Boosting impact bcg

Adding Data, Boosting Impact
Improving Engagement and Performance in Digital Advertising

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September 2014
Paul Zwillenberg, Dominic Field, Neal Rich, Kristi Rogers, and Samuel Cohen
Adding Data, Boosting
Impact
Improving Engagement and Performance in Digital Advertising
commissioned by

2 Adding Data, Boosting Impact
AT A GLANCE
Digital advertisers can achieve far better performance—and deliver much more
relevant content—by adopting the latest data-driven approaches.
Realizing Digital’s Promise
Advertisers using advanced targeting techniques and tackling performance barriers
can achieve a 32 percent improvement in cost per action (CPA), along with in-
creased consumer engagement and campaign performance. Some advertisers
improved CPA by more than 50 percent. Even those that thought they were already
operating at a best-practice level were surprised to see such gains.
Improving Engagement
Our tests found that advanced techniques have the potential to deliver substantial
increases in targeting effectiveness and user engagement.
Steps to Improved Performance
To capitalize fully on the potential of digital advertising, advertisers need to
embrace advanced techniques; learn from the wealth of data that digital technolo-
gy provides; address fragmentation in digital strategy, teams, tools, and data; and
make full use of the capabilities of digital talent.

The Boston Consulting Group 3
If he were alive today, department store magnate John Wanamaker, who famous-
ly wondered which half of the money he spent on advertising was wasted, would
be doubly frustrated. The odds are that he’d still be misspending—even though the
tools and techniques that can finally solve his conundrum would lie within his reach.
Digital’s potential—the delivery of relevant advertisements to interested users at
opportune times—has enticed marketers since the earliest days of the World Wide
Web. More recently, programmatic buying of display advertising has promised to
extend the benefits of digital advertising by using instantaneous data in a real-time
environment to reach individuals with relevant messages.
In practice, however, a variety of factors undercut digital advertising’s capabilities.
Tests conducted by The Boston Consulting Group show that although current tech-
niques are often effective, companies have an opportunity to achieve significantly
better engagement and performance by adopting the latest data-driven approach-
es—in ways that enhance both relevance and the consumer experience. (See the
sidebar “About This Report.”) Advertisers and agencies are leaving money on the
table because of inexperience with these new capabilities, inconsistent campaign
execution, and a fragmented approach to campaign development and delivery.
Realizing the Promise
For advertisers and their agencies, better performance is within their grasp. It can
be achieved through a powerful combination of data, talent, and technology—learn-
Digital advertising can engage with
and benefit consumers by delivering
highly relevant content in real time,
but many advertisers and agencies
fail to fully realize this potential. The
reasons include inexperience with the
latest targeting techniques, inconsis-
tent campaign execution, and
fragmentation in campaign strategy
and development. To gain an under-
standing of what overcoming these
impediments can mean for the
performance of a typical digital-adver-
tising campaign, Google commis-
sioned The Boston Consulting Group
to prepare this independent report.
The findings outlined in this report
have been discussed with Google
executives, but BCG is responsible for
the analysis and conclusions.
About This Report

