Programmatic Quality Guide: Dstillery

Quality
GUIDE
programmatic
how to improve the quality of Campaigns
with

quality in advertisingsince 2008

What does quality mean in marketing?
Good creative? Reaching the right audience? Viewability?
Running campaigns in brand-safe environments?
All of these are inputs. We tend to focus on outputs. To us,...
Quality in marketing delivers real results that build brands
and build businesses.
As a pioneer in programmatic marketing for brands, Dstillery
has been delivering quality in advertising – “only the good stuff” –
since 2008. Last fall, we released the Inventory Quality Report,
a revealing look at fraud in our industry and a guide to how
Dstillery is leading the effort to thwart fraud in its many forms.
That report established some standards for quality in
digital marketing.
Since then, quality has become a consistent theme in our industry:
• The ANA and White Ops released a study finding that 25% of video
impressions and half of third-party sourced traﬃc is fraudulent.
• A Google study found that fewer than 50% of ad impressions
are viewable.
• Kraft said it rejects up to 85% of impressions because of poor quality.
But inventory quality is just one part of the equation.
At Dstillery, we’re committed to driving Quality ROI: the real results
that build businesses and earn the trust of marketers. Our relentless
focus on quality not only enables us to create and reach the right
audiences, but also powers our predictions of who will be most likely
to become your next customers, and when best to reach them.
In the pages that follow, you’ll learn how to improve the quality
of your campaigns, and how a focus on quality can translate
into increased marketing effectiveness.
Let’s raise a toast to quality.
Cheers!
Louise Doorn, CMO
On behalf of all your friends at Dstillery
INTRODUCTION

TABLE of
CONTENTS
PROGRAMMATIC
QUALITY GUIDE

1. 2. 3.
AUDIENCE
QUALITY
INVENTORY
& DELIVERY
CROSS
SCREEN
4.
DRIVING
ROI

photo courtesy of IMDB
5 | AUDIENCE QUALITY • CROSS-SCREEN • INVENTORY & DELIVERY • DRIVING ROI
“Anyone with an internet connection
and an idea can develop an audience.”
- kevin spacey, Actor

1. AuDIeNce QuALIty
Why Obsessing Over Audience Quality Drives ROI
Quality audience development – how the right audiences
are defined, identified, and targeted – is the backbone of
any successful campaign.
How do you ensure you’re reaching the right audience?
1. DEFINE YOUR GOALS
The first step is specifying the population you want to
replicate. The more specific the better, because this
population serves as the seed data used in prospecting
models to find your next best customer. Prospecting expands
your target audience and scales the reach of your message.
2. TAKE THE GARBAGE OUT:
DATA HYGIENE
Building quality audiences and prospect models requires
quality data. This may sound obvious, but because of the
complexity of data-driven marketing, it’s often overlooked.
We never assume that the data we use is correct, even
when we partner with industry-leading data providers.
Instead, we test and scrub data before it enters any of our
systems. We look for three primary breaches of data hygiene:
2A. NON-HUMAN TRAFFIC
We scrutinize online cookie data to ensure the cookie
corresponds with a real human, not a bot. The foundation
of all prospecting models is the data from these cookies,
including site visits and web conversions. The most
insidious and prevalent threat to cookie accuracy is
web traffic sourced from bots. We have established
standards for realistic human activity, and applied them
to all cookie behavior. We examine clickthrough rates,
site visit patterns, and number of sites visited in a given
period to identify cookies that look suspicious. We put
the suspicious cookies in a “penalty box” – a time-out
of sorts – until we have evidence that they’re human.
PROGRAMMATIC QUALITY REPORT

7 | AUDIENCE QUALITY • CROSS-SCREEN • INVENTORY DELIVERY • DRIVING ROI
50% of location signal data
can be innaccurate
This process ensures we’re delivering real ads to real
people, and that our data inputs are accurate. White
Ops recently found that we delivered more than 97%
of ads to humans, a figure that surpasses not only
all of the other programmatic vendors but also the
average direct publisher buy.

