prospects in targeted
markets informing them to get the new circular online. The segment of homeowners that are
least likely readers of newspapers will be targeted first (ages 27-40). Users can also register to
receive a weekly preview copy by email.

Behavioral Targeting (Implicit Profile Date Targeting)
DIY Hardware is able to tap into consumer behaviors on their site and around the Web to
accurately target their circular products. For starters, DIY monitors the behavior of consumers
who receive their in-store weekly email to learn which products are of interest. If a consumer
clicks on a product from the email circular, DIY Hardware takes note of that and later targets
them throughout the Web via what is referred to as retargeting. Here, as consumers view
media on thousands of Web sites, a behavioral network shows them an ad banner for the exact
product viewed on DIY’s Web site.

DIY Hardware employs location based targeting to reach consumers via their mobile decides.
Utilizing a phone’s GPS capabilities, DIY Hardware sends local ad messages via the mobile
Web or apps that encourage a consumer to visit the store near them for one of many circular
products on sale. Given the proximity of one’s cell phone to them at all times, this is a quickly
growing method to reach affluent people on the go.

DIY Hardware maintains a local presence by advertising on local media Web sites. When
DIY’s banners load on these sites, a look-up is done to determine the context of the page. A few
highly regarded Web sites and rich media vendors have the capability to then populate the ad
banner with product images and prices relevant to the local content being viewed. This allows
the advertiser to traffic one piece of creative along with a weekly feed of circular products to
populate within the banner.


Objective: Localized Branding
DIY Hardware is looking to continue to evolve a very strong national brand, and to remind customers
of all of the ways DIY Hardware can help make their homes better. They want customers to know their
local DIY Hardware understands their needs and can deliver on better customer service, in-stock
inventory, and a friendly shopping environment.

IP-based geo-targeting can be used to get blanket coverage. For larger DMAs, they can improve
on reaching a more specific audience by combining IP-based geo-targeting with another
targeting methodology.

Localized Search Targeting
DIY Hardware can tailor advertising messaging to users depending on their local search criteria.
For example, DIY Hardware can target ad creative highlighting all the different top Designer
Paint Brands that they carry at the local DIY Hardware to users searching for open houses in
Beverly Hills, CA. For users searching for open houses in San Francisco, CA, they would tailor
the ad messaging to all of the Green and Environmental friendly products they carry at DIY
Hardware stores near San Francisco.

DIY Hardware can target users on Facebook based on where they’ve indicated they live. Similar
to sending marketing by post mail, DIY Hardware can show users’ ads with local messaging
based on where a user lives. This data is typically very accurate and therefore an effective
creative that would resonate with the user could be an ad that shows the location of the nearest
DIY Hardware with a friendly face.

Behavioral Targeting (Implicit Profile Data Targeting)
Because this is slightly less accurate than Explicit Profile data targeting but has a broader reach,
DIY Hardware can create a more generalized message based on where the customer likely
lives. For example, DIY Hardware can have ads showing different colored Cape Cod-style
homes being painted in the New England area, and similar creative but on Colonial-style homes
for users in Georgia.

With the best in-stock inventory, DIY Hardware has the unique ability to promote a DIY inventory
tool that could be available for its customers on a mobile device. This could allow the customer
to check inventory for what they want to buy on their own mobile phones. Promoting a tool
available on mobile devices to check inventory in their local DIY Hardware would leverage
technology across all stores and demonstrate to the customer that DIY Hardware understands
the needs of customers constantly on the go.

DIY Hardware can tailor messaging based on the local content of the site. In a promotion for
DIY Hardware with sports teams, the advertising can be painting a chair purple-and-gold when
shown on a page about the Los Angeles Lakers, or painting pinstripes when shown on a page
where users are reading about the New York Yankees. This will further show that DIY Hardware
can relate to the customer and because of the National footprint, there is a local DIY Hardware
that can help the customer paint their chairs the correct color based on their sports team.


Example B: Local Advertiser
Advertiser
Local Auto Dealership (TLC Cadillac)

Marketing Objectives

• Drive dealership traffic
• Promote sales events
• Increase test drive for new models
Target Audience
TLC Cadillac targets a specific audience segment of car buyers. This audience segment consists
of 35+ year olds with household income $80k+ and professional or technical occupations.
Ideally, they have expressed interest in purchasing a luxury domestic car. Geographically, TLC
Cadillac only wants to reach people in Central and Southern Connecticut which is within one
hour travel from its location.