ing from the wealth of data that digital technology provides, making full use of the
capabilities of digital talent (the new “math men” and “math women”) to optimize
campaigns, removing complexity and conflicting incentives, and embracing ad-
vanced techniques.
Tests conducted in North America and Europe with five large, diverse, and highly ex-
perienced digital advertisers in four industries—consumer products, auto, financial
services, and information services—show that advertisers that use new techniques
and start to tackle barriers to performance can score big gains. On average, the cam-
paigns in our study that employed the test approach achieved a 32 percent improve-
ment in cost per action (CPA)—the critical metric for most digital campaigns—com-
pared with equivalent campaigns run by the same advertisers that did not use this
approach. In some cases, advertisers improved CPA by more than 50 percent.
Our tests included digital display campaigns that employed programmatic, or auto-
mated, buying of advertising placements. In addition to the five global advertisers,
we worked closely with a number of experienced and sophisticated agencies to
identify how to improve performance. The tests demonstrated that the use of ad-
vanced targeting techniques can drive increased performance across all major met-
rics, improving action rates for clicks and view-throughs, in addition to CPA, by as
much as 200 percent, and reducing cost per click and cost per view by as much as
70 percent. The advanced techniques employed included search and video remar-
keting and behavioral analytics, all of which are enabled by programmatic buying.
(For definitions and descriptions of digital-advertising metrics and other terms, see
the Appendix.)
To achieve these gains in large-scale campaigns, advertisers need to take advantage
of the potential to experiment and learn online and make effective use of individu-
als—in-house or external—with advanced digital skills. Companies that do so will
likely experience an even greater impact on performance over time. Additionally,
advertisers that address the fragmentation of strategy and execution that occurs in
almost every digital-advertising campaign can compound the efficiency and perfor-
mance benefits.
Digital channels play an increasingly influential and complex role along the entire
consumer-purchasing journey. Brands need to be effective online if they want to en-
hance their influence and impact and build deep relationships with consumers, who
have a clear preference for advertising that is relevant and timely—so long as it does
not interrupt or pester. (See the sidebar “The Consumer’s Point of View.”) Achieving
a consistently high level of online relevance, by delivering pertinent messages at the
right times, requires the use of programmatic buying with real-time bidding.
Several major brand and direct advertisers in industries such as consumer pack-
aged goods and financial services have recently signaled their intentions to greatly
increase their commitment to programmatic buying. They have good reasons to
move fast. Advertisers that are quick to implement more advanced programmatic-
targeting techniques, address underperforming campaigns early, and use what they
learn in the process to instill cycles of continuous improvement will gain significant
competitive advantage over later arrivals to the programmatic party.
The campaigns in our
study that employed
the test approach
achieved a 32 percent
improvement in cost
per action—the
critical metric for
most digital
campaigns.

Advanced Techniques Deliver Higher Relevance
Standard targeting techniques, such as site-based targeting when used on its own, of-
ten do not deliver ads with a high level of relevance to consumers. Site-based target-
ing, in its simplest form, is the kind of digital targeting that serves ads determined by
the content of the website that a consumer is visiting at that moment. For example,
someone who surfs a sports-themed website may receive ads for a gym membership.
However, site-based targeting does not tell the advertiser whether the sports fan is ac-
tually in the market for a new gym. Slightly more sophisticated behavioral targeting
uses information collected by the advertiser—potentially in combination with
third-party data—to reach consumers based on their previous online activity. This
kind of targeting can identify groups of consumers who are actively looking for a new
gym by, for example, pinpointing those online (or mobile) users who have browsed
Web pages about fitness centers or even visited the membership information page of
a fitness center’s website.
The big advantage of behavioral targeting is that it identifies users whose previous on-
Advertisers worry, appropriately so,
that consumers will object to being
targeted and resent being tailed
around the Web. Marketers need to
recognize privacy concerns and focus
on building trust with consumers,
offering them transparency and choice.
Research shows that consumers, for
their part, view digital ads in predomi-
nantly pragmatic terms, but they also
see lines that should not be crossed.1
When the ads are relevant, timely,
and not intrusive, they are welcomed
and seen as useful sources of infor-
mation. If an ad presents a concrete
benefit, such as a discount or a new
product or service that a consumer
was unaware of, even better. As a
German consumer put it, “I was
looking for a leather rucksack. The
ads led me to search on other sites
that I would not have gone to.” A
French user said, “If I’m looking for a
flight for a holiday and it suggests a
hotel that I didn’t know about, that’s
useful. What’s useless is showing me
stuff I’m not interested in.”
The research found that consumers,
in general, do not object to targeting;
they appreciate receiving relevant
content. They are highly sensitive to
intrusions, however—ads that inter-
rupt or get in their way—as well as
bullying ads that are too pushy. Con-
sumers also object to repetition.
“There’s [an ad for] a t-shirt that’s got
dogs’ faces that has been chasing me
around the Internet for a very long
time.... It makes me not want to buy
anything from them,” a consumer said.
At the end of the day, the key issue is
trust. Consumers are willing to
support new uses of personal data—
but only if they trust the data user to
steward data in ways that will not
harm them. (See “In Twitter We
Trust… Maybe,” BCG Commentary,
January 2014.)
Note
1. “Advertising User Research, U.S. and
Europe,” Google User Experience Research,
June 2014.
The Consumer’s Point of View