2B. STALE SIGNALS
Cookie data needs to be fresh. What a consumer did
this morning or yesterday tends to be far more relevant
for marketers than what she did last month or last year.
We score cookies every day for every brand. We look
for signals that a user is no longer in-market for a given
product or service, and we take them out of the target
pool. Prefab segments are the standard in the industry,
but they are by definition stale, and they consistently
underperform our up-to-the-minute models.
2C. ERRANT LOCATION DATA
Scrubbing location data is as important and nuanced
as cleaning up the cookie pool. Mobile companies,
app companies and data providers all share location
data on consumers via their smartphones and tablets.
Much of this data is fabricated or erroneous. In fact,
we’ve found that 50% of the location signal data we
receive is inaccurate. While other companies play
along – attaching a location signal to a consumer
increases the value of that consumer to a marketer –
we are ruthless about scrubbing out the errant data.
What comprises that 50% inaccuracy? A wide range
of deception and errors. For example, we receive raw
data that indicates that thousands of people are in a
location where they simply can’t be. The geographic
center of the continental United States is a case
in point. It’s a field in Nebraska, in the middle
of nowhere. The data we’re given indicates that tens
of thousands of people are gathering in this field every
day. This erroneous data can be generated when
people decline to share their data, so the cell phone
defaults to “U.S.,” which is all the device knows about
that user. Or it can happen if the device can’t locate
itself with enough accuracy – its location data will
default to whatever level of precision it can provide –
city, state or in this case country.
“Third-party location data can be dramatically
imprecise, depending on the methods used to
determine that location’s lat/lon,” explains Daniel
Yi, Dstillery Data Analyst. “All of our location data
is verified by human beings. We validate that each
reported location actually exists where it says it is
on a map, and if necessary we adjust the lat/long
to rooftop accuracy of any physical buildings.”
“There is no such thing as an average customer. As our tools for
learning about consumers improve, we find that the customer
population is more diverse than conventional wisdom would suggest.”
– Brian d’Alessandro, VP of Data Science, Dstillery
All of our location data is
verified by human beings

– 1 –
– 2 –
Checklist
Audience Quality:
Use fresh customer intelligence (online KPIs and offline
behaviors) to inform your audience building models
Build audiences using verified human cookies
and accurate location data (tested and scrubbed)
Employ real-time scoring of audiences vs. stale segments
to reach your best prospects at the right moment
Beyond removing large swaths of erroneous data,
we do a lot of work to contextualize and verify the
location data we receive. In fact, our Location
Still – the data repository where we store all of our
location and place data – goes through rigorous human
QA, making sure the location data we use matches
the actual places where we find your audiences.
Our human and technological checkpoints
are constantly on the lookout for data errors,
so your campaigns reach the right target audience,
using models built from accurate consumer signals –
both online and in the real world.
3. Build Your Models
Only with quality data can you build quality data
models. The models allow us to identify new
consumers for prospecting by first building a unique
signal for your brand. Your brand signal is developed
using billions of data points, from expressed behaviors
important to your brand (e.g., retail store visits) to
actions derived from first-party digital data (e.g.,
website visits, social activity, online conversions,
desktop and mobile activity) that differentiate your
top prospects from the rest of the world.

You need some level of differentiation when building
a target audience. Otherwise your target market is
everybody, which would be a complete waste of time
and money. So, we look for ways to narrow the target.
Thankfully, today we know much more about people.
The technology we’ve developed allows us to scale
the old idea of segments out to who is truly interested
in your product, at a much more granular level than
geography or demographics.
In fact, there is so much data that no human could
possibly sift through all of it. We need computers to
analyze the data. We use your existing set of
customers as a seed for building new audiences.
We use technology not just to find out who they are,
but more important to find out how they are
diﬀerent or special from the average consumer.
We let computers sift through billions of atomic behaviors
– people’s actions and choices. We look for behaviors
that differentiate your product. Once the model finds
9 | AuDIeNce QuALIty • CROSS-SCREEN • INVENTORY DELIVERY • DRIVING ROI
the secret sAuce:
How to Build Incremental Audience
By Claudia Perlich, Chief Data Scientist, Dstillery
One benefit of programmatic marketing is the
ability to deliver incremental audience. But how
do we go about building expanded audiences?
While the math and data science is quite
complicated, the concepts behind audience
expansion are actually quite intuitive.
When you think about reaching an incremental
audience, the first thing to recognize is who that
audience is not. It’s clearly not the brand’s existing
customers; they wouldn’t be incremental. It’s also not
people who have absolutely no interest in the product.
For example, there are people who love reading
books and aren’t interested in sports. If I’m selling
sports products, those people are probably not the
audience I want to reach. When we think about
building incremental audience, there has to be a
basic interest in the product we are trying to sell.
So who to target? In the past, marketers would
consider who the product is for – a certain
demographic group of a certain age, perhaps,
or a certain segment of consumers with an
interest, like sports or books. Marketers would
do segmentation analysis to understand that,
say, their customers tended to be females in
their 20s, or men over 50. But that actually
doesn’t tell us very much – even through a lot
of your customers may be women, there are
also a lot of women who don’t buy your product!

