The Unsung Heroes of Marketing Insight White Paper by BECKON

HOW TAXONOMY AND NORMALIZATION (MAGICALLY)
TURN MESSY BIG DATA INTO BIG INSIGHT
THE
UNSUNG
HEROES OF
MARKETING INSIGHT

WWW.BECKON.COM SALES@BECKON.COM
CONTENTS
1 INTRODUCTION
3 MODERN MARKETING DATA MANAGEMENT—THE NEW NORMAL
5 TWO BIG DATA SILO-BUSTERS: NORMALIZATION, TAXONOMY
11 STRUCTURED MARKETING DATA REAL-WORLD BENEFITS
13 THE DATA STRUCTURE IMPERATIVE: MODERN MARKETING
DEMANDS IT

1
INTRODUCTION
“The marketing performance measurement future will be
about showing early wins and faster results.”
- Source, ARF West 2015
Modern marketing intelligence demands that we gather, integrate and analyze
far more data streams than ever before, and it demands that we do it very
fast—in as near to real-time as possible. Much fuss is made over technology
systems required to process ‘big data’ (Hadoop, DMPs, etc.), and on the
analytics that matter in this new omnichannel world (mix modeling, test and
control, attribution, etc.). But the unsung (and not very sexy) heroes of real-
time, omnichannel performance analytics are more fundamental: taxonomy and
normalization. These are the keys to turning messy big data into big insight.
Without consistent categorization and normalized marketing definitions,
critical datasets of spend, marketing activity and business outcomes cannot
be merged for analysis. Without a consistently applied taxonomy, any sort
of analysis that might be conducted simply can’t to be trusted. We can’t
benchmark or drive reliable predictive analytics if we can’t trust our data.
And we won’t have confidence in any insight untrustworthy data reveals.
The finance discipline figured this out long ago. Using Generally Accepted
Accounting Principles (GAAP) to standardize definitions, and a general ledger
or chart of accounts as a taxonomy, finance makes sense of its own messy data
problem without fuss every day. Marketing intelligence professionals, too, must
get ready to work “below the waterline” on taxonomy and normalization—
the foundations of data integrity—in order to derive insight from messy
performance data.

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“Until we can be persuasive with good quality data, we’ll
continue to have disaffection for what data and models
can do for us.”
-source, ARF West 2015
In this paper, we’ll review best practices for consistent data labeling that
applies a marketing-specific taxonomy and normalizes messy marketing
data so benchmarking, reliable predictive analytics, and trustworthy insights
are possible. We’ll show what is enabled when standardized definitions and
categorizations are implemented, and we’ll give examples of how Converse,
Microsoft Mobile, UnionBank and others aligned their disparate datasets and
unlocked critical insights thanks to the power of taxonomy and normalization.

3
1 MODERN MARKETING DATA
MANAGEMENT—THE NEW
NORMAL
Marketing insights and research teams know how to structure data. They have
been structuring research studies and conducting structured data gathering
via surveys and other forms of quantitative research for a long time. And they
know the power of standardized aggregations (e.g. males 11-17, females 18 to
34, etc.).
What’s shifting is the volume and variety of data intelligence professionals
must gather, synthesize and interpret. An immense amount of data has
suddenly become available to the modern marketing organization and it’s
coming at us from every touch point – email, TV, print, radio, online display,
search, social media, mobile and web. These datasets (that we didn’t create)
are coming at us in formats we can’t control.
But the rate and variety of information streaming into marketing departments
today simply cannot be ignored. The available information has never been
more rich, nor so fresh. If we can find a way to “read” it, or make sense of it
in real-time, we can know right away which content and offers are resonating
with which audiences, and what part of our spend is most and least effective
across the entire mix. Modern marketing data simply must be wrangled, tamed
and synthesized before that can happen.
This represents a significant shift for marketing insights professionals. Insight
teams can’t just be creators of perfect datasets any longer—they must become
master data integrators. Modern marketing success simply demands that we
manage and analyze large volumes of disparate datasets with speed and agility.

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It’s no small task, but no doubt one of the largest imperatives facing
omnichannel brands today. Data normalization and categorization is a
challenge for any business function (although finance, sales and HR have
mastered the discipline far better than marketing has), but it’s especially
challenging for omnichannel marketers. Our spend and performance data is
spread across dozens of specialized execution tools and systems. Our business
outcome data (spend and revenue) is somewhere else. Brand health metrics
like net promoter scores and customer satisfaction ratings are somewhere else
still. Our data lives in spreadsheets, PDFs, word docs, PowerPoint decks and
emails. It comes in clicks, likes, gross ratings points, page views, downloads,
impressions, foot traffic and retail sales.
We simply must create a sustainable data management discipline to get to
the level of optimization speed and agility the modern marketplace demands.
Normalization and taxonomy are where it begins.

5
2 TWO BIG DATA SILO-
BUSTERS: NORMALIZATION,
TAXONOMY
If there are two silver bullets that make real-time omnichannel data
management and analytics possible, they are:
NORMALIZATION
Normalization puts the consistency into marketing data. It ensures that we’re
performing apples-to-apples comparisons between things. There are two key
aspects of our data that must be normalized: metrics and units.
Metrics
Digital marketing metrics are a relatively new thing and the vernacular we use
to describe our world morphs all the time. Which makes for a lot of confusion.
For instance, if one dataset calls every video play a “view” metric and another
dataset also calls every web page visit a “view” metric, automatically merging
the two sets doesn’t illuminate much for a marketer. In fact, it muddies the
waters—each is an importantly different customer interaction.
The finance profession is lucky in that is has a global set of standards called
Generally Accepted Accounting Principles (GAAP) that defines metrics for the
entire industry. All finance has to do is adhere to them. The marketing industry
has yet to adopt any standard definitions of things, which means we must take
the initiative to do it within our own organizations. And, like finance, we must
adhere to them.
Units
If one dataset of click metrics includes a week’s worth while another has a
month’s worth, or if one set of sales figures is in dollars and the other in yen,
then no resulting analytics can or will be trusted. Normalization reconciles
disparate data so that apples-to-apples comparisons are possible. Here’s an
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example of normalizing data that covers two different time periods:
Channel Raw Data Normalized Data
Google AdWords 1200 signups/week 1200 signups/week
DART 6000 signups/month 1500 signups/week
Now, we can directly compare the performance of each channel.
Of our two silver bullets, normalization is the more straight forward process.
Let’s spend a bit more time discussing taxonomy.
TAXONOMY
A taxonomy is a classification system. It’s the process of adding an additional
layer of description data to our raw data in the form of tagging, or “metadata”.
One familiar example is what we might have learned in high school—the way life is
classified by biologists. Here’s an example of the taxonomy that describes man:
Tag Property
Kingdom Anaimalia (“animals”)
Phyllum Chordata (“chord in the back”)
Class Mammal
Order Primate
Family Hominid (“man shaped”)
Genus Homo (“man”)
Species Sapien (“wise”)
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7
In business, the most familiar taxonomy is the general ledger or financial chart
of accounts:
Account Category
8200 Administrative Expenses
8210 Office Supplies
8220 Phone & Fax
8230 Postage
8300 Computer Expenses
8310 Software
8320 Consultant Services
8330 Other computer expenses
With this system, every one in the company tags financial data on the way
in with account codes. When we submit our expenses for reimbursement,
for instance, typically we need to report expenses and associate each with
the right general ledger category. And because all the messy spend data is
categorized on the way in, as data gets entered, the finance team has real-time
visibility into the financial status of the whole business. Also, it can instantly
drill down into each reporting category to understand spend in any one area.
“Tagging” financial information with a consistent account code structure is
what allows for instant visibility into detailed financial performance.
For marketers, if we want to know which channel most cost effectively drives
shoe sales among males aged 11-17, campaign spend and revenue must be
tagged, or described in the data by segment, channel and product.

8
In the above example, each ad spend and attributable revenue figure in column
1 would be given the three tags in its corresponding row. For instance, the
$150,000 Google AdWords spend would be tagged as follows:
PRODUCT LINE = HATS
SEGMENT = MALES 11-17
CHANNEL = GOOGLE ADWORDS
The $3.5 MM revenue figure in the same row, 2nd column would have the same
set of tags.
Of course in the real world, it gets a bit more complicated—we’re typically
active in far more than just two channels. Here’s a handy taxonomy that covers
most of the channels available to marketers today and separates them into
online and offline categories by adding another tag: channel type.
Metrics Tags
Ad Spend
Attributable
Revenue (MM)
Product Line Segment Channel
$150,000 $3.5 hats Males 11-17 Google AdWords
$600,000 $8 shoes Males 11-17 Google AdWords
$750,000 $16 hats Males 11-17 TV ads
$250,000 $14 shoes Males 11-17 TV ads
$75,000 $8.5 hats Females 11-17 Google AdWords
$450,000 $7 shoes Females 11-17 Google AdWords
$500,000 $10 hats Females 11-17 TV ads
$750,000 $18 shoes Females 11-17 TV ads

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Channel Name Property
Display Online
Email Online
Website Online
eCommerce Online
Mobile Ap Online
SEO Online
SEM Online
Facebook Online
Twitter Online
Other social Online
Video Offline
TV Offline
Radio Offline
Print Offline
Out of Home (OOH) Offline
Direct mail Offline
PR Offline
Events Offline
In-house Offline
Retail Offline
Call center Offline
But more than just being able to drill down by segment, channel, product line
and line of business, we often want frame our marketing data with the activity’s
primary intent. Here’s an example tagging system that aggregates marketing
performance data into the more holistic and universal frameworks we commonly
use—the buyer’s journey, or customer funnel that analyzes our success with
driving awareness, engagement and outcomes. It further classifies our awareness
metrics by media type.

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Metric Media Type Funnel Stage
Display impressions paid awareness
AdWords impressions paid awareness
TV impressions paid awareness
Facebook likes earned awareness
Twitter mentions earned awareness
Pinterest Pins earned awareness
Website page views owned awareness
Community page views owned awareness
Sales n/a outcome
Sign-ups n/a outcome
Downloads n/a outcome
As we can quickly see, there are many ways to use tags to classify our data. But
this is exactly what has to happen if we are able to drill down into our data at a
moment’s notice in order to answer the business questions we get every day:
Which is our best channel for laptop sales? Which content resonates best with
millennials? How well did women engage with our Mother’s Day Campaign?
Here’s a set of further examples of how we might tag incoming metrics.
Metric Tag
Click-through-rate on Google Ads offering a 10%
discount on back to school laptops targeted to Dads in
North America.
Channel = [online, SEM], Campaign = [Back to School],
Content/offer = [10% off], LOB = [consumer], Audience
Segment = [male], product group = [Laptops], and
Region = [North America], funnel stage = [engagement]
Banner ad impressions offering 2 for 1 pricing on Odwalla
products to Millennials served during a Memorial Day
campaign in Latin America.
Channel = [online, display], Campaign = [Memorial
Day], Content/offer = [2 for 1], Audience segment
= [millennials], brand = [Odwalla], region = [Latin
America], funnel stage = [Impression, paid media].
The count of Lexus RX photos shared by women over
Facebook during a Mother’s Day campaign that offered
to rebate the first month’s car payment.
Channel = [Facebook], Campaign = [Mother’s Day],
Content/offer = [First Month Free], LOB = [consumer],
Audience Segment = [female], product group = [Lexus
RX], and funnel stage = [engagement]

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3 STRUCTURED MARKETING
DATA REAL-WORLD
BENEFITS
The process of standardizing definitions and units and organizing marketing
data according to a marketing specific taxonomy has very big, real-world
benefits. Here are several examples of those benefits and how some of the
world’s top brands have realized them:
Align marketing activities with business results. The Converse North America
marketing team has gone through the exercise of applying standardized
definitions, units and taxonomy. They are now able to quickly track marketing
spend against target segments like males 11-17, and easily plot that spend
against awareness, consideration, purchase intent and sales among males 11-17.
They now know which activities aimed at males 11-17 drive the biggest purchase
intent and sales jumps among that segment—a huge breakthrough.
Faster time-to-insight. By doing the upfront work of categorizing and
normalizing data on the way in, Union bank reduced their time to insight
dramatically. Now, they’re no longer sorting through piecemeal data looking
for insights like needles in a haystack. Union Bank reduced their integrated
marketing reporting cycle by 98%—from three months to 24 hours.
Big omnichannel ah-ha moments. When Converse opened its new San
Francisco store, they were able to see for the first time, that when they ran Out
of Home and Print media in that one market, they were getting higher response
rates across the board: higher email open rates, more natural search volume,
more use of store finder functionality, as well as increased click-through rates
of the online display and search ads. Those datasets could not have been
aligned and those insights would have gone undiscovered without the process
of normalization and the consistent application of taxonomy.
1
2
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Data-driven decision-support. Through normalizing and applying a taxonomy
to their marketing data, Microsoft Mobile was able to plot different types of
“High Quality Engagements” against phone activations. Immediately, they saw
that some types of engagements did drive revenue, but others did not. Indeed,
only a few particular interactions in the series drove actual sales. Microsoft
reallocated spend to focus on those interactions that were data-proven to
drive sales.
Agency accountability. By tagging all its marketing performance data by the
agency owner (another taxonomy best practice), Converse can now easily pull
up a report that shows a normalized view of agency performance in an apples-
to-apples format: which agency delivers the most per $1 they pay them in fees.
This essential data can ensure accountability with whatever partners they work
with and helps informs decisions when relationships are up for review.
Internal benchmarking. With normalized definitions and consistent taxonomy,
GAP, Microsoft, Converse, KB Home, Stubhub and many more, stack-rank all
their efforts by any taxonomy category and can easily see which:
• country delivers the most per $1 they spend on marketing
• ad network delivers the most per $1 they give them
• social media platform drives the most engagement per $1 invested
• products or teams are getting the best results on facebook
• etc.
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5
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13
4 THE DATA STRUCTURE
IMPERATIVE: MODERN
MARKETING DEMANDS IT
“Consumer marketing analytics is the hottest space right
now—investments in it yield extremely high ROI”
-ARF West 2015
Not sexy, but oh so necessary. Taxonomy and normalization are now
fundamentally necessary for omnichannel marketing success and there’s no
turning back. Consistent categorization, normalized metrics and units are
absolutely necessary before we can call our data trustworthy, and all the
measurement methodologies we want to engage in—test and control, mix
modeling and attribution—just aren’t possible if our data remains a basket of
apples, oranges, bananas, persimmons, and kumquats.
Consistent, best practice marketing data management should now be every
omnichannel marketing departments’ top priority. Only if we take the time to
perform the unglamorous task of climbing into the diving bell and working
below the waterline to structure our data in smart, useful ways can we take full
advantage of what modern marketing has to offer.
“Marketing performance measurement is a huge
opportunity and worth the journey.”
-ARF West 2015

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ABOUT BECKON
Beckon is omnichannel analytics software for marketing in all its modern
complexity. Our software-as-a-service platform integrates messy marketing
data and delivers rich dashboards and scorecards for cross-channel marketing
intelligence. Built by marketers for marketers, Beckon is the dashboard to the
CMO—best-practice analytics and marketing-impact metrics right out of the box
for ultra-fast time to marketing value. Beckon serves marketers who want to bring
order to chaos, make data-informed optimization decisions, and tell the marketing
story in terms of business impact. Find your strength in numbers with Beckon.
LEARN MORE
Contact us for a complimentary consultation to ﬁnd out how Beckon can help
you better demonstrate the marketing contribution at your organization.
SALES@BECKON.COM

The Unsung Heroes of Marketing Insight White Paper by BECKON

The Unsung Heroes of Marketing Insight White Paper by BECKON

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