line actions more directly indicate that they are likely to be interested in a company’s
product or service. It helps uncover what users are considering purchasing. Since be-
havioral targeting involves deciding whether to buy an impression on a user-by-user
basis—in real time—it depends on programmatic buying using real-time bidding. By
tracking the consumer’s online actions until time of purchase, it can also ensure that
users are not retargeted for what they have already bought or are no longer interested
in—an oft-cited shortcoming of digital advertising and an annoyance for consumers.
Until recently, advertisers faced a trade-off. Site-based targeting provided cam-
paigns with scale but struggled with delivering relevance on a consistent basis.
Standard behavioral targeting could deliver relevance but did not always provide
adequate scale. Within the last year or so, some advertisers and their agencies
have been employing more data-driven advanced behavioral techniques, in combi-
nation with existing techniques, to achieve the best of all worlds: to reach a large-
scale, engaged audience with the right message in a cost-effective manner. (See
Exhibit 1.)
These data-driven advanced behavioral techniques identify and engage consumers
across multiple digital channels. In the case of the fitness center, the advertiser can
target users who have viewed the gym’s online videos, clicked on its paid search
ads, or engaged with its website in some predefined way. While advanced tech-
niques at first might appear to represent an incremental improvement, our tests
found that they actually are a major advance—a step change in capability that has
the potential to deliver substantial increases in targeting effectiveness and in user
www.
• Categories and keywords
• Site-specific buys
Scale
Specificity
• Remarketing from website
• Third-party lists
Scale
Specificity
• Video/search remarketing
• Behavioral analytics
• Look-alike lists
Scale
Specificity
0010
0111
01 0
V1.0
Site-based
targeting
V3.0
Advanced
behavioral targeting
V2.0
Standard
behavioral targeting
Source: BCG analysis.
Exhibit 1 | Targeting Techniques Have Evolved to Enable Advertisers to
Achieve Scale and Specificity

engagement. Even though standard behavioral techniques are often thought to de-
crease the reach of a campaign, our tests showed that advanced behavioral tech-
niques perform differently. Advertisers can actually maintain reach while increas-
ing performance by identifying new pools of high-value consumers. The increases
we experienced were immediate and substantial, and there was also clearly the po-
tential for additional incremental improvement over time.
Many advertisers think that success in digital advertising is mainly a function of
moving more money into online channels. Our tests show that how campaigns are
constructed matters just as much as the amount of money spent. Those companies
that use more advanced, data-driven capabilities will substantially improve perfor-
mance and—perhaps even more important—gain information and learning that
will give them a big advantage over competitors.
How to Improve Engagement
Advertisers and their agencies have the tools they need. By adding advanced
techniques to their campaign mix, and reallocating budgets accordingly, the
advertisers in our study were able to produce better results in numerous areas—
including both reduced costs per action and increased action rates—while
maintaining the scale of their campaigns. (See Exhibit 2.) A major automaker, for
example, reduced its CPA by 32 percent, its cost per view-through by 29 percent, and
its cost per click by 6 percent—while increasing its click-through rate by 36 percent
and its view-through rate by 81 percent. (See the sidebar “Our Methodology.”) Even
those advertisers that thought they were operating at a best-practice level or using
the latest techniques were surprised to see such gains.
The “freshness” and “completeness” of data available for campaign targeting and
optimization is critical to the performance we achieved. Advertisers with a data
Improved return on investment,
test versus control campaign
Cost per view-through
–28%
Cost per click
–10%
Cost per action
–32%
Campaign budget (%)
TestControl
33
V1.0
and
V2.0
V3.0
100
67
V1.0
and
V2.0
V2.0
Standard
behavioral
targeting
V3.0
Advanced
behavioral
targeting
V1.0
Site-based
targeting
Exhibit 2 | Advanced Behavioral Targeting Techniques Improve Performance Gains