Recommended Media Plan
To reach its target audience, TLC Cadillac will push its message out to target demographic
through guaranteed-placements and will pull in buyers who are actively looking for new
vehicles through non-guaranteed performance placements. The goals of these media buys are
to drive foot traffic to the dealership in general, build awareness of large sales events, and
gather customers interested in test driving the newest vehicles.

IP-based geo-targeting
This will be utilized to deliver a large quantity of ad impressions to the target audience segment.
The best ad creative would be one for promotion of large sales events like a Labor Day Blowout
Sale since the added promotional offers would be more likely to induce foot traffic than a
general advertisement touting the dealership location. Of the IP-based geo-targeting budget,
greater allocation could be made to target the affluent Fairfield County. Specifying ZIP codes
of that county like 06830, 06831, 06832, and 06836, which identify Greenwich residents,
can further refine the ad delivery to households likely to fit the target audience parameters.
Unfortunately, the inaccuracy of IP-based geo-targeting will lead to many wasted impressions
which are delivered to misidentified users outside the target zone. However, a good amount will
be delivered to users within the target zone. Because of this result, much attention must be given
to acquiring impressions at low CPMs to ensure enough impressions are being delivered within
the target zone for the budget given.

Explicit Profile Data Targeting and Behavioral Targeting (Implicit Profile Data Targeting)
These ad buys can be utilized to deliver sizeable ad impressions to the target audience segment.
Explicit profile targeting would restrict the ad delivery to an ad view who self identified as
being a 35-64 year old who earn $80k+, and has a professional or technical occupation.
Behavioral targeting would deliver impressions to audience segments identified to be “Auto
intenders/In Market: Luxury Class” and “Auto intenders/In Market: Cadillac.” These methods
would best achieve the goal of driving general foot traffic since the media spend will be
devoted to reaching users who have gone to auto-websites in recent history or self-identified
themselves as auto-enthusiasts.


This will deliver both guaranteed placement and non-guaranteed performance placement.
To gain signups for future exclusive test drive events for new models, TLC Cadillac can make
contextually-targeted ad buys on auto portals to run ad creatives that offer the ability to
register to users reading auto-related websites. To reach users already in the market, TLC
Cadillac can pay on a performance basis to place its ads on a wide range of content sites to
reach that user who is on a site with specific keywords that relate to a stage of decision making.

Search Targeting
Using search targeting can best deliver non-guaranteed performance placement to ensure TLC
Cadillac’s message is put in front of a user actively searching for information relating to an
auto purchase. Most of the non-guaranteed placement budget should be dedicated to search
targeting to ensure sufficient fulfillment in a highly valuable inventory. Search targeting would
be best suited to drive interested customer traffic to the dealership.

Mobile Targeting
This will help drive dealership traffic and promote sales events in a similar fashion to IP-based
geo-targeting, contextual targeting, and search targeting by simply doing the same thing these
methods do for the desktop but for a mobile handset. However, more of the budget should be
allocated to achieve the goal of increasing test drives for new models. Mobile targeting can
allow TLC Cadillac to reach users who are in short walking or driving distance and offer them
the ability to walk-in the store to simply test drive a new model. TLC Cadillac can go step further
and offer their already existing free beverages for prospective customers as another way to
entice people to stop by the dealership while nearby.


Summary
Methods of Placing Geo-targeted Advertising

Definition Example Usage
Location based on user’s End-user at home using Broad-targeting of users
IP-based IP address internet; IP address of based on where they are
local ISP is detected browsing the internet
Target user searches for End-user searches for Target specific local
Search local content homes in a particular search terms initiated by
neighborhood of interest users
Location is based off End-user registers with Target users based on
Explicit
user’s registration info their home address on a their registration info
Profile
social network
Location is inferred End-user browses local Target the user with
Behavioral
based on a user’s news and sports content specific messaging
(Implicit
browsing behavior from different websites based on their browsing
Profile)
behavior
Targeting on mobile User is browsing the Target users on their
Mobile/
handsets based on GPS internet from their mobile device when
Location-
coordinates mobile device their approximate
based
location is known
Targeting to content User is browsing local User is browsing local
Contextual being viewed on the weather weather
website


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