out, “Oh, your people visit this store or these websites
at greater rates than other consumers,” those become
relevant signals that we feed back into the system to
generate your unique Brand Signal.
Then we rank everyone in the market based on those
differentiating behaviors. We leave out the top group
of consumers – your existing customers – and target
the next level, those consumers who are likely ready
to purchase your product.
The advantage in this approach to incremental
audiences is that you have much more precision
in defining who they are, and it is much easier
to get them to try your product than lower
scoring consumers.
In the end, every programmatic platform has access
to the same 300 million people in the U.S. But access
isn’t a differentiator. The differentiator is knowing
which one million or 10 million people to target out
of the 300 million.
That’s why the data that feeds the models is also a
differentiator. At Dstillery, we have data partnerships
with providers that give us a very different view of what’s
happening than what you can see on the exchanges
alone. Ultimately, quality depends on having good data –
and data scientists who know what to do with it.
soop r-man ’fekt noun :
When one mobile device zooms across the map, seeming to travel dozens of
miles in mere milliseconds. this occurs when location data reported by an app
is based solely on an iP address or a user registration, or is hard-coded by
a developer, instead of recorded through GPS or Wi-fi geolocation methods.
(For more on The Superman Eﬀect, check out our blog.)
superMAN effect
THE BOTTOM LINE: BETTER PRECISION
MEANS FEWER WASTED IMPRESSIONS.
The differentiator is knowing which one million people to target
out of the 300 million. - Claudia PerLICH

Device Matching: IP is just the beginning
Freshness + Frequency = Better Accuracy

Think about all the laptops, tablets, and smartphones
hanging out at your local Starbucks. Technically, these
devices are related, since they are all using the same
IP, but the web browsing activity of one user has nothing
to do with the location activity of another. The resulting
profile would be neither useful nor valid to marketers –
except, maybe, to Starbucks.
And what about when customers leave Starbucks?
What if you want to take a mobile audience and
serve ads to them at home on their laptops, or to
continue the conversation with desktop users on
their mobile devices? Effective marketing reaches the
right consumers wherever they are. This necessitates
connecting devices – including desktop, smartphones,
tablets, and connected TVs – to create a single
consumer profile.
The solution? At Dstillery, we’ve developed proprietary
CrossWalk technology to match devices in the
most intelligent and accurate way possible.
DEVICE MATCHING: IP IS JUST
THE BEGINNING
Device matching starts with an IP connection —
as soon as two devices are seen on the same IP,
they are connected. But that connection might be
weak or strong. And as you can imagine, simple
IP matching creates a lot of false connections.
The popularity of the IP address is only one of the
criteria evaluated when making a device match.
If too many devices or too many cookies share the
same IP, the are ignored — no accurate connections
can be built out of that noise.
We take extensive measures to ensure your location
and cookie-based audiences are built using accurate,
verified data.
... desktop, smartphones, tablets,
and connected TVs... create a
single consumer profile
2. CROSS-SCREEN QUALITY
Crossing Screens with Confidence

FRESHNESS + FREQUENCY =
BETTER ACCURACY
Once we determine an IP is useful, with a reasonable
amount of devices shown on it we evaluate device
activity on the IP – namely, how recently devices
have been on it, and how frequently they are seen on
the IP. If we see a low-populated IP with the same
laptop and smartphone connected to it every day for
the past few weeks, it’s a pretty safe assumption that
the devices belong to the same household, and the
right user profile can be constructed from this match.
GO CROSS-SCREEN WITH CONFIDENCE
Once a user profile is constructed, the fun part
begins. With our CrossWalk device-matching
technology, you can target a desktop user accurately
on her smartphone, continue the conversation with a
mobile user on her desktop back at home, or deliver
sequential messaging across screens. You can even
build profiles of multiple users traveling to attend
the same concert, then continue to message
them across their devices after the show is over.
Quality device matching enables quality
cross-screen campaigns.
Figure 1
Figure 2
Quality device matching enables
quality cross-screen campaigns.
CROSS-SCREEN QUALITY IN ACTION
Adobe / Goodby Silverstein Partners
adobe wanted to connect with senior-level decision-makers attending the dreamforce
conference in real-time across screens.
“We wanted to get in front of our audiences while they were at dreamforce the second
we knew they were attendees. the fact that dstillery was able to accomplish this in
real-time was remarkable.” - Victoria Barbatelli, Communications Strategist, Goodby
Silverstein Partners
Dstillery’s location intelligence helped deliver more than one million highly qualified
impressions and hundreds of site visits during the weeklong campaign.

US Total Media Ad Spend BY CHANNEL Share
(By Media 2014 - 2018)
SOURCE: eMarketer, june 2014
As usage of new media channels grow, advertising dollars follow.
2014 2015 2016 2017 2018
% of total
37.3%
26.4%
TV DIGITAL MOBILE PRINT RADIO OUTDOOR

WE’RE BIG IN
ANtArctIcA:
Location Data Foibles and Fixes
Dstillery Geospatial Analyst Peter Lenz recently spoke
about location-based targeting, latitudes, longitudes,
and Somali pirates.
Dstillery: How has location data changed the
nature of advertising?
Peter: Location data provides a whole new dimension to
ad delivery. We used to be in a two-dimensional world:
the page where the ad is delivered and the user who
sees it. Now, with location data, we’ve moved into three
dimensions: the inventory, the user, and the context of
where that user is located when he or she sees the ad.
It’s a relatively recent shift, and it gives us a whole new
way of looking at the world. It’s really exciting.
D: So when marketers talk about location data,
do they mostly mean latitude/longitude?
P: Yes. Cell phones work on latitude/longitude, because
that’s what people are familiar with. We use “lat/long”
for two things mostly: location targeting – to reach
users who are currently in or have gone to certain
locations – and analytics, so we can analyze how
and where ads are delivered.
D: So where does the lat/long data come from?
P: What we’re seeing is data sent to us by data partners
and ad exchanges. In the case of the exchanges,
they either get the lat/long data from the cell phones
themselves, or from third-party vendors, which take
an IP address and then figure out the lat/long of a person
to a certain level of accuracy. It’s often very inaccurate.
D: Really? How can you tell?
P: According to the incoming data from exchanges,
there is a perpetual gathering of hundreds of thousands
of users standing in the exact same spot.
D: Seems unlikely. But why does this happen?
P: What’s going on is people are declining to share their
data, so the cell phone is defaulting to “U.S.,” which
is all you could know about that user. Or the device
can’t locate itself with enough accuracy, so it defaults
to whatever level of precision it can provide – city, state,
or in this case, country. We get lots of “centeroids”
of states. There’s one point in Brisbane, Australia, which
is actually capturing many users all over Queensland.
The devices don’t know exactly where, so each one
makes a best guess and says, “I’m in Brisbane!”
D: What about in populated areas?
Do you see inaccuracies there, too?
P: Yes. Very often it’s false precision – you’ll see data for

users who are often in the same building, maybe
using the same IP address. So they’re roughly within
a couple feet or hundreds of feet from each other,
but the device will say it’s ‘here’ at a specific point,
when it’s actually over there.
D: Any other common errors you see?
P: Well, latitude and longitude divide the Earth into
four quadrants, so the order the latitude and longitude
are reported in, along with whether they are positive
or negative, makes a difference. And the data often
comes to us in the wrong order. We see a lot of data
where it’s as if the Earth were turned upside down.
D: Upside down?
P: It happens a lot with app developers. They mix up
the way they report lat/long inside their code, so you
end up with this upside down ‘Shadow America’ that
lies in Antarctica and the Indian Ocean. Oddly, with
the same type of error, there’s also a ‘Shadow Europe’
in the Indian Ocean, right off of the Horn of Africa.
What we are seeing is app developers switching latitude
and longitude. There probably aren’t 10,000 people with
smartphones sitting on the Antarctic ice cap or ﬂoating
in the Indian Ocean. Maybe the Somali pirates have cell
phones, but they’re probably not playing Candy Crush!
D: How is the rest of the industry
approaching this?
P: To be honest, location data is so new, it’s still the wild
west in many ways. I’m not sure if other companies are
paying attention to these issues the way we are, but we
think it’s important, so we’re focused on it and investing in it.
“There probably aren’t 10,000 people with smartphones sitting on
the Antartic icecap or floating in the Indian ocean.” - Peter lenz
Arrrr...
Candy
CrUsh!
D: How do Marketers go about ﬁxing it?
P: The first step is just being on the lookout
for these types of errors. We have a banned
lat/lon list and, depending on the product,
we also apply multiple filters to the underlying
data to ensure accuracy.
Such as:
• Eliminating coordinates with suspicious
volumes (in many cases, “hard-coded” IP
locations in the center of zips, states, etc.)
• Removing physically impossible patterns
(like a device traveling across the country
in milliseconds)
• Ignoring apps with unlikely readings
(like 80%+ of all users being located
at only a few distinct locations)

pLAceABILIty:
The Key to Measuring Mobile
Signal Quality
By Lauren Moores, VP Analytics, Dstillery
The use of location data as a signal
for building and targeting audiences
continues to grow. At the same time,
as we have seen with other emerging data streams,
with more signal comes more noise. Without taking
certain precautions to ensure signal quality,
the likelihood of using inaccurate or even fraudulent
location data is significant – and increasing.
Location data, whether it’s from mobile phones,
tablets or wearables, is only as good as its origin
and classification. Don’t ever forget the importance
of placeability, which is the accuracy of the location
data we’re receiving.
Think about your own experience using applications
that rely on location to provide content or functionality.
On a recent road trip, for example, I found that as often
as not at least one of my map apps could not accurately
determine my current location or reverted back to a
location from hours before. Similarly, how many times
have you used Uber or Hailo and had to manually
adjust the pin to ensure a pickup within a few feet
and not blocks away?
Advertisers running geotargeted campaigns demand
accuracy, and should be similarly demanding when
they build audiences using location intelligence.
ThinkNear estimated that 19% of location-based
ad inventory available through mobile ad calls
is more than six miles off target. Ouch.
Given the increasing value placed on location information
by publishers and marketers, there is a growing
sub-industry in spoofing locations. We’ve even seen
spoof location apps crop up. Less sinister but equally
disruptive, some apps hardcode randomized locations
so that they qualify for a premium data category.
In some cases, more than half of mobile bid requests
can be considered suspicious.
With the growth of location as a means to provide
services and advertising to the right person at the right
time, we need to be aware of placeability. It is essential
that we examine the data we use for audience and
targeting so that we can avoid wasted ad impressions.
An extended version of this piece ﬁrst appeared in AdExchanger
19% of location-based ad inventory available...
is more than six miles off target

Ask your vendors which data types they’re using,
and what safeguards they use confirm accuracy!
Location Data: A Primer
Sources of location data, ranked by accuracy
Courtesy of Lauren Moores, VP of Analytics, Dstillery.
1. GPS tracking
Opt-in through an app; it is likely the
most accurate but limited if the device is
not turned on or “sight lines” are obstructed,
which more likely happens indoors.
2. Wi-Fi sensors
Incorporates the use of floor plans
and measurement of how a device
moves through an indoor area.
3. Cell tower triangulation
Relies on the density of cell phone towers
and the response of at least three tower
pings to be able to pinpoint a device.
4. Registration data
Uses the address that a user has
given, most likely a zip code, which is
then translated to a location centroid.
5. Reverse IP geocoding
Takes an IP address and chooses a
nearby latitude and longitude coordinate.
This type of identifier is fraught with error.
6. iBeacon
Opt-in by the user; built into the app by
developers; broadcasts where the device is
approximately once per second and is not
considered as accurate as other sensors.

“Bots, botnets, it’s all so abstract, isn’t
it? But what we’re really talking about is
advertisers paying for all of our computers
to get broken into.
It feels good to strike back. As more and more
of the good guys join the fight, the difference
between the world we’re creating and the one
we’re leaving behind becomes ever more stark.”
- Michael Tiffany, CEO, White Ops

by Alec Greenberg,
VP of Media Operations, Dstillery
You can have the best performing
data, models and audience targeting,
but campaigns won’t truly succeed
unless they are delivered to human
beings on curated inventory.
According to the recent study released by White Ops
and the ANA in December, bots were responsible for
between 5% and 50% of monetized traﬃc on even
the most premium publisher sites. In total, advertisers
will lose $6.3 billion globally to ad fraud in 2015.
As online ad fraud continues to proliferate and the
majority of the ecosystem plays catch-up, quality
inventory and ad delivery is far from a given.
You’re probably asking yourself, “Isn’t losing a
certain percentage of my budget to bots the cost
of doing business?” The answer is absolutely not.
Every marketer and every agency should care
about wasting advertising dollars, even a small
percentage of them, delivering ads to machines
that will never make a real purchase or be
inﬂuenced by a brand’s message.
At Dstillery, we have used our three-year head
start in fighting fraud to prove, beyond a shadow
of a doubt, that you can run an honest business
and serve ads to humans rather than bots.
We’ve developed an eight-step inventory hygiene
process to handle every inbound bid request.
These steps use two fraud-fighting patents and
two partnerships with fraud and brand safety
vendors, all to ensure that our inventory is the
highest quality in the industry.
...advertisers will lose $6.3B
globally to ad fraud in 2015
3. INVENTORY DELIVERY QUALITY
Signed, Sealed Delivered

8 Steps to Quality Inventory
(In 40 Milliseconds)
1. BID REQUEST
The BRQ is where we see inventory specifications, including the impression URL.
We look at only bid requests that use Dstillery cookies and originate from the U.S., Canada and the UK.
2. INITIAL APPROVAL: INTEGRAL AD SCIENCE
IAS helps weed out 1.5 billion suspicious or inappropriate bid requests daily.
3. DSTILLERY APPROVALS
We check URLs against blacklists and for high user overlap, weeding out an additional 30 million impressions.
4. CHILD ONLINE PRIVACY PROTECTION ACT (COPPA) COMPLIANCE
In compliance with COPPA, we do not target or collect data on anyone under the age of 13.
5. URL CHECKPOINT
We assure that the bid URL matches the URL where ads are served, and that CTRs are within a normal range.
6. WIN AUCTION!
YYour bid wins the auction – but we still keep working to ensure quality delivery.
7. FINAL CHECKPOINT
Once we win the auction, we have actual inventory data to confirm:
A) The final landing page URL matches the initial BRQ URL.
B) The final URL clears global blacklists.
C) The URL still originates from the U.S., Canada or the UK.
8. SERVING THE IMPRESSION
Only if inventory passes these final checkpoints will the ad be served. White Ops and IAS then verify the
impression was served to a real person.
If inventory doesn’t pass the final check, Dstillery serves a blank ad at no cost to the advertiser.

INTEGRAL AD SCIENCE:
Ranks Dstillery Leader In Fraud Prevention in Q4 ’14*
checkLIst
Questions Marketers Should Ask Their Media Partners:
What monitoring tools and practices do you have in place?
What quality-related criteria have your publisher partners adopted?
Do you work with third-party providers to ensure brand safety and compliance?
How do you determine which impressions are exposed to real humans?
How do you ensure that ads are served as reported, and that URLs are visible to the advertiser?
whIte ops:
Ranks Dstillery 97%+ Human Delivery in Q4 ’14+
Integral Ad Science, one of the industry leaders in inventory quality and fraud prevention, continues
to show Dstillery having the lowest percentage of ad fraud – over 4X lower than industry averages.
IAS also scored Dstillery as the lowest in terms of brand safety risk.
White Ops is a pioneer in the detection of and systematic defense against bot and malware fraud.
White Ops confirms Dstillery’s patented technology works, scoring us among the lowest fraud rates
in the industry across all inventory and campaigns:
over 97% of our ads are served
to real consumers, consistent
with our Q3’14 performance.
% of ad fraud
14.5%
3.4%
DstILLery INDustry
BeNchMArk
brand Saftey: Moderate to
Very HiGH riSk
17.5%
1.7%
DstILLery INDustry
BeNchMArk
*SOURCE: INTEGRAL AD SCIENCE SEPT 2014, +SOURCE: ANA-WHITE OPS DEC 2014 REPORT

“Acquisition is always our goal. We’re helping
the brand reach people who are considered
high lifetime value customers.
- Maggie Summers,
Media Supervisor, iCrossing

by Gilad Barash, Data Scientist, Dstillery
From awareness to DR campaigns, all
marketing tries to spur some change in
consumer behavior, and ultimately aims
to drive a significant and positive return
on investment (ROI).
Right now, quality doesn’t play a huge role in evaluation
of ROI – but it should. Much of measurement relies
on a “dollars in, dollars out” approach. Without a
focus on quality, however, we’re missing out on a
major measurement aspect of that metric – how real
those results are.
For example, when running an awareness campaign in
which reach and target market are the main success
benchmarks, we need to ask whether we are truly hitting
the number of in-market consumers as claimed? If the
data used to calculate reach isn’t verified, marketers run
a real risk of not reaching the consumers they intended
to, or not reaching individual unique consumers (or even
humans) at all. In short, the numbers on your weekly
status reports are only as good as the real results they
add to your bottom line.
Here at Dstillery, we’re committed to providing real
results to our clients, and that extends beyond sourcing
audiences or delivering your ads to real humans on
brand safe inventory. Our entire platform rests on the
quality of data: in order to build accurate models for
prospecting and to measure ROI, we need to ensure
that the data we work with is verified, and of the
highest possible quality.
Historically, our modeled audiences have performed
well for all clients across campaigns and verticals –
a feat that is achieved only when the initial data we
use to build our model is clear and correct.
What does that mean for you? Less wasted marketing
dollars, and greater certainty that the results you’re
receiving are real. White Ops/ANA projected over
$6 billion in wasted marketing spend this year,
mainly because many marketing channels do nothing
to ensure that the audience delivered matches the
intended target audience.
“Quality” isn’t just an abstract term that data scientists
use to describe the accuracy of their input data. Quality
is the backbone of our entire marketing ecosystem.
It’s a fundamental concept we should all place greater
focus on to ensure that marketing campaign results
are real and trustworthy, and truly contribute to your
business’s bottom line.
4. DRIVING ROI
Your Bottom Line

1. 2.
AUDIENCE
QUALITY
Quality in, quality out.
Make sure the data
sources (cookie and
location) are clean,
accurate, recent
and verified before
building models.
CROSS
SCREEN
Know how your
partners build
cross-screen profiles.
Do they go beyond
mere IP matching,
or is it guesswork?
PROGRAMMATICQUALITYGUIDE
Show Your Work:
Key Takeaways on Quality For Marketers

3.
INVENTORY
DELIVERY
Make sure your partners
have the appropriate
checks in place get
your ads seen by
humans in in brand-safe
environments. The best-
laid campaign plans will
fall flat if your ads aren’t
delivered as intended –
or if they reach bots
instead of humans.
4.
DRIVING
ROI
How real are your
results? Find out
how your partners
verify reporting so
you can trust your
campaign will move
your bottom line.

Dstillery Quality GUIDE Contributors:
Gilad Barash, Data Scientist
Brian D’Alessandro, VP of Data Science, @delbrains
Louise Doorn, CMO, @doorn
Alec Greenberg, VP of Media Operations, @AlecGreenberg
Peter Lenz, Geospatial Analyst
Sean Lough, Director of Marketing Communications PR, @Sean_Lough
Catherine Mietek, Director of Product Marketing, @catherinemietek
Lauren Moores, VP of Analytics, @lolomoo
Claudia Perlich, Chief Scientist, @claudia_perlich
Tom Phillips, CEO, @tomdstillery
Ori Stitelman, Senior Data Scientist
Daniel Yi, Data Analyst
Follow Us @Dstillery Connect with Us
Dstillery is a pioneer in marketing technology. We capture the promise of digital media by applying
data science and massive intersecting data sets to predict brand affinity and build audiences.
For more informaton: info@dstillery.com
470 Park Ave South, 6th Floor, New York, NY 10016 | P: 646-278-4929 | dstillery.com

Programmatic Quality Guide: Dstillery

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