We used an A/B testing methodology
to analyze the impact of advanced
techniques on the effectiveness of
digital-marketing campaigns. Our
study involved five advertisers in four
different industries in Europe and
North America. For each advertiser,
we ran two campaign scenarios—a
control and a test group—in parallel
for four to six weeks with identical
budgets that ranged from $30,000 to
$80,000. Both campaigns were
expertly planned and executed, and
exclusion rules ensured no cross-con-
tamination of users between the
groups.
Each control group was designed to
be representative of a typical dis-
play-advertising campaign and was
based on past campaigns that each
advertiser had run. The control
groups’ campaigns used primarily
site-based and standard behavioral
targeting techniques. Our test
scenario employed the same tech-
niques as the control group but added
advanced targeting techniques, such
as display remarketing from search
ads, video remarketing, and behavior-
al analytics. In the test groups,
advanced techniques accounted for
approximately one-third of total
spending.
The control and test groups were
treated equally at each step of the
campaign. For the first week, all
techniques were allocated equal
budgets. By the second campaign
week, the parameters related to
individual techniques—such as bid
levels and budget pacing—were
adjusted to maximize performance.
For the final weeks of the study, the
worst-performing techniques were
dropped and their budgets reassigned
to the stronger performers.
Our analysis of performance included
clicks, view-throughs, conversions (as
defined by the advertiser), and, when
possible, user-engagement metrics on
the advertiser’s website, including
time spent on page, number of pages
visited, and bounce rate. The control
and test groups were filtered for
statistical biases unrelated to ad-
vanced targeting. The statistical signif-
icances of the performance uplifts
among the control and test groups
were tested using a resampling
approach—and the observed differ-
ences in cost per action, cost per
click, and cost per view were each
found to be statistically significant at
99.9 percent using a one-tailed test.
For a subset of the study, a third
baseline scenario was run in parallel
to the test and control groups. This
scenario included targeting tech-
niques from the control and test
groups but displayed a charity ad that
was unrelated to the campaign, rather
than the campaign creative. This
baseline scenario provided a measure
of how many of the users being
targeted in the control and test
scenarios would have viewed through,
engaged, or converted even in the
absence of a relevant ad.
Our Methodology

strategy that ensures data is available in real time (for example, right after a con-
sumer has clicked on a search ad)—and not lost across channels—will have a signif-
icant advantage over those that do not. They can target consumers at specific stages
of the purchasing journey in ways that those using standard techniques cannot—
leading to a higher level of conversions. (See Exhibit 3.)
Advanced targeting also enables the identification of users who are more readily
and deeply engaged. In the five-week period of our study, a large North American
bank used video and display remarketing from search ads, as well as behavioral an-
alytics, to reach more engaged users who subsequently spent 30,000 more hours
and visited 1,000,000 more pages on its website, compared with a control group not
targeted using advanced techniques. This means that the average targeted user who
then came to the bank’s website spent 30 extra minutes browsing, visited 14 more
pages, made seven additional site visits, and had a 10 percent lower bounce rate.
Getting the most out of advanced targeting requires a thoughtful approach to
data—in particular, choosing the right metrics for optimization. For example, our
tests indicate that view-throughs often have a higher correlation with conversions
than clicks—and view-throughs provide more data from which to optimize. But
many advertisers have no way of tracking this critical metric and tweaking the cam-
paign based on its performance. One information-services advertiser achieved a re-
duction of approximately 50 percent in its cost per conversion compared with its
best-ever historical performance by employing advanced targeting techniques and
by switching optimization to focus on view-throughs. Clicks were not strongly cor-
related to conversions for this advertiser—and compared with the alternative of us-
Video
remarketing
Third-party
lists
Categories
and keywords
Site
speciﬁc
Search
remarketing
Look-
alikes
Behavioral
analytics
Website
remarketing
1000
30
30
100
130
110
20
300
280
Purchase/
conversion
Preference
Consideration
Awareness
#
V3.0 Advanced
behavioral
targeting
V1.0 and V2.0
Site-based and
standard behavioral
targeting
#
Note: The timing of techniques along the purchasing journey in this chart is one example; it can differ by campaign. The number in each circle
is the indexed number of users who made a purchase, averaged across test campaigns in the study. The data is based on an “equal attribution”
methodology in which equal credit went to each targeting technique used during a consumer’s path to conversion.
Exhibit 3 | Advanced Techniques Target and Engage Consumers at Different Points in Their
Purchasing Journey

ing conversions as the metric for optimization, view-throughs provided many more
data points during the campaign, thereby enhancing the advertiser’s ability to sepa-
rate real performance boosts from digital noise.
Sophisticated advertisers—both brand and performance focused—have created
“proxy” metrics based on customer engagement and enabled by programmatic buy-
ing when the metrics linked directly to campaign goals (such as sales) may not be
available or significant. For example, some advanced brand marketers that do not
have their own direct e-commerce presence optimize digital campaigns on a
cost-per-engagement basis in which prolonged interactions with key Web content
are deemed to better correlate with campaign goals than simple clicks or views.
Frustrated by Fragmentation
Most companies do not take full—or anywhere near full—advantage of the ad-
vanced technologies currently available to improve targeting, engagement, and per-
formance. Often the biggest impediment is fragmentation. There’s plenty of frag-
mentation that advertisers cannot address—in audience and devices, for example,
not to mention in the digital ecosystem itself. But the fragmentation that takes
place in campaign development and execution—in strategy, teams, tools, and
data—is within advertisers’ and agencies’ power to control. These forms of frag-
mentation too often result in conflicting goals and incentives, lack of transparency
in performance, wasted effort, and sluggish execution—holding back big gains in
performance.
In most campaigns, the fragmentation starts as soon as the strategy is split between
online and offline and multiplies as decisions are made with respect to digital-
advertising channels (such as search, mobile, video, and display), buying approach
(reservation versus programmatic), targeting criteria, and so forth. A major
advertiser could not participate in our study because the company divides its
digital-display-campaign
activity across four
separate agency teams—
each addressing different
parts of the consumer
purchasing journey. Since
the stages of the journey
often overlap, teams
working on the same
campaign in a real-time
bidding environment are
bidding against each other
to attract the same users.
In most cases, the teams do
not realize this because
they are using different
tools that don’t actively
communicate with each
other. During our study, a
Advertisers Face Big Challenges
in Campaign Fragmentation
Most companies do
not take full advan-
tage of the advanced
technologies currently
available to improve
targeting, engage-
ment, and perfor-
mance.

targeting technique used by one advertiser suddenly stopped functioning. An
investigation revealed that another campaign—for the same advertiser but run by
a different team at the same agency—had gone live and was targeting audiences
based on precisely the same data but with a higher bid price. There was no process
in place to prevent this from occurring. In many such cases, advertisers risk
deteriorating performance over time as the price of targeting users rises when
competing agency teams attempt to outbid one another.
A much-heard complaint is that it takes an army to execute a campaign. Team frag-
mentation leads to inefficiency, misalignment of resources, lack of accountability,
and sometimes conflicting incentives, among other problems. “You spend so much
time getting people to work together [that] it’s dysfunctional,” a vice president of
marketing told us. Said another: “Incentives are misaligned across agencies, pub-
lishers, [and] tech providers. Digital’s fragmentation forces you to make decisions
on where to focus.”
Using unified tools can tackle inefficiencies and enable the design of campaigns
based on advanced techniques. As we found in an earlier study of digital-campaign
processes, the setup of even standard techniques is often highly inefficient, with
only about 20 percent of the activity actually creating value. The balance is taken
up in repetitive tasks, waiting time, and approvals. For advanced techniques, this
process can be even more complex and inefficient. To address efficiency issues and
take full advantage of advanced behavioral targeting techniques, advertisers need
to leverage unified tools to connect their media, workflow, and data strategies.
The use of data to optimize targeting is often cut short by a fragmented campaign
setup and approach. An ad hoc use of data results in missed opportunities. On sev-
eral occasions, we came across advertisers that had purchased prime advertising in-
ventory—for example, on the home page or masthead of a major publisher—with-
out properly setting up the mechanisms in advance to collect data from the
consumers who were exposed to the ads, missing out on the opportunity to reen-
gage with them. Given the value of consumer insights, it is surprising how many of
these opportunities are missed and how many consumer interactions go ignored.
Advertisers also find themselves stuck in a rut of insignificant metrics. A global adver-
tiser requested its agency optimize to a conversion metric that was providing only a
handful of data points each day. This made it impossible for the agency to prove that
their techniques were driving improvements in performance since the variations
were too small to be statistically significant—they could have been random noise. As
part of the study, we worked with the agency to identify a proxy metric that was both
statistically significant and correlated to the advertiser’s original performance goals.
Five Steps to Improved Performance
Digital advertising is a complex and often inefficient business, but powerful solu-
tions exist for those companies that make the effort to apply them. Last year, BCG
mapped and measured the end-to-end processes of 24 digital campaigns across
15 European advertising companies. We found that complexity too often reigned.
We identified more than 25 common pain points across the value chain caused by
To take advantage of
advanced behavioral
targeting techniques,
advertisers need to
leverage unified tools
to connect their
media, workflow, and
data strategies.

various stakeholders, processes, and tools. Those companies that had determined to
get their arms around the complexity, however, realized a saving of staff time of up
to 33 percent in their campaign operations. (See Efficiency and Effectiveness in Digi-
tal Advertising: Cutting Complexity, Adding Value, BCG Focus, May 2013.) Our 2014
study demonstrates that similar substantial uplifts are possible in targeting and en-
gagement. To achieve them, advertisers need to do five things.
Adopt a unified technology platform. This is a first step that helps pave the way for
other improvements, including the use of advanced targeting techniques. Unified
technology platforms provide a single user interface and make it possible to source
data from a single pool, eliminating the need to reconcile, consolidate, and transfer
data among multiple sources. This provides the data “freshness” and “complete-
ness” crucial to achieving good performance in targeting. Unified platforms also
allow remarketing lists to be used across tools and channels in advanced targeting
techniques such as display remarketing from search ads. An advertiser in our study
used a unified platform to make fresh search data available across two separate
teams developing search and display campaigns. This allowed for immediate
retargeting of consumers who had clicked on a search ad for the brand. Using one
set of tools also ensures that teams aren’t cannibalizing performance by bidding
against themselves—a phenomenon we witnessed several times during the study.
Among other benefits, unified platforms allow campaigns to launch faster. Agencies
can also spend more time gaining insights and fine-tuning campaigns, rather than
wasting time on manual downloads and data consolidation.
Implement advanced techniques. Three of the four advanced targeting and engage-
ment techniques tested in our study resulted in substantial improvements in almost
all key metrics for all advertisers. With display remarketing from search ads—serving
display ads to users who have clicked on a paid search link purchased by the
advertiser—marketers can target an additional group of consumers actively search-
ing for a product or service. This technique drove 24 percent of conversions in one
test campaign, with a CPA that was approximately half that of the best previously
achieved by the advertiser.
Video remarketing involves serving ads to users who have interacted with the adver-
tiser’s video channel. (Interacting, in this case, means either watching a video or
subscribing to a channel.) This drove the lowest cost per click for two of the adver-
tisers in our study. Moreover, this technique reaches users early in the purchasing
journey, when they are only just starting to engage with the brand—often acting as
an early driver of conversions overlooked in common last-click attribution models.
The use of behavioral analytics reaches users that have not only visited the advertis-
er’s website but demonstrated particular behaviors while there, such as spending a
certain amount of time or visiting a certain number of pages. When one advertiser
remarketed to users who had visited two or more pages, CPA was 26 percent lower
than simply remarketing to all website visitors.
Among the advanced techniques we tested, only targeting third-party look-alikes—
which relies on identifying third-party lists of users with characteristics akin to an
Three of the four
advanced targeting
and engagement
techniques tested in
our study resulted in
substantial improve-
ments in almost all
key metrics for all
advertisers.

advertiser’s own first-party targets—failed to generate significant improvement.
This may be because the quality of third-party lists can vary considerably and our
test period did not allow for the extensive trial and error of various lists.
Attack fragmentation. Advertisers generally look to agencies to tackle the fragmen-
tation problem, but it is the advertisers themselves that often mandate multiple
strategies and teams to begin with. Advertisers will likely have to take the lead in
addressing the issue, which will be a tough challenge. Many bad practices are
entrenched, and campaigns rely on multiple parties in their development—some of
which have little incentive to change behavior.
Still, there are some basic, no-regrets steps any company can take that will help.
These include the following:
•• Root out the causes of fragmentation. Select a typical digital campaign and map out
all the various internal and external people, teams, agencies, and tools involved.
This will show where companies sit on the “fragmentation meter” and let ad-
vertisers identify practices that can be combined or consolidated. (See Exhibit 4.)
•• Break down silos. Advertisers and agencies need to make sure everyone involved
in running campaigns communicates with each other regularly to discuss how
the strategy gets translated into execution.
•• Connect the data strategy with the overall strategy. Advertisers need to connect
Strategy
Teams
Tools
Data
Campaign
duration
Campaign
budget
Campaign
strategy
In-house
groups
Outside
agencies
Ad tech
providers
Tool versions
(such as DSPs)
Data collection
and management
Cross-channel
data sharing
Low risk of
fragmentation
“Always on”
Centrally managed
End to end
Single
Single or none
Single or none
One
Centralized
Automated
High risk of
fragmentation
Short
Split accountability
Numerous
Numerous
Numerous
Multiple
Numerous tools
and parties
Manual/None
Segmented/siloed
Position your
organization
on the meter
Sources of fragmentation that may have a negative impact
Note: DSPs = demand-side platforms.
Exhibit 4 | The Fragmentation Meter Helps Advertisers Identify Practices That Can Be
Combined or Consolidated

their data strategy with their overall strategy, putting in place tools to collect,
analyze, and leverage data in a centralized manner. Advertisers also have to
make sure that they have the metrics that correlate to and drive the key strate-
gic objectives of the campaign. Having one centralized source of data linked to
other tools ensures it is available when and where it is needed.
•• Get closer to your data. This is one of the marketer’s most potentially valuable
assets. Advertisers should ensure that no major digital assets are being
underleveraged (video, search, and website)—and that no data is lost along the
way. Marketers can test the statistical significance of their metrics and ensure
that the important progress indicators of the campaign aren’t just noise—and if
they are, test whether better proxy metrics can be created.
•• Use multiple attribution models. For example, our study found that video is often
undervalued in a “last click” attribution model, despite playing a critical role
early in the purchasing journey. Further, broad site-based targeting tended to get
too much credit.
Bring the math men and women to the table. Marketers and agencies need to
augment their digital skills. Strategy and creativity will always be critical to success-
ful campaigns, but the ability to effectively collect, analyze, and use data today is
no less essential. Too often, however, companies regard this capability as a stand-
alone function segregated from mainstream campaign development. Mathemati-
cians, statisticians, and other analytics experts need to be part of the campaign
team and part of ongoing campaign improvements and evaluations. Advertisers
should look to structure and train their internal teams to leverage these capabili-
ties—and to work with their agencies to fill skill gaps.
Test and learn. The Internet is the best marketing laboratory yet invented. Direct
marketers know this. Because their ultimate metric is online sales, they are adept
at testing one technique against another and tweaking campaigns until they get
the results they want. Brand marketers can take a page from this book, substitut-
ing their own metrics—such as average session duration as well as graphic interac-
tion and information downloading—for direct sales. Our study tested individual
campaigns one against another with the goal of seeing whether using advanced
techniques could improve effectiveness. We came away convinced that by experi-
menting further over time, we could readily deliver continuous incremental
improvements on top of the already substantial increase of 30 to 50 percent that
we achieved.
What Would Wanamaker Do?
John Wanamaker never solved his riddle, but he lacked the tools available to digital
advertisers today. Advertisers in the digital age have the ability to engage with con-
sumers in ways never before possible—and in the process not only improve sales
but also build lasting consumer-brand relationships. Advertisers need to adopt ad-
vanced tools. To use those tools, they will need to change some practices and ad-
dress fragmentation and inefficiency. Advertisers have a lot to gain from fixing the
system and realizing much of the promise that digital advertising holds.
Strategy and
creativity will always
be critical to success-
ful campaigns, but
the ability to
effectively collect,
analyze, and use data
today is no less
essential.

Appendix: A Digital-Advertising Lexicon
Digital advertising is a technical discipline, and like other such specialized fields, it
has spawned its own terminology. Here are definitions of some of the most com-
monly used terms and acronyms, including those used in this report.
Bounce rate: The percentage of people who view only one page on a website
Behavioral analytics: The analysis of user behavior on the advertiser’s website to
identify groups of users with demonstrated behaviors that can be used for targeting
Behavioral targeting: Displaying ads to individual consumers based on their on-
line behavior—for example, websites they have visited, search ads they have clicked
on, and videos they have viewed
Click-through rate: The number of people who click on an ad divided by the num-
ber of times the ad is shown
Cost per action: The cost of a campaign, or campaign technique, divided by the
number of conversions (purchases or other pre-specified action) that the campaign
or technique generates
Cost per click: The cost of a campaign, or campaign technique, divided by the
number of clicks on the campaign ad
Cost per view-through: The cost of a campaign, or campaign technique, divided
by the number of visits to the advertiser’s website made independently by people
after seeing an ad
Display remarketing from search ads: Serving display ads to users who have
clicked on a paid search link purchased by the advertiser
Programmatic buying: The automated buying and selling of digital advertising
Real-time-bidding: The auction-based buying and selling of digital-advertising in-
ventory, in real time, and on a per-impression basis
Reservation buying: An ad placement negotiated directly with the publisher or
website owner
Site-based targeting: Displaying ads based on the content of the website
Third-party look-alikes: Those consumers, identified using third-party data, who
have similar characteristics as an advertiser’s first-party target audience
View-through rate: The number of people who independently navigate to the ad-
vertiser’s website after seeing an ad divided by the number of times an ad is shown
Video remarketing: Serving display ads to users who have interacted with the ad-
vertiser’s video channel

About the Authors
Paul Zwillenberg is a partner and managing director in the London office of The Boston Consult-
ing Group and is a regional leader of the firm’s Technology, Media, and Telecommunications prac-
tice. You may contact him by e-mail at zwillenberg.paul@bcg.com.
Dominic Field is a partner and managing director in BCG’s London office and a global leader of
the firm’s Marketing practice. You may contact him by e-mail at field.dominic@bcg.com.
Neal Rich is a principal in BCG’s Chicago office. You may contact him by e-mail at
rich.neal@bcg.com.
Kristi Rogers is a project leader in the firm’s London office. You may contact her by e-mail at
rogers.kristi@bcg.com.
Samuel Cohen is a consultant in BCG’s London office. You may contact him by e-mail at
cohen.samuel@bcg.com.
Acknowledgments
The authors are grateful to the following advertising agencies for their assistance with, and support
of, the study: Audience on Demand UK, Essence, M2 Universal, and ZenithOptimedia UK. They
also wish to acknowledge the assistance of the following BCG partners and colleagues: Mark
Heath, Elias Baltassis, Julia Booth, Salvatore Cali, Guangying Hua, Jan Jamrich, Iman Karimi, John
Keezell, and Olaf Rehse.
The authors would like to thank David Duffy for his assistance in writing the report and Katherine
Andrews, Gary Callahan, Sarah Davis, Angela DiBattista, Abby Garland, Carrie Perzanowski, and
Sara Strassenreiter for their help in its editing, design, production, and distribution.
For Further Contact
If you would like to discuss this report, please contact one of the authors.

To find the latest BCG content and register to receive e-alerts on this topic or others, please visit bcgperspectives.com.
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© The Boston Consulting Group, Inc. 2014. All rights reserved.
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