the marketing measurement journey white paper by beckon
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HOW TO GO FROM MARKETING DATA CHAOS
TO PREDICTIVE ANALYTICS
THE MARKETING
MEASUREMENT JOURNEY
THE MARKETING
MEASUREMENT JOURNEY
SALES@BECKON.COMWWW.BECKON.COM
CONTENTS
3 INTRODUCTION
5 STAGE 0: LOTS OF CROSS-CHANNEL DATA, NO INTEGRATION
7 STAGE 1: MANUAL INTEGRATION OF CROSS-CHANNEL DATA
11 STAGE 2: AUTOMATED INTEGRATION OF CROSS-CHANNEL DATA
14 STAGE 3: PROACTIVE PLANNING
17 STAGE 4: DATA MODELING
20 RESOURCES
SALES@BECKON.COMWWW.BECKON.COM
We know great marketing matters. But telling the story of marketing’s impact
on the business—and having the concrete metrics to prove it—seems impossible
given the chaos of modern marketing. And going further, using metrics to
accurately predict what the impact of particular marketing decisions will be,
that’s marketing’s Holy Grail. But without the metrics that prove our contribution,
it will be forever out of reach.
Well, bust out your Grail gloves
and clear some space on the
mantle. Because today, with
the right approach to data, we
can not only show the business
impact of everything that
marketing does, but predict the
business outcome of particular
marketing decisions.
But in order to get there, we
have a road ahead—and most
of us have barely begun. According to the Economist Intelligence Unit, just
24% of marketers say we consistently use data to develop actionable insights
for the overall marketing strategy. The No. 2 complaint (after lack of budget) is
“difficulty in interpreting big data”. Multi-channel marketers especially, who deal
with large, messy, disparate data sets, face specific challenges in the quest for
top-shelf marketing analytics.
So, while every marketer would love to dive right into predictive analytics, it’s
not something that happens overnight. Marketing measurement is a journey of
64% of marketers claim their companies suffer from “digital dysfunction”—uncertainty about how to integrate digital strategies into themarketing mix.
—Domus with Harris Interactive
INTRODUCTION maturity. What we can do is understand where on that journey our organization
is now, and be intentional and methodical about our next step—and the next
step, and the next as we build out a marketing measurement capability that’s
best in class.
This paper describes the four stages we must go through to develop an analytics
framework that measures and predicts marketing’s impact on the business.
STAGE 1: MANUAL INTEGRATION OF CROSS-CHANNEL DATAUsing data to describe the past
STAGE 2: AUTOMATED INTEGRATION OF CROSS-CHANNEL DATAUsing data to act
STAGE 3: PROACTIVE PLANNINGUsing data to plan
STAGE 4: PREDICTIVE ANALYTICSUsing data to predict
We’ll walk through the ins and outs of each stage and give you the tools to
recognize where you are now and start your own journey. Along the way, we’ll
introduce you to a typical multi-channel marketer, Mike, and share his story of
marketing measurement maturity.
But before we dive in, let’s remind ourselves what life is like for most marketers
today—those of us at Stage 0, who have yet to embark on any measurement
journey at all.
3
SALES@BECKON.COMWWW.BECKON.COM
We know great marketing matters. But telling the story of marketing’s impact
on the business—and having the concrete metrics to prove it—seems impossible
given the chaos of modern marketing. And going further, using metrics to
accurately predict what the impact of particular marketing decisions will be,
that’s marketing’s Holy Grail. But without the metrics that prove our contribution,
it will be forever out of reach.
Well, bust out your Grail gloves
and clear some space on the
mantle. Because today, with
the right approach to data, we
can not only show the business
impact of everything that
marketing does, but predict the
business outcome of particular
marketing decisions.
But in order to get there, we
have a road ahead—and most
of us have barely begun. According to the Economist Intelligence Unit, just
24% of marketers say we consistently use data to develop actionable insights
for the overall marketing strategy. The No. 2 complaint (after lack of budget) is
“difficulty in interpreting big data”. Multi-channel marketers especially, who deal
with large, messy, disparate data sets, face specific challenges in the quest for
top-shelf marketing analytics.
So, while every marketer would love to dive right into predictive analytics, it’s
not something that happens overnight. Marketing measurement is a journey of
maturity. What we can do is understand where on that journey our organization
is now, and be intentional and methodical about our next step—and the next
step, and the next as we build out a marketing measurement capability that’s
best in class.
This paper describes the four stages we must go through to develop an analytics
framework that measures and predicts marketing’s impact on the business.
STAGE 1: MANUAL INTEGRATION OF CROSS-CHANNEL DATAUsing data to describe the past
STAGE 2: AUTOMATED INTEGRATION OF CROSS-CHANNEL DATAUsing data to act
STAGE 3: PROACTIVE PLANNINGUsing data to plan
STAGE 4: PREDICTIVE ANALYTICSUsing data to predict
We’ll walk through the ins and outs of each stage and give you the tools to
recognize where you are now and start your own journey. Along the way, we’ll
introduce you to a typical multi-channel marketer, Mike, and share his story of
marketing measurement maturity.
But before we dive in, let’s remind ourselves what life is like for most marketers
today—those of us at Stage 0, who have yet to embark on any measurement
journey at all.
4
SALES@BECKON.COMWWW.BECKON.COM
We know great marketing matters. But telling the story of marketing’s impact
on the business—and having the concrete metrics to prove it—seems impossible
given the chaos of modern marketing. And going further, using metrics to
accurately predict what the impact of particular marketing decisions will be,
that’s marketing’s Holy Grail. But without the metrics that prove our contribution,
it will be forever out of reach.
Well, bust out your Grail gloves
and clear some space on the
mantle. Because today, with
the right approach to data, we
can not only show the business
impact of everything that
marketing does, but predict the
business outcome of particular
marketing decisions.
But in order to get there, we
have a road ahead—and most
of us have barely begun. According to the Economist Intelligence Unit, just
24% of marketers say we consistently use data to develop actionable insights
for the overall marketing strategy. The No. 2 complaint (after lack of budget) is
“difficulty in interpreting big data”. Multi-channel marketers especially, who deal
with large, messy, disparate data sets, face specific challenges in the quest for
top-shelf marketing analytics.
So, while every marketer would love to dive right into predictive analytics, it’s
not something that happens overnight. Marketing measurement is a journey of
STAGE 0: LOTS OF CROSS-CHANNEL DATA,NO INTEGRATIONUsing marketing data haphazardly—if at all
1Stage 0 is the reality for most of us, though not all marketing departments want
to admit it. Flying blind is the norm. But it’s important to recognize that it’s not
our fault—our marketing data is a mess for reasons beyond our control:
• We may plan and communicate in integrated ways, but execution—sending
emails, trafficking ads, posting tweets—typically happens in a siloed fashion.
• The vast majority of us use specialized, best-of-breed tools for marketing automa-
tion and campaign management—each of which produces its own stream of data
exhaust in its own unique format.
• Reporting functionality in these tools, if available at all, is typically provided as an
afterthought and lacks the robust analyses we need.
• We have an array of specialized agency partners, some online, some off, and
each with its own reporting process and format.
In a nutshell, marketing today
relies upon highly specialized
teams performing highly
specialized functions using
highly specialized apps and
tools. So it’s no surprise that
our marketing data lives in
silos. Email tools give us email
data, our agencies give us
media data, and so on. We fall
victim to “marketing entropy”,
where our marketing data is in a state of constantly increasing disorder. To put
structure back into the system takes enormous energy, and we don’t know where
to begin.
Most marketers tackling data integration for the first time attempt to do it
manually. It’s a clunky, cumbersome and inefficient process. But the good news
is at least we’ve embarked on our journey toward marketing measurement
maturity—we’ve jumped into Stage 1.
maturity. What we can do is understand where on that journey our organization
is now, and be intentional and methodical about our next step—and the next
step, and the next as we build out a marketing measurement capability that’s
best in class.
This paper describes the four stages we must go through to develop an analytics
framework that measures and predicts marketing’s impact on the business.
STAGE 1: MANUAL INTEGRATION OF CROSS-CHANNEL DATAUsing data to describe the past
STAGE 2: AUTOMATED INTEGRATION OF CROSS-CHANNEL DATAUsing data to act
STAGE 3: PROACTIVE PLANNINGUsing data to plan
STAGE 4: PREDICTIVE ANALYTICSUsing data to predict
We’ll walk through the ins and outs of each stage and give you the tools to
recognize where you are now and start your own journey. Along the way, we’ll
introduce you to a typical multi-channel marketer, Mike, and share his story of
marketing measurement maturity.
But before we dive in, let’s remind ourselves what life is like for most marketers
today—those of us at Stage 0, who have yet to embark on any measurement
journey at all.
5
FLYING BLIND
Mike, VP of marketing and planning for a sports equipment retailer, knew his
team was doing a bang-up job. During his tenure, e-commerce sales had risen
by more than 25% and in-store sales were up 27%. But the C-suite didn’t give
his team much credit. They claimed there were a host of other factors at work
besides the efforts of Mike’s team.
Mike couldn’t tell compelling stories of marketing’s impact on the business
because his team was struggling to manage its marketing data. They were
awash in AdWords spreadsheets, reports from Socialbakers and Marketo,
Google Analytics data, and PowerPoint slides from three different ad agencies.
There was just too much data, and it didn’t fit together. Although Mike’s team
was able to use marketing data to optimize within channels, they couldn’t use
it to inform cross-channel strategy—in terms of the big picture, Mike and his
team were flying blind.
When questions would come down from the CEO, Mike’s team would jump
on them and spend endless hours pulling numbers from a welter of data
sources—paper printouts of last year’s campaigns, digital files sitting in
email inboxes, apps they had to log into, and on and on. Three weeks later,
they’d finally have an answer. By that time, the CEO had often forgotten
she’d asked the question in the first place. Other times the analysis just led
to more questions like, “Can I have that data broken out by target audience?”
Mike knew that meant another three-week exercise, and he constantly
found himself slinking back to the drawing board and prepping his team for
another late night.
SALES@BECKON.COMWWW.BECKON.COM
Stage 0 is the reality for most of us, though not all marketing departments want
to admit it. Flying blind is the norm. But it’s important to recognize that it’s not
our fault—our marketing data is a mess for reasons beyond our control:
• We may plan and communicate in integrated ways, but execution—sending
emails, trafficking ads, posting tweets—typically happens in a siloed fashion.
• The vast majority of us use specialized, best-of-breed tools for marketing automa-
tion and campaign management—each of which produces its own stream of data
exhaust in its own unique format.
• Reporting functionality in these tools, if available at all, is typically provided as an
afterthought and lacks the robust analyses we need.
• We have an array of specialized agency partners, some online, some off, and
each with its own reporting process and format.
In a nutshell, marketing today
relies upon highly specialized
teams performing highly
specialized functions using
highly specialized apps and
tools. So it’s no surprise that
our marketing data lives in
silos. Email tools give us email
data, our agencies give us
media data, and so on. We fall
victim to “marketing entropy”,
where our marketing data is in a state of constantly increasing disorder. To put
structure back into the system takes enormous energy, and we don’t know where
to begin.
Most marketers tackling data integration for the first time attempt to do it
manually. It’s a clunky, cumbersome and inefficient process. But the good news
is at least we’ve embarked on our journey toward marketing measurement
maturity—we’ve jumped into Stage 1.
6
Companies must develop “empirically-based engagement strategies”—strategies informed by data around past customer experiences and behaviors.
—Digitizing the Consumer Decision Journey,
McKinsey & Company
SALES@BECKON.COMWWW.BECKON.COM
Like Mike, many of us have reached the point where flying our marketing function
blind is not an option. So we dip our toes in the waters of Stage 1 and attempt to
figure out where we’ve been by using data to describe the past. We hire an agen-
cy or analyst to manually pull channel KPIs from disparate sources and merge
them in ways that make cross-channel sense.
It’s absolutely the right idea—we have to extract data from each of our
specialized execution tools and bring it together in a sensible way if we’re to
have visibility into cross-channel performance. But we quickly see a number of
limitations to this manual approach, including:
• It’s labor intensive (in other words, expensive). Merging disparate sets of KPIs
so they make sense requires that we transform the data in some way—that we
add a layer of metadata, tagging and/or formulae so that our underlying data
is associated in a useful, consistent way. For instance, labeling all page views,
retweets and shares as “engagements” lets us associate and compare what
happens on our website with what happens on Twitter. Further, it often makes
sense to claim certain KPIs are worth more than others—that one social media
share is equal to three page views, for instance. Manually performing this kind
of data transformation is hugely labor intensive and can quickly become cost
prohibitive.
• There’s a huge time lag. Extracting data manually simply takes a lot of time.
Yes, our goal in Stage 1 is to use data to describe the past. And as we continue
our journey toward predictive analytics, we’ll see how important historical data
becomes. But if we can only see into an outdated slice of the past—if the data
in our reports is always a quarter, six months or a year old—we risk basing our
decisions on stale information. We need to describe the past consistently and
in detail—and we need our data to be as fresh as possible. The Holy Grail of
marketing calls for access to marketing data in real time, or as near to it as
possible.
• The data is error prone. By definition, manual data transformation means humans
are doing it. And because in a multi-channel arena it’s such a multi-faceted,
complex task, there’s a high likelihood that it will be full of errors.
• The manual effort takes a toll. Often we spend more time cutting, pasting and
transforming our marketing data sets so they’ll work together than we do analyz-
ing the data, gathering insights and reporting conclusions. When our analyst
starts complaining, “This is not what I was hired to do,” we know we’re in trouble.
• Our spreadsheets are “brittle”. Each month, someone on the team has been en-
tering a metric by region, but when we want to see it broken out by customer
segment, we hit a wall. We could go back to the source data, but then we’re
facing the problems above all over again: It’s labor intensive, takes too much
time, results in too many errors and takes a toll on our employees.
But remember, developing a marketing measurement capability with an eye to
predictive analytics is a journey. An integrated view, even if manually provided,
is much better than flying blind. When we integrate marketing data manually,
we can ease many of these pain points by making sure our data is complete
(captures spend and performance KPIs from ALL channels and sources), unified
(lives together in a single repository) and, importantly, properly structured.
Raw marketing data comes to
us relatively unstructured—with-
out the metadata, tagging
and/or formulae that make it
work together. Some of our
marketing spend is in dollars,
some in euros and some in
yen. We’ve got page views, TV
impressions, email opens and
more. If none (or even just
some) of the data is structured
such that it’s associated, our
view of the past is always incomplete. We can only see one slice at a time—the
Euro slice, the AdWords slice, the email slice and so on. Structuring our market-
ing data so that disparate data sets are associated enables apples-to-apples
comparisons.
Structuring our marketing data also means putting it into the language of
business. No CEO or CFO cares about opens, clicks, views or followers—but
they do care about customer engagement. The way we transform (structure
and associate) our data on the way in has everything to do with how much
insight we can extract from it later. If we hope to derive marketing insights from
our marketing data (e.g., use data to describe how we’ve been driving customer
engagement), our structure must have a marketing point of view (i.e., we must
define “engagement” KPIs).
The right marketing data structure is an incredibly strategic decision. Meet
with your CFO to ensure it aligns with the way the business reports results as
well—that way all the dots between marketing activity and business outcomes
are fully connected. If the business reports financials by segment, for example,
make sure your data structure can also describe marketing activities and
outcomes at the segment level.
Keeping all this in mind, your
marketing data structure
should also be flexible—it
should be easy to add channels,
campaigns, segments, regions
and so on as the business
grows and reorganizes. For
a deeper dive, see Marketing
Data Management in the Age
of Integration.
The bottom line is that the business as a whole must decide what goals and
objectives to pursue, then marketing must develop a data structure that
delivers actionable analytics—key diagnostic ratios and aggregate metrics that
track overall marketing performance against business goals. These can include
performance ratios (brand health, paid-to-earned media ratios, engagement
rates, etc.) and efficiency ratios (cost per engagement, ROI and the like). For
an in-depth guide to building a comprehensive, actionable marketing analytics
framework, see The Integrated Marketing Analytics Guidebook: Metrics That
Matter.
STAGE 1: MANUAL INTEGRATION OF CROSS-CHANNEL DATAUsing data to describe the past
2
Using marketing data to act means we must manage our data with the same
speed and complexity as we execute our campaigns and programs. For
this reason, most marketers come to realize that integrating and managing
data manually—with people power—is untenable in today’s fast-paced,
omni-channel marketing landscape. It’s too complicated and too slow. And, all
too frustrating—turning marketing’s unwieldy data sets into a decision-driving
business asset means repeating the same rote steps week after week, month
after month, year after year. That’s no job for marketers. It’s a job tailor-made
for technology.
In other areas of marketing, automated solutions that handle complex tasks
quickly and accurately have pushed aside manual solutions—automated media
buying and email marketing, for instance, are now ubiquitous at both brands
and agencies. Today, the task of managing and reporting out marketing data
can be automated as well.
The benefits of letting technology do the heavy lifting of integrating and structur-
ing cross-channel marketing data are immense:
• Real-time data for real-time
decisions. Marketing teams
move fast and make decisions
at a breakneck speed. Our
data has to move as fast as
we do. When cross-channel
and cross-platform spend and
performance data at both the
campaign and content level is
delivered daily, we have 365
chances per year to optimize.
• More time for insight and action. Automating our cross-channel marketing data
frees our people from data chaos so they can focus on gathering more insights
and making better decisions.
• Accuracy. No question, automation is more accurate and reliable than a
manual approach.
• Flexibility. Count on the fact that the business questions we need to answer
will always change. Technology lets us pull together metrics at any level of gran-
ularity we can imagine—we can pivot, slice and dice our data instantly from any
angle. Humans working with spreadsheets are simply not as flexible.
To use marketing data to act, it must be accessible, real-time, trustworthy
and accurate. That requires ongoing, consistent ETL—a term familiar to IT
departments, but relatively new to marketers. It stands for extract, transform
and load.
For decades, IT departments—in service to finance, operations and human
resources—have partially or fully automated the task of extracting data from
a number of native tools, transforming that data so it’s all associated, and
loading it back into a single, structured repository for reporting. Thanks to
the recent explosion of available marketing channels, marketing departments
suddenly face an enormous ETL challenge as well—arguably, the most complex
and extensive ETL challenge ever. Traditionally, the business sends IT to
the rescue. But because the ETL process is especially complex for modern
marketers—involving countless KPIs across dozens of channels—old-school,
IT-style ETL typically misses the mark. The reports delivered are too generic,
lacking the marketing-specific insight we need.
Extracting, transforming and loading marketing data is a unique use case with
very specific requirements. For more, see Is ETL Outsourcing Right for You?
At this point, with an automated solution integrating our marketing data, our
cross-channel visibility is accurate, complete and real-time. We can identify
trends early, recognize mistakes quickly, optimize continuously, and spot
opportunities in time to act on them. What’s more, we’re perfectly positioned
to move on to Stage 3: proactive planning.
7
DATA WRANGLING
Sick of playing catch-up all the time, Mike decided he needed a master
spreadsheet of important KPIs culled from all his single-channel reports. He
expanded the contract with his agency and tasked them with pulling together
an integrated spreadsheet that aggregated data from all the execution tools
they used. Mike then asked them to generate a monthly PowerPoint report so
he could proactively deliver integrated reports to the management team.
Defining which KPIs mattered and what metrics to pull out of the various tools
was a six-week affair. When the newly structured reports actually started to
arrive, they were full of eight-week-old data. Mike raised his concerns with the
agency, who replied that so many man-hours were needed to aggregate his
marketing data that he’d have to double his spend with them just to reduce
the reporting lag to four weeks. So Mike moved forward with what he could
get.
When Mike presented his new reports at a management meeting, someone
asked him about a huge spike in April conversions—that was the slowest
month for the business, so it didn’t make sense. Mike said he’d look into it.
The agency sent him the largest spreadsheet he’d ever seen—14 tabs plus 62
hidden tabs. Trying to follow their calculations was impossible, so he gave
up and hoped the question about the big spike in April conversions would
be forgotten.
SALES@BECKON.COMWWW.BECKON.COM
Like Mike, many of us have reached the point where flying our marketing function
blind is not an option. So we dip our toes in the waters of Stage 1 and attempt to
figure out where we’ve been by using data to describe the past. We hire an agen-
cy or analyst to manually pull channel KPIs from disparate sources and merge
them in ways that make cross-channel sense.
It’s absolutely the right idea—we have to extract data from each of our
specialized execution tools and bring it together in a sensible way if we’re to
have visibility into cross-channel performance. But we quickly see a number of
limitations to this manual approach, including:
• It’s labor intensive (in other words, expensive). Merging disparate sets of KPIs
so they make sense requires that we transform the data in some way—that we
add a layer of metadata, tagging and/or formulae so that our underlying data
is associated in a useful, consistent way. For instance, labeling all page views,
retweets and shares as “engagements” lets us associate and compare what
happens on our website with what happens on Twitter. Further, it often makes
sense to claim certain KPIs are worth more than others—that one social media
share is equal to three page views, for instance. Manually performing this kind
of data transformation is hugely labor intensive and can quickly become cost
prohibitive.
• There’s a huge time lag. Extracting data manually simply takes a lot of time.
Yes, our goal in Stage 1 is to use data to describe the past. And as we continue
our journey toward predictive analytics, we’ll see how important historical data
becomes. But if we can only see into an outdated slice of the past—if the data
in our reports is always a quarter, six months or a year old—we risk basing our
decisions on stale information. We need to describe the past consistently and
in detail—and we need our data to be as fresh as possible. The Holy Grail of
marketing calls for access to marketing data in real time, or as near to it as
possible.
• The data is error prone. By definition, manual data transformation means humans
are doing it. And because in a multi-channel arena it’s such a multi-faceted,
complex task, there’s a high likelihood that it will be full of errors.
• The manual effort takes a toll. Often we spend more time cutting, pasting and
transforming our marketing data sets so they’ll work together than we do analyz-
ing the data, gathering insights and reporting conclusions. When our analyst
starts complaining, “This is not what I was hired to do,” we know we’re in trouble.
• Our spreadsheets are “brittle”. Each month, someone on the team has been en-
tering a metric by region, but when we want to see it broken out by customer
segment, we hit a wall. We could go back to the source data, but then we’re
facing the problems above all over again: It’s labor intensive, takes too much
time, results in too many errors and takes a toll on our employees.
But remember, developing a marketing measurement capability with an eye to
predictive analytics is a journey. An integrated view, even if manually provided,
is much better than flying blind. When we integrate marketing data manually,
we can ease many of these pain points by making sure our data is complete
(captures spend and performance KPIs from ALL channels and sources), unified
(lives together in a single repository) and, importantly, properly structured.
Raw marketing data comes to
us relatively unstructured—with-
out the metadata, tagging
and/or formulae that make it
work together. Some of our
marketing spend is in dollars,
some in euros and some in
yen. We’ve got page views, TV
impressions, email opens and
more. If none (or even just
some) of the data is structured
such that it’s associated, our
view of the past is always incomplete. We can only see one slice at a time—the
Euro slice, the AdWords slice, the email slice and so on. Structuring our market-
ing data so that disparate data sets are associated enables apples-to-apples
comparisons.
Structuring our marketing data also means putting it into the language of
business. No CEO or CFO cares about opens, clicks, views or followers—but
they do care about customer engagement. The way we transform (structure
and associate) our data on the way in has everything to do with how much
insight we can extract from it later. If we hope to derive marketing insights from
our marketing data (e.g., use data to describe how we’ve been driving customer
engagement), our structure must have a marketing point of view (i.e., we must
define “engagement” KPIs).
The right marketing data structure is an incredibly strategic decision. Meet
with your CFO to ensure it aligns with the way the business reports results as
well—that way all the dots between marketing activity and business outcomes
are fully connected. If the business reports financials by segment, for example,
make sure your data structure can also describe marketing activities and
outcomes at the segment level.
Keeping all this in mind, your
marketing data structure
should also be flexible—it
should be easy to add channels,
campaigns, segments, regions
and so on as the business
grows and reorganizes. For
a deeper dive, see Marketing
Data Management in the Age
of Integration.
The bottom line is that the business as a whole must decide what goals and
objectives to pursue, then marketing must develop a data structure that
delivers actionable analytics—key diagnostic ratios and aggregate metrics that
track overall marketing performance against business goals. These can include
performance ratios (brand health, paid-to-earned media ratios, engagement
rates, etc.) and efficiency ratios (cost per engagement, ROI and the like). For
an in-depth guide to building a comprehensive, actionable marketing analytics
framework, see The Integrated Marketing Analytics Guidebook: Metrics That
Matter.
Using marketing data to act means we must manage our data with the same
speed and complexity as we execute our campaigns and programs. For
this reason, most marketers come to realize that integrating and managing
data manually—with people power—is untenable in today’s fast-paced,
omni-channel marketing landscape. It’s too complicated and too slow. And, all
too frustrating—turning marketing’s unwieldy data sets into a decision-driving
business asset means repeating the same rote steps week after week, month
after month, year after year. That’s no job for marketers. It’s a job tailor-made
for technology.
In other areas of marketing, automated solutions that handle complex tasks
quickly and accurately have pushed aside manual solutions—automated media
buying and email marketing, for instance, are now ubiquitous at both brands
and agencies. Today, the task of managing and reporting out marketing data
can be automated as well.
The benefits of letting technology do the heavy lifting of integrating and structur-
ing cross-channel marketing data are immense:
• Real-time data for real-time
decisions. Marketing teams
move fast and make decisions
at a breakneck speed. Our
data has to move as fast as
we do. When cross-channel
and cross-platform spend and
performance data at both the
campaign and content level is
delivered daily, we have 365
chances per year to optimize.
• More time for insight and action. Automating our cross-channel marketing data
frees our people from data chaos so they can focus on gathering more insights
and making better decisions.
• Accuracy. No question, automation is more accurate and reliable than a
manual approach.
• Flexibility. Count on the fact that the business questions we need to answer
will always change. Technology lets us pull together metrics at any level of gran-
ularity we can imagine—we can pivot, slice and dice our data instantly from any
angle. Humans working with spreadsheets are simply not as flexible.
To use marketing data to act, it must be accessible, real-time, trustworthy
and accurate. That requires ongoing, consistent ETL—a term familiar to IT
departments, but relatively new to marketers. It stands for extract, transform
and load.
For decades, IT departments—in service to finance, operations and human
resources—have partially or fully automated the task of extracting data from
a number of native tools, transforming that data so it’s all associated, and
loading it back into a single, structured repository for reporting. Thanks to
the recent explosion of available marketing channels, marketing departments
suddenly face an enormous ETL challenge as well—arguably, the most complex
and extensive ETL challenge ever. Traditionally, the business sends IT to
the rescue. But because the ETL process is especially complex for modern
marketers—involving countless KPIs across dozens of channels—old-school,
IT-style ETL typically misses the mark. The reports delivered are too generic,
lacking the marketing-specific insight we need.
Extracting, transforming and loading marketing data is a unique use case with
very specific requirements. For more, see Is ETL Outsourcing Right for You?
At this point, with an automated solution integrating our marketing data, our
cross-channel visibility is accurate, complete and real-time. We can identify
trends early, recognize mistakes quickly, optimize continuously, and spot
opportunities in time to act on them. What’s more, we’re perfectly positioned
to move on to Stage 3: proactive planning.
8
SALES@BECKON.COMWWW.BECKON.COM
Like Mike, many of us have reached the point where flying our marketing function
blind is not an option. So we dip our toes in the waters of Stage 1 and attempt to
figure out where we’ve been by using data to describe the past. We hire an agen-
cy or analyst to manually pull channel KPIs from disparate sources and merge
them in ways that make cross-channel sense.
It’s absolutely the right idea—we have to extract data from each of our
specialized execution tools and bring it together in a sensible way if we’re to
have visibility into cross-channel performance. But we quickly see a number of
limitations to this manual approach, including:
• It’s labor intensive (in other words, expensive). Merging disparate sets of KPIs
so they make sense requires that we transform the data in some way—that we
add a layer of metadata, tagging and/or formulae so that our underlying data
is associated in a useful, consistent way. For instance, labeling all page views,
retweets and shares as “engagements” lets us associate and compare what
happens on our website with what happens on Twitter. Further, it often makes
sense to claim certain KPIs are worth more than others—that one social media
share is equal to three page views, for instance. Manually performing this kind
of data transformation is hugely labor intensive and can quickly become cost
prohibitive.
• There’s a huge time lag. Extracting data manually simply takes a lot of time.
Yes, our goal in Stage 1 is to use data to describe the past. And as we continue
our journey toward predictive analytics, we’ll see how important historical data
becomes. But if we can only see into an outdated slice of the past—if the data
in our reports is always a quarter, six months or a year old—we risk basing our
decisions on stale information. We need to describe the past consistently and
in detail—and we need our data to be as fresh as possible. The Holy Grail of
marketing calls for access to marketing data in real time, or as near to it as
possible.
• The data is error prone. By definition, manual data transformation means humans
are doing it. And because in a multi-channel arena it’s such a multi-faceted,
complex task, there’s a high likelihood that it will be full of errors.
• The manual effort takes a toll. Often we spend more time cutting, pasting and
transforming our marketing data sets so they’ll work together than we do analyz-
ing the data, gathering insights and reporting conclusions. When our analyst
starts complaining, “This is not what I was hired to do,” we know we’re in trouble.
• Our spreadsheets are “brittle”. Each month, someone on the team has been en-
tering a metric by region, but when we want to see it broken out by customer
segment, we hit a wall. We could go back to the source data, but then we’re
facing the problems above all over again: It’s labor intensive, takes too much
time, results in too many errors and takes a toll on our employees.
But remember, developing a marketing measurement capability with an eye to
predictive analytics is a journey. An integrated view, even if manually provided,
is much better than flying blind. When we integrate marketing data manually,
we can ease many of these pain points by making sure our data is complete
(captures spend and performance KPIs from ALL channels and sources), unified
(lives together in a single repository) and, importantly, properly structured.
Raw marketing data comes to
us relatively unstructured—with-
out the metadata, tagging
and/or formulae that make it
work together. Some of our
marketing spend is in dollars,
some in euros and some in
yen. We’ve got page views, TV
impressions, email opens and
more. If none (or even just
some) of the data is structured
such that it’s associated, our
view of the past is always incomplete. We can only see one slice at a time—the
Euro slice, the AdWords slice, the email slice and so on. Structuring our market-
ing data so that disparate data sets are associated enables apples-to-apples
comparisons.
Structuring our marketing data also means putting it into the language of
business. No CEO or CFO cares about opens, clicks, views or followers—but
they do care about customer engagement. The way we transform (structure
and associate) our data on the way in has everything to do with how much
insight we can extract from it later. If we hope to derive marketing insights from
our marketing data (e.g., use data to describe how we’ve been driving customer
engagement), our structure must have a marketing point of view (i.e., we must
define “engagement” KPIs).
The right marketing data structure is an incredibly strategic decision. Meet
with your CFO to ensure it aligns with the way the business reports results as
well—that way all the dots between marketing activity and business outcomes
are fully connected. If the business reports financials by segment, for example,
make sure your data structure can also describe marketing activities and
outcomes at the segment level.
Keeping all this in mind, your
marketing data structure
should also be flexible—it
should be easy to add channels,
campaigns, segments, regions
and so on as the business
grows and reorganizes. For
a deeper dive, see Marketing
Data Management in the Age
of Integration.
The bottom line is that the business as a whole must decide what goals and
objectives to pursue, then marketing must develop a data structure that
delivers actionable analytics—key diagnostic ratios and aggregate metrics that
track overall marketing performance against business goals. These can include
performance ratios (brand health, paid-to-earned media ratios, engagement
rates, etc.) and efficiency ratios (cost per engagement, ROI and the like). For
an in-depth guide to building a comprehensive, actionable marketing analytics
framework, see The Integrated Marketing Analytics Guidebook: Metrics That
Matter.
Using marketing data to act means we must manage our data with the same
speed and complexity as we execute our campaigns and programs. For
this reason, most marketers come to realize that integrating and managing
data manually—with people power—is untenable in today’s fast-paced,
omni-channel marketing landscape. It’s too complicated and too slow. And, all
too frustrating—turning marketing’s unwieldy data sets into a decision-driving
business asset means repeating the same rote steps week after week, month
after month, year after year. That’s no job for marketers. It’s a job tailor-made
for technology.
In other areas of marketing, automated solutions that handle complex tasks
quickly and accurately have pushed aside manual solutions—automated media
buying and email marketing, for instance, are now ubiquitous at both brands
and agencies. Today, the task of managing and reporting out marketing data
can be automated as well.
The benefits of letting technology do the heavy lifting of integrating and structur-
ing cross-channel marketing data are immense:
• Real-time data for real-time
decisions. Marketing teams
move fast and make decisions
at a breakneck speed. Our
data has to move as fast as
we do. When cross-channel
and cross-platform spend and
performance data at both the
campaign and content level is
delivered daily, we have 365
chances per year to optimize.
• More time for insight and action. Automating our cross-channel marketing data
frees our people from data chaos so they can focus on gathering more insights
and making better decisions.
• Accuracy. No question, automation is more accurate and reliable than a
manual approach.
• Flexibility. Count on the fact that the business questions we need to answer
will always change. Technology lets us pull together metrics at any level of gran-
ularity we can imagine—we can pivot, slice and dice our data instantly from any
angle. Humans working with spreadsheets are simply not as flexible.
To use marketing data to act, it must be accessible, real-time, trustworthy
and accurate. That requires ongoing, consistent ETL—a term familiar to IT
departments, but relatively new to marketers. It stands for extract, transform
and load.
For decades, IT departments—in service to finance, operations and human
resources—have partially or fully automated the task of extracting data from
a number of native tools, transforming that data so it’s all associated, and
loading it back into a single, structured repository for reporting. Thanks to
the recent explosion of available marketing channels, marketing departments
suddenly face an enormous ETL challenge as well—arguably, the most complex
and extensive ETL challenge ever. Traditionally, the business sends IT to
the rescue. But because the ETL process is especially complex for modern
marketers—involving countless KPIs across dozens of channels—old-school,
IT-style ETL typically misses the mark. The reports delivered are too generic,
lacking the marketing-specific insight we need.
Extracting, transforming and loading marketing data is a unique use case with
very specific requirements. For more, see Is ETL Outsourcing Right for You?
At this point, with an automated solution integrating our marketing data, our
cross-channel visibility is accurate, complete and real-time. We can identify
trends early, recognize mistakes quickly, optimize continuously, and spot
opportunities in time to act on them. What’s more, we’re perfectly positioned
to move on to Stage 3: proactive planning.
9
“Too often, teams try to aggregate interesting data sources and see where it takes them. Data scientists and business managers need to define their problem[s] … and desired outcomes.”
—Information Week
SALES@BECKON.COMWWW.BECKON.COM
http://bit.ly/1v5ErdD
http://bit.ly/1svqV1c
Like Mike, many of us have reached the point where flying our marketing function
blind is not an option. So we dip our toes in the waters of Stage 1 and attempt to
figure out where we’ve been by using data to describe the past. We hire an agen-
cy or analyst to manually pull channel KPIs from disparate sources and merge
them in ways that make cross-channel sense.
It’s absolutely the right idea—we have to extract data from each of our
specialized execution tools and bring it together in a sensible way if we’re to
have visibility into cross-channel performance. But we quickly see a number of
limitations to this manual approach, including:
• It’s labor intensive (in other words, expensive). Merging disparate sets of KPIs
so they make sense requires that we transform the data in some way—that we
add a layer of metadata, tagging and/or formulae so that our underlying data
is associated in a useful, consistent way. For instance, labeling all page views,
retweets and shares as “engagements” lets us associate and compare what
happens on our website with what happens on Twitter. Further, it often makes
sense to claim certain KPIs are worth more than others—that one social media
share is equal to three page views, for instance. Manually performing this kind
of data transformation is hugely labor intensive and can quickly become cost
prohibitive.
• There’s a huge time lag. Extracting data manually simply takes a lot of time.
Yes, our goal in Stage 1 is to use data to describe the past. And as we continue
our journey toward predictive analytics, we’ll see how important historical data
becomes. But if we can only see into an outdated slice of the past—if the data
in our reports is always a quarter, six months or a year old—we risk basing our
decisions on stale information. We need to describe the past consistently and
in detail—and we need our data to be as fresh as possible. The Holy Grail of
marketing calls for access to marketing data in real time, or as near to it as
possible.
• The data is error prone. By definition, manual data transformation means humans
are doing it. And because in a multi-channel arena it’s such a multi-faceted,
complex task, there’s a high likelihood that it will be full of errors.
• The manual effort takes a toll. Often we spend more time cutting, pasting and
transforming our marketing data sets so they’ll work together than we do analyz-
ing the data, gathering insights and reporting conclusions. When our analyst
starts complaining, “This is not what I was hired to do,” we know we’re in trouble.
• Our spreadsheets are “brittle”. Each month, someone on the team has been en-
tering a metric by region, but when we want to see it broken out by customer
segment, we hit a wall. We could go back to the source data, but then we’re
facing the problems above all over again: It’s labor intensive, takes too much
time, results in too many errors and takes a toll on our employees.
But remember, developing a marketing measurement capability with an eye to
predictive analytics is a journey. An integrated view, even if manually provided,
is much better than flying blind. When we integrate marketing data manually,
we can ease many of these pain points by making sure our data is complete
(captures spend and performance KPIs from ALL channels and sources), unified
(lives together in a single repository) and, importantly, properly structured.
Raw marketing data comes to
us relatively unstructured—with-
out the metadata, tagging
and/or formulae that make it
work together. Some of our
marketing spend is in dollars,
some in euros and some in
yen. We’ve got page views, TV
impressions, email opens and
more. If none (or even just
some) of the data is structured
such that it’s associated, our
view of the past is always incomplete. We can only see one slice at a time—the
Euro slice, the AdWords slice, the email slice and so on. Structuring our market-
ing data so that disparate data sets are associated enables apples-to-apples
comparisons.
Structuring our marketing data also means putting it into the language of
business. No CEO or CFO cares about opens, clicks, views or followers—but
they do care about customer engagement. The way we transform (structure
and associate) our data on the way in has everything to do with how much
insight we can extract from it later. If we hope to derive marketing insights from
our marketing data (e.g., use data to describe how we’ve been driving customer
engagement), our structure must have a marketing point of view (i.e., we must
define “engagement” KPIs).
The right marketing data structure is an incredibly strategic decision. Meet
with your CFO to ensure it aligns with the way the business reports results as
well—that way all the dots between marketing activity and business outcomes
are fully connected. If the business reports financials by segment, for example,
make sure your data structure can also describe marketing activities and
outcomes at the segment level.
Keeping all this in mind, your
marketing data structure
should also be flexible—it
should be easy to add channels,
campaigns, segments, regions
and so on as the business
grows and reorganizes. For
a deeper dive, see Marketing
Data Management in the Age
of Integration.
The bottom line is that the business as a whole must decide what goals and
objectives to pursue, then marketing must develop a data structure that
delivers actionable analytics—key diagnostic ratios and aggregate metrics that
track overall marketing performance against business goals. These can include
performance ratios (brand health, paid-to-earned media ratios, engagement
rates, etc.) and efficiency ratios (cost per engagement, ROI and the like). For
an in-depth guide to building a comprehensive, actionable marketing analytics
framework, see The Integrated Marketing Analytics Guidebook: Metrics That
Matter.
Using marketing data to act means we must manage our data with the same
speed and complexity as we execute our campaigns and programs. For
this reason, most marketers come to realize that integrating and managing
data manually—with people power—is untenable in today’s fast-paced,
omni-channel marketing landscape. It’s too complicated and too slow. And, all
too frustrating—turning marketing’s unwieldy data sets into a decision-driving
business asset means repeating the same rote steps week after week, month
after month, year after year. That’s no job for marketers. It’s a job tailor-made
for technology.
In other areas of marketing, automated solutions that handle complex tasks
quickly and accurately have pushed aside manual solutions—automated media
buying and email marketing, for instance, are now ubiquitous at both brands
and agencies. Today, the task of managing and reporting out marketing data
can be automated as well.
The benefits of letting technology do the heavy lifting of integrating and structur-
ing cross-channel marketing data are immense:
• Real-time data for real-time
decisions. Marketing teams
move fast and make decisions
at a breakneck speed. Our
data has to move as fast as
we do. When cross-channel
and cross-platform spend and
performance data at both the
campaign and content level is
delivered daily, we have 365
chances per year to optimize.
• More time for insight and action. Automating our cross-channel marketing data
frees our people from data chaos so they can focus on gathering more insights
and making better decisions.
• Accuracy. No question, automation is more accurate and reliable than a
manual approach.
• Flexibility. Count on the fact that the business questions we need to answer
will always change. Technology lets us pull together metrics at any level of gran-
ularity we can imagine—we can pivot, slice and dice our data instantly from any
angle. Humans working with spreadsheets are simply not as flexible.
To use marketing data to act, it must be accessible, real-time, trustworthy
and accurate. That requires ongoing, consistent ETL—a term familiar to IT
departments, but relatively new to marketers. It stands for extract, transform
and load.
For decades, IT departments—in service to finance, operations and human
resources—have partially or fully automated the task of extracting data from
a number of native tools, transforming that data so it’s all associated, and
loading it back into a single, structured repository for reporting. Thanks to
the recent explosion of available marketing channels, marketing departments
suddenly face an enormous ETL challenge as well—arguably, the most complex
and extensive ETL challenge ever. Traditionally, the business sends IT to
the rescue. But because the ETL process is especially complex for modern
marketers—involving countless KPIs across dozens of channels—old-school,
IT-style ETL typically misses the mark. The reports delivered are too generic,
lacking the marketing-specific insight we need.
Extracting, transforming and loading marketing data is a unique use case with
very specific requirements. For more, see Is ETL Outsourcing Right for You?
At this point, with an automated solution integrating our marketing data, our
cross-channel visibility is accurate, complete and real-time. We can identify
trends early, recognize mistakes quickly, optimize continuously, and spot
opportunities in time to act on them. What’s more, we’re perfectly positioned
to move on to Stage 3: proactive planning.
10
“… ’A’ marketers … are better than their colleagues at … alignment, accountability, and analytics [which enable] them to serve as value creators for their organizations.”
—VEM/ITSMA Marketing Performance
Management Survey
SALES@BECKON.COMWWW.BECKON.COM
Using marketing data to act means we must manage our data with the same
speed and complexity as we execute our campaigns and programs. For
this reason, most marketers come to realize that integrating and managing
data manually—with people power—is untenable in today’s fast-paced,
omni-channel marketing landscape. It’s too complicated and too slow. And, all
STAGE 2: AUTOMATED INTEGRATION OF CROSS-CHANNEL DATAUsing data to act
too frustrating—turning marketing’s unwieldy data sets into a decision-driving
business asset means repeating the same rote steps week after week, month
after month, year after year. That’s no job for marketers. It’s a job tailor-made
for technology.
In other areas of marketing, automated solutions that handle complex tasks
quickly and accurately have pushed aside manual solutions—automated media
buying and email marketing, for instance, are now ubiquitous at both brands
and agencies. Today, the task of managing and reporting out marketing data
can be automated as well.
The benefits of letting technology do the heavy lifting of integrating and structur-
ing cross-channel marketing data are immense:
• Real-time data for real-time
decisions. Marketing teams
move fast and make decisions
at a breakneck speed. Our
data has to move as fast as
we do. When cross-channel
and cross-platform spend and
performance data at both the
campaign and content level is
delivered daily, we have 365
chances per year to optimize.
• More time for insight and action. Automating our cross-channel marketing data
frees our people from data chaos so they can focus on gathering more insights
and making better decisions.
• Accuracy. No question, automation is more accurate and reliable than a
manual approach.
• Flexibility. Count on the fact that the business questions we need to answer
will always change. Technology lets us pull together metrics at any level of gran-
ularity we can imagine—we can pivot, slice and dice our data instantly from any
angle. Humans working with spreadsheets are simply not as flexible.
To use marketing data to act, it must be accessible, real-time, trustworthy
and accurate. That requires ongoing, consistent ETL—a term familiar to IT
departments, but relatively new to marketers. It stands for extract, transform
and load.
For decades, IT departments—in service to finance, operations and human
resources—have partially or fully automated the task of extracting data from
a number of native tools, transforming that data so it’s all associated, and
loading it back into a single, structured repository for reporting. Thanks to
the recent explosion of available marketing channels, marketing departments
suddenly face an enormous ETL challenge as well—arguably, the most complex
and extensive ETL challenge ever. Traditionally, the business sends IT to
the rescue. But because the ETL process is especially complex for modern
marketers—involving countless KPIs across dozens of channels—old-school,
IT-style ETL typically misses the mark. The reports delivered are too generic,
lacking the marketing-specific insight we need.
Extracting, transforming and loading marketing data is a unique use case with
very specific requirements. For more, see Is ETL Outsourcing Right for You?
At this point, with an automated solution integrating our marketing data, our
cross-channel visibility is accurate, complete and real-time. We can identify
trends early, recognize mistakes quickly, optimize continuously, and spot
opportunities in time to act on them. What’s more, we’re perfectly positioned
to move on to Stage 3: proactive planning.
11
REAL-TIME DATA, REAL-TIME DECISIONS
One day, the agency told Mike that the resource who owned Mike’s huge (and
computer-crashing) spreadsheet of integrated marketing data had left the firm.
The agency was trying to decipher the spreadsheet but was making
little progress.
Mike decided to stop paying agency man-hours for manual data integration
and invest in automation—a data management and reporting solution that
could aggregate data from all his disparate sources and give him real-time
reports and cross-channel analytics. Now, he had near real-time visibility
into performance across all his channels. And he had a self-serve interface
to answer those ad hoc questions from the CEO in minutes instead of weeks.
There was an adjustment period, to be sure. The numbers that came straight
from the marketing team’s executional systems looked very different than
the manually culled numbers. Sometimes the discrepancies could be traced
to errors in the crazy spreadsheet. Other times they couldn’t be explained.
Using automation to integrate their marketing data meant Mike, his marketing
team and the company executives had to get used to a new “true”. But Mike’s
confidence in his numbers grew, as did his confidence in his decisions, which
he now made more quickly. The CEO and CFO grew more confident as well—in
Mike, his team, and marketing’s overall contribution.
3
SALES@BECKON.COMWWW.BECKON.COM
Using marketing data to act means we must manage our data with the same
speed and complexity as we execute our campaigns and programs. For
this reason, most marketers come to realize that integrating and managing
data manually—with people power—is untenable in today’s fast-paced,
omni-channel marketing landscape. It’s too complicated and too slow. And, all
too frustrating—turning marketing’s unwieldy data sets into a decision-driving
business asset means repeating the same rote steps week after week, month
after month, year after year. That’s no job for marketers. It’s a job tailor-made
for technology.
In other areas of marketing, automated solutions that handle complex tasks
quickly and accurately have pushed aside manual solutions—automated media
buying and email marketing, for instance, are now ubiquitous at both brands
and agencies. Today, the task of managing and reporting out marketing data
can be automated as well.
The benefits of letting technology do the heavy lifting of integrating and structur-
ing cross-channel marketing data are immense:
• Real-time data for real-time
decisions. Marketing teams
move fast and make decisions
at a breakneck speed. Our
data has to move as fast as
we do. When cross-channel
and cross-platform spend and
performance data at both the
campaign and content level is
delivered daily, we have 365
chances per year to optimize.
• More time for insight and action. Automating our cross-channel marketing data
frees our people from data chaos so they can focus on gathering more insights
and making better decisions.
• Accuracy. No question, automation is more accurate and reliable than a
manual approach.
• Flexibility. Count on the fact that the business questions we need to answer
will always change. Technology lets us pull together metrics at any level of gran-
ularity we can imagine—we can pivot, slice and dice our data instantly from any
angle. Humans working with spreadsheets are simply not as flexible.
To use marketing data to act, it must be accessible, real-time, trustworthy
and accurate. That requires ongoing, consistent ETL—a term familiar to IT
departments, but relatively new to marketers. It stands for extract, transform
and load.
For decades, IT departments—in service to finance, operations and human
resources—have partially or fully automated the task of extracting data from
a number of native tools, transforming that data so it’s all associated, and
loading it back into a single, structured repository for reporting. Thanks to
the recent explosion of available marketing channels, marketing departments
suddenly face an enormous ETL challenge as well—arguably, the most complex
and extensive ETL challenge ever. Traditionally, the business sends IT to
the rescue. But because the ETL process is especially complex for modern
marketers—involving countless KPIs across dozens of channels—old-school,
IT-style ETL typically misses the mark. The reports delivered are too generic,
lacking the marketing-specific insight we need.
Extracting, transforming and loading marketing data is a unique use case with
very specific requirements. For more, see Is ETL Outsourcing Right for You?
At this point, with an automated solution integrating our marketing data, our
cross-channel visibility is accurate, complete and real-time. We can identify
trends early, recognize mistakes quickly, optimize continuously, and spot
opportunities in time to act on them. What’s more, we’re perfectly positioned
to move on to Stage 3: proactive planning.
12
45% of executives now view “marketers’ limited competency in data analysis as a major obstacle to implementing more effective strategies.”
—The Economist Intelligence Unit
SALES@BECKON.COMWWW.BECKON.COM
Using marketing data to act means we must manage our data with the same
speed and complexity as we execute our campaigns and programs. For
this reason, most marketers come to realize that integrating and managing
data manually—with people power—is untenable in today’s fast-paced,
omni-channel marketing landscape. It’s too complicated and too slow. And, all
too frustrating—turning marketing’s unwieldy data sets into a decision-driving
business asset means repeating the same rote steps week after week, month
after month, year after year. That’s no job for marketers. It’s a job tailor-made
for technology.
In other areas of marketing, automated solutions that handle complex tasks
quickly and accurately have pushed aside manual solutions—automated media
buying and email marketing, for instance, are now ubiquitous at both brands
and agencies. Today, the task of managing and reporting out marketing data
can be automated as well.
The benefits of letting technology do the heavy lifting of integrating and structur-
ing cross-channel marketing data are immense:
• Real-time data for real-time
decisions. Marketing teams
move fast and make decisions
at a breakneck speed. Our
data has to move as fast as
we do. When cross-channel
and cross-platform spend and
performance data at both the
campaign and content level is
delivered daily, we have 365
chances per year to optimize.
• More time for insight and action. Automating our cross-channel marketing data
frees our people from data chaos so they can focus on gathering more insights
and making better decisions.
• Accuracy. No question, automation is more accurate and reliable than a
manual approach.
• Flexibility. Count on the fact that the business questions we need to answer
will always change. Technology lets us pull together metrics at any level of gran-
ularity we can imagine—we can pivot, slice and dice our data instantly from any
angle. Humans working with spreadsheets are simply not as flexible.
To use marketing data to act, it must be accessible, real-time, trustworthy
and accurate. That requires ongoing, consistent ETL—a term familiar to IT
departments, but relatively new to marketers. It stands for extract, transform
and load.
For decades, IT departments—in service to finance, operations and human
resources—have partially or fully automated the task of extracting data from
a number of native tools, transforming that data so it’s all associated, and
loading it back into a single, structured repository for reporting. Thanks to
the recent explosion of available marketing channels, marketing departments
suddenly face an enormous ETL challenge as well—arguably, the most complex
and extensive ETL challenge ever. Traditionally, the business sends IT to
the rescue. But because the ETL process is especially complex for modern
marketers—involving countless KPIs across dozens of channels—old-school,
IT-style ETL typically misses the mark. The reports delivered are too generic,
lacking the marketing-specific insight we need.
Extracting, transforming and loading marketing data is a unique use case with
very specific requirements. For more, see Is ETL Outsourcing Right for You?
At this point, with an automated solution integrating our marketing data, our
cross-channel visibility is accurate, complete and real-time. We can identify
trends early, recognize mistakes quickly, optimize continuously, and spot
opportunities in time to act on them. What’s more, we’re perfectly positioned
to move on to Stage 3: proactive planning.
http://bit.ly/1pRj6TB
13
SALES@BECKON.COMWWW.BECKON.COM
Using marketing data to act means we must manage our data with the same
speed and complexity as we execute our campaigns and programs. For
this reason, most marketers come to realize that integrating and managing
data manually—with people power—is untenable in today’s fast-paced,
omni-channel marketing landscape. It’s too complicated and too slow. And, all
too frustrating—turning marketing’s unwieldy data sets into a decision-driving
business asset means repeating the same rote steps week after week, month
after month, year after year. That’s no job for marketers. It’s a job tailor-made
for technology.
In other areas of marketing, automated solutions that handle complex tasks
quickly and accurately have pushed aside manual solutions—automated media
buying and email marketing, for instance, are now ubiquitous at both brands
and agencies. Today, the task of managing and reporting out marketing data
can be automated as well.
The benefits of letting technology do the heavy lifting of integrating and structur-
ing cross-channel marketing data are immense:
• Real-time data for real-time
decisions. Marketing teams
move fast and make decisions
at a breakneck speed. Our
data has to move as fast as
we do. When cross-channel
and cross-platform spend and
performance data at both the
campaign and content level is
delivered daily, we have 365
chances per year to optimize.
• More time for insight and action. Automating our cross-channel marketing data
frees our people from data chaos so they can focus on gathering more insights
and making better decisions.
• Accuracy. No question, automation is more accurate and reliable than a
manual approach.
• Flexibility. Count on the fact that the business questions we need to answer
will always change. Technology lets us pull together metrics at any level of gran-
ularity we can imagine—we can pivot, slice and dice our data instantly from any
angle. Humans working with spreadsheets are simply not as flexible.
STAGE 3: PROACTIVE PLANNINGUsing data to plan
Once our data is structured, and flowing reliably and consistently enough to be
trustworthy, we can use variances to plan and act. Here’s how it works:
1 Know the baselines. A baseline is an historical steady state—what things were
like before we began a particular campaign or initiative. To determine the true
effect of a TV campaign on in-store sales, for example, we need to know the
state of in-store sales before the TV spots were running.
2 Set targets. Once we under-
stand baselines, our targets
will represent the lift above
baseline that we expect to
achieve given additional
investments or marketing
efforts. All our efforts should
have an objective or general
intention, but when we use
data to plan, we turn those
intentions into quantifiable
targets. Setting targets means
being able to say, for instance,
that we intend to increase sales by 15%, bring awareness costs down by 10% or
increase engagements by 25%. Without robust and accurate data to serve as
a trustworthy baseline, a target is relatively meaningless because it’s random.
And no one wants to be held accountable for something random.
3 Track variances. Once we’ve set targets and captured some actual performance
data, then we have variances. Thinking like a CFO, we can track variances each
day—comparing planned and actual numbers—and use them to set our agenda
and decide what to do next. If we’re 90% of the way through a campaign, for
example, but have only reached 10% of our goal, we can change the mix and
move money around on the fly. In short, we can act proactively to close the gap
instead of waiting for the end of the campaign to realize, “Darn, we missed our
target. We’ll do better next year.”
4 Distribute shared reports and dashboards. Automated, integrated reporting
and shared dashboards are critical tools for using data to plan and make
variance-based decisions. We need to collaborate around the data as a team,
make shared decisions every day, and readily communicate—and defend—the
strategies and action plans we propose.
Once we’ve mastered using data to view the past, act and plan, we’re ready to go
for the Holy Grail of marketing measurement: using marketing data to predict.
To use marketing data to act, it must be accessible, real-time, trustworthy
and accurate. That requires ongoing, consistent ETL—a term familiar to IT
departments, but relatively new to marketers. It stands for extract, transform
and load.
For decades, IT departments—in service to finance, operations and human
resources—have partially or fully automated the task of extracting data from
a number of native tools, transforming that data so it’s all associated, and
loading it back into a single, structured repository for reporting. Thanks to
the recent explosion of available marketing channels, marketing departments
suddenly face an enormous ETL challenge as well—arguably, the most complex
and extensive ETL challenge ever. Traditionally, the business sends IT to
the rescue. But because the ETL process is especially complex for modern
marketers—involving countless KPIs across dozens of channels—old-school,
IT-style ETL typically misses the mark. The reports delivered are too generic,
lacking the marketing-specific insight we need.
Extracting, transforming and loading marketing data is a unique use case with
very specific requirements. For more, see Is ETL Outsourcing Right for You?
At this point, with an automated solution integrating our marketing data, our
cross-channel visibility is accurate, complete and real-time. We can identify
trends early, recognize mistakes quickly, optimize continuously, and spot
opportunities in time to act on them. What’s more, we’re perfectly positioned
to move on to Stage 3: proactive planning.
14
AT LAST, MARKETING AGILITY
All of Mike’s multi-channel marketing data had been flowing automatically
into a single data warehouse, and he’d been monitoring performance
daily for several months. While Mike and his team were thrilled with their
ability (finally!) to accurately describe what they’d been contributing to the
business, and to take action based on real-time data, now they wanted to go
further—start using marketing data to look forward.
Mike had always wanted to kick off a campaign with a clear target and manage
to that target in real time, but 1) he’d never had access to a real-time feedback
loop, and 2) there was no historical data or trusted baseline on which to even
set targets. Now he had both.
So, for the big fall push, Mike and his team looked at data from the last several
campaigns, including last year’s holiday campaign, and set a goal. They looked
at reports daily and could see how they were tracking to their goal. On one
campaign, they were 90% of the way to their target just halfway through the
campaign—that was on track to be a strong performer. But another effort with
a key retail partner had just thee weeks left and was only 15% to goal. Mike
was able to pull resources from the successful campaign and put them into the
retailer partnership. Everyone mobilized to close the gap as fast as possible,
and they hit the target. His team was working in an integrated way and making
spend decisions based on real-time performance data.
4
SALES@BECKON.COMWWW.BECKON.COM
Using marketing data to act means we must manage our data with the same
speed and complexity as we execute our campaigns and programs. For
this reason, most marketers come to realize that integrating and managing
data manually—with people power—is untenable in today’s fast-paced,
omni-channel marketing landscape. It’s too complicated and too slow. And, all
too frustrating—turning marketing’s unwieldy data sets into a decision-driving
business asset means repeating the same rote steps week after week, month
after month, year after year. That’s no job for marketers. It’s a job tailor-made
for technology.
In other areas of marketing, automated solutions that handle complex tasks
quickly and accurately have pushed aside manual solutions—automated media
buying and email marketing, for instance, are now ubiquitous at both brands
and agencies. Today, the task of managing and reporting out marketing data
can be automated as well.
The benefits of letting technology do the heavy lifting of integrating and structur-
ing cross-channel marketing data are immense:
• Real-time data for real-time
decisions. Marketing teams
move fast and make decisions
at a breakneck speed. Our
data has to move as fast as
we do. When cross-channel
and cross-platform spend and
performance data at both the
campaign and content level is
delivered daily, we have 365
chances per year to optimize.
• More time for insight and action. Automating our cross-channel marketing data
frees our people from data chaos so they can focus on gathering more insights
and making better decisions.
• Accuracy. No question, automation is more accurate and reliable than a
manual approach.
• Flexibility. Count on the fact that the business questions we need to answer
will always change. Technology lets us pull together metrics at any level of gran-
ularity we can imagine—we can pivot, slice and dice our data instantly from any
angle. Humans working with spreadsheets are simply not as flexible.
Once our data is structured, and flowing reliably and consistently enough to be
trustworthy, we can use variances to plan and act. Here’s how it works:
1 Know the baselines. A baseline is an historical steady state—what things were
like before we began a particular campaign or initiative. To determine the true
effect of a TV campaign on in-store sales, for example, we need to know the
state of in-store sales before the TV spots were running.
2 Set targets. Once we under-
stand baselines, our targets
will represent the lift above
baseline that we expect to
achieve given additional
investments or marketing
efforts. All our efforts should
have an objective or general
intention, but when we use
data to plan, we turn those
intentions into quantifiable
targets. Setting targets means
being able to say, for instance,
that we intend to increase sales by 15%, bring awareness costs down by 10% or
increase engagements by 25%. Without robust and accurate data to serve as
a trustworthy baseline, a target is relatively meaningless because it’s random.
And no one wants to be held accountable for something random.
3 Track variances. Once we’ve set targets and captured some actual performance
data, then we have variances. Thinking like a CFO, we can track variances each
day—comparing planned and actual numbers—and use them to set our agenda
and decide what to do next. If we’re 90% of the way through a campaign, for
example, but have only reached 10% of our goal, we can change the mix and
move money around on the fly. In short, we can act proactively to close the gap
instead of waiting for the end of the campaign to realize, “Darn, we missed our
target. We’ll do better next year.”
4 Distribute shared reports and dashboards. Automated, integrated reporting
and shared dashboards are critical tools for using data to plan and make
variance-based decisions. We need to collaborate around the data as a team,
make shared decisions every day, and readily communicate—and defend—the
strategies and action plans we propose.
Once we’ve mastered using data to view the past, act and plan, we’re ready to go
for the Holy Grail of marketing measurement: using marketing data to predict.
To use marketing data to act, it must be accessible, real-time, trustworthy
and accurate. That requires ongoing, consistent ETL—a term familiar to IT
departments, but relatively new to marketers. It stands for extract, transform
and load.
For decades, IT departments—in service to finance, operations and human
resources—have partially or fully automated the task of extracting data from
a number of native tools, transforming that data so it’s all associated, and
loading it back into a single, structured repository for reporting. Thanks to
the recent explosion of available marketing channels, marketing departments
suddenly face an enormous ETL challenge as well—arguably, the most complex
and extensive ETL challenge ever. Traditionally, the business sends IT to
the rescue. But because the ETL process is especially complex for modern
marketers—involving countless KPIs across dozens of channels—old-school,
IT-style ETL typically misses the mark. The reports delivered are too generic,
lacking the marketing-specific insight we need.
Extracting, transforming and loading marketing data is a unique use case with
very specific requirements. For more, see Is ETL Outsourcing Right for You?
At this point, with an automated solution integrating our marketing data, our
cross-channel visibility is accurate, complete and real-time. We can identify
trends early, recognize mistakes quickly, optimize continuously, and spot
opportunities in time to act on them. What’s more, we’re perfectly positioned
to move on to Stage 3: proactive planning.
15
68% of marketers say there is more pressure to show ROI on spend, and 75% say it’s their greatest concern, yet 56% say we’re unprepared for ROI accountability.
—IBM Global CMO Study
SALES@BECKON.COMWWW.BECKON.COM
Using marketing data to act means we must manage our data with the same
speed and complexity as we execute our campaigns and programs. For
this reason, most marketers come to realize that integrating and managing
data manually—with people power—is untenable in today’s fast-paced,
omni-channel marketing landscape. It’s too complicated and too slow. And, all
too frustrating—turning marketing’s unwieldy data sets into a decision-driving
business asset means repeating the same rote steps week after week, month
after month, year after year. That’s no job for marketers. It’s a job tailor-made
for technology.
In other areas of marketing, automated solutions that handle complex tasks
quickly and accurately have pushed aside manual solutions—automated media
buying and email marketing, for instance, are now ubiquitous at both brands
and agencies. Today, the task of managing and reporting out marketing data
can be automated as well.
The benefits of letting technology do the heavy lifting of integrating and structur-
ing cross-channel marketing data are immense:
• Real-time data for real-time
decisions. Marketing teams
move fast and make decisions
at a breakneck speed. Our
data has to move as fast as
we do. When cross-channel
and cross-platform spend and
performance data at both the
campaign and content level is
delivered daily, we have 365
chances per year to optimize.
• More time for insight and action. Automating our cross-channel marketing data
frees our people from data chaos so they can focus on gathering more insights
and making better decisions.
• Accuracy. No question, automation is more accurate and reliable than a
manual approach.
• Flexibility. Count on the fact that the business questions we need to answer
will always change. Technology lets us pull together metrics at any level of gran-
ularity we can imagine—we can pivot, slice and dice our data instantly from any
angle. Humans working with spreadsheets are simply not as flexible.
To use marketing data to act, it must be accessible, real-time, trustworthy
and accurate. That requires ongoing, consistent ETL—a term familiar to IT
departments, but relatively new to marketers. It stands for extract, transform
and load.
For decades, IT departments—in service to finance, operations and human
resources—have partially or fully automated the task of extracting data from
a number of native tools, transforming that data so it’s all associated, and
loading it back into a single, structured repository for reporting. Thanks to
the recent explosion of available marketing channels, marketing departments
suddenly face an enormous ETL challenge as well—arguably, the most complex
and extensive ETL challenge ever. Traditionally, the business sends IT to
the rescue. But because the ETL process is especially complex for modern
marketers—involving countless KPIs across dozens of channels—old-school,
IT-style ETL typically misses the mark. The reports delivered are too generic,
lacking the marketing-specific insight we need.
Extracting, transforming and loading marketing data is a unique use case with
very specific requirements. For more, see Is ETL Outsourcing Right for You?
At this point, with an automated solution integrating our marketing data, our
cross-channel visibility is accurate, complete and real-time. We can identify
trends early, recognize mistakes quickly, optimize continuously, and spot
opportunities in time to act on them. What’s more, we’re perfectly positioned
to move on to Stage 3: proactive planning.
16
You’re almost there—on the doorstep of predictive analytics. You have an
integrated view of the past and you can use data to act and plan. Your data
house is now in order—and just in time. According to the Accenture Analytics
in Action survey of 600 business executives, the use of forward-looking data
analysis has tripled since 2009.
STAGE 4: DATA MODELINGUsing data to predict
Predictive modeling is
forward-looking—the process
of determining the most likely
outcome based on historical
data sets. It’s the ability to say,
“If we do X, Y will likely happen.”
For marketers, that translates
into knowing, for instance, that
increasing our paid search
spend by X will likely increase
organic search traffic by Y. Or that, yes, out-of-home advertising in San Francisco
will make all the direct marketing tactics in the region more effective and drive
up regional sales by 16%.
The more data points we have to extrapolate from, the better our predictive
ability will be. Say we spent $5M on a back to school initiative in 2013 and got
$25M in sales. If that’s the only data we have, it will be hard to predict with any
confidence what will happen to 2014 back to school results if we increase spend
to $6M. But if we have back to school spend and results for 2011, 2012 and 2013,
our predictions for 2014 will be much more accurate.
Predictive analytics requires
that we 1) have a solid history
of having “done X”, and 2) have
accurately recorded all the “Y”
values that resulted. We need
to have been tracking long
enough to have confidence in
the data models we generate.
Remember, we have to flip a
coin many times before we see
that heads and tails eventually
come up evenly. If we only observe a few flips, we might conclude that tails
comes up twice as often as heads. When it comes to using our marketing data
to predict the results of our actions, the same principle applies. The longer we
do it and the more structure we bring to tracking and measuring over time, the
more accurate and meaningful our insights will be.
GO FORTH AND MEASURE
At the moment, predictive analytics is the shining goal, the Holy Grail, for many
marketers. It’s easy to see why.
But many of us (if we’re honest) are still flying blind, with data locked away
in disparate silos. Or we’re nobly, but manually, trying to cobble together an
integrated picture, even though the number of man-hours this takes ironically
prevents us from telling marketing’s story well. Consider that only 25% of mar-
keters can answer the question, “What is marketing’s impact on the business?”
according to the VEM/ITSMA Marketing Performance Management Survey.
The bottom line is that marketers
face ever more pressure to
quantify their contribution to the
business. Cultivating a marketing
measurement capability that’s
best in class enables us to answer
the call.
But we can’t install that capability
overnight. It requires a commitment to a process. A process of gathering our
data and structuring it so that it’s associated and aligned with the business. It
means gathering data consistently over time so it becomes trustworthy. Only
then can we even begin to think about the marketing Holy Grail—reliably
predicting the impact of our various marketing actions. But the good news is, it’s
there for us—any of us—if we want it. All we have to do is reach for it.
BONA FIDE DATA-DRIVEN MARKETING
Under Mike’s leadership, the sports equipment retailer’s multi-channel
marketing practice was top notch. No question, they were a data-driven
team. The reporting they provided to the C-suite was no longer limited to
just campaign or program performance, no longer full of likes and clicks, but
full of insightful dashboards showing how efficiently and effectively Mike’s
team had been using its budget in a complex, multi-channel environment to
drive customers through the purchase funnel. They’d been capturing data
reliably and consistently for nearly two years. Mike could now say things like,
“If we need a bump in sales before the end of the quarter, referral site banner
ads offering promotional discounts are the most effective and efficient way
to drive e-commerce sales,” and be confident it was true—it was based on a
significant amount of accurate, reliable data.
Mike’s success over the past two years resulted in larger and larger
budgets—he’d saved the retailer a lot of wasted spend through ongoing
optimization, plus he’d built enough trust in the C-suite that they granted his
requests for more money. With robust, historical data sets documenting both
spend and business outcomes, it was increasingly easy and straightforward to
predict business outcomes based on various levels of marketing spend—and
a far cry from the early “flying blind” days.
5
SALES@BECKON.COMWWW.BECKON.COM
Using marketing data to act means we must manage our data with the same
speed and complexity as we execute our campaigns and programs. For
this reason, most marketers come to realize that integrating and managing
data manually—with people power—is untenable in today’s fast-paced,
omni-channel marketing landscape. It’s too complicated and too slow. And, all
too frustrating—turning marketing’s unwieldy data sets into a decision-driving
business asset means repeating the same rote steps week after week, month
after month, year after year. That’s no job for marketers. It’s a job tailor-made
for technology.
In other areas of marketing, automated solutions that handle complex tasks
quickly and accurately have pushed aside manual solutions—automated media
buying and email marketing, for instance, are now ubiquitous at both brands
and agencies. Today, the task of managing and reporting out marketing data
can be automated as well.
The benefits of letting technology do the heavy lifting of integrating and structur-
ing cross-channel marketing data are immense:
• Real-time data for real-time
decisions. Marketing teams
move fast and make decisions
at a breakneck speed. Our
data has to move as fast as
we do. When cross-channel
and cross-platform spend and
performance data at both the
campaign and content level is
delivered daily, we have 365
chances per year to optimize.
• More time for insight and action. Automating our cross-channel marketing data
frees our people from data chaos so they can focus on gathering more insights
and making better decisions.
• Accuracy. No question, automation is more accurate and reliable than a
manual approach.
• Flexibility. Count on the fact that the business questions we need to answer
will always change. Technology lets us pull together metrics at any level of gran-
ularity we can imagine—we can pivot, slice and dice our data instantly from any
angle. Humans working with spreadsheets are simply not as flexible.
To use marketing data to act, it must be accessible, real-time, trustworthy
and accurate. That requires ongoing, consistent ETL—a term familiar to IT
departments, but relatively new to marketers. It stands for extract, transform
and load.
For decades, IT departments—in service to finance, operations and human
resources—have partially or fully automated the task of extracting data from
a number of native tools, transforming that data so it’s all associated, and
loading it back into a single, structured repository for reporting. Thanks to
the recent explosion of available marketing channels, marketing departments
suddenly face an enormous ETL challenge as well—arguably, the most complex
and extensive ETL challenge ever. Traditionally, the business sends IT to
the rescue. But because the ETL process is especially complex for modern
marketers—involving countless KPIs across dozens of channels—old-school,
IT-style ETL typically misses the mark. The reports delivered are too generic,
lacking the marketing-specific insight we need.
Extracting, transforming and loading marketing data is a unique use case with
very specific requirements. For more, see Is ETL Outsourcing Right for You?
At this point, with an automated solution integrating our marketing data, our
cross-channel visibility is accurate, complete and real-time. We can identify
trends early, recognize mistakes quickly, optimize continuously, and spot
opportunities in time to act on them. What’s more, we’re perfectly positioned
to move on to Stage 3: proactive planning.
17
You’re almost there—on the doorstep of predictive analytics. You have an
integrated view of the past and you can use data to act and plan. Your data
house is now in order—and just in time. According to the Accenture Analytics
in Action survey of 600 business executives, the use of forward-looking data
analysis has tripled since 2009.
Predictive modeling is
forward-looking—the process
of determining the most likely
outcome based on historical
data sets. It’s the ability to say,
“If we do X, Y will likely happen.”
For marketers, that translates
into knowing, for instance, that
increasing our paid search
spend by X will likely increase
organic search traffic by Y. Or that, yes, out-of-home advertising in San Francisco
will make all the direct marketing tactics in the region more effective and drive
up regional sales by 16%.
The more data points we have to extrapolate from, the better our predictive
ability will be. Say we spent $5M on a back to school initiative in 2013 and got
$25M in sales. If that’s the only data we have, it will be hard to predict with any
confidence what will happen to 2014 back to school results if we increase spend
to $6M. But if we have back to school spend and results for 2011, 2012 and 2013,
our predictions for 2014 will be much more accurate.
Predictive analytics requires
that we 1) have a solid history
of having “done X”, and 2) have
accurately recorded all the “Y”
values that resulted. We need
to have been tracking long
enough to have confidence in
the data models we generate.
Remember, we have to flip a
coin many times before we see
that heads and tails eventually
come up evenly. If we only observe a few flips, we might conclude that tails
comes up twice as often as heads. When it comes to using our marketing data
to predict the results of our actions, the same principle applies. The longer we
do it and the more structure we bring to tracking and measuring over time, the
more accurate and meaningful our insights will be.
GO FORTH AND MEASURE
At the moment, predictive analytics is the shining goal, the Holy Grail, for many
marketers. It’s easy to see why.
But many of us (if we’re honest) are still flying blind, with data locked away
in disparate silos. Or we’re nobly, but manually, trying to cobble together an
integrated picture, even though the number of man-hours this takes ironically
prevents us from telling marketing’s story well. Consider that only 25% of mar-
keters can answer the question, “What is marketing’s impact on the business?”
according to the VEM/ITSMA Marketing Performance Management Survey.
The bottom line is that marketers
face ever more pressure to
quantify their contribution to the
business. Cultivating a marketing
measurement capability that’s
best in class enables us to answer
the call.
But we can’t install that capability
overnight. It requires a commitment to a process. A process of gathering our
data and structuring it so that it’s associated and aligned with the business. It
means gathering data consistently over time so it becomes trustworthy. Only
then can we even begin to think about the marketing Holy Grail—reliably
predicting the impact of our various marketing actions. But the good news is, it’s
there for us—any of us—if we want it. All we have to do is reach for it.
“Forward-looking companies are using predictive analytics across a range of disparate data types to achieve greater value.”
—Information Week
37% of marketers say the “ability to use data analysis to extract predictive findings from Big Data is our highest priority.” Five years ago, it was just 17%.
—The Economist Intelligence Unit
SALES@BECKON.COMWWW.BECKON.COM
Using marketing data to act means we must manage our data with the same
speed and complexity as we execute our campaigns and programs. For
this reason, most marketers come to realize that integrating and managing
data manually—with people power—is untenable in today’s fast-paced,
omni-channel marketing landscape. It’s too complicated and too slow. And, all
too frustrating—turning marketing’s unwieldy data sets into a decision-driving
business asset means repeating the same rote steps week after week, month
after month, year after year. That’s no job for marketers. It’s a job tailor-made
for technology.
In other areas of marketing, automated solutions that handle complex tasks
quickly and accurately have pushed aside manual solutions—automated media
buying and email marketing, for instance, are now ubiquitous at both brands
and agencies. Today, the task of managing and reporting out marketing data
can be automated as well.
The benefits of letting technology do the heavy lifting of integrating and structur-
ing cross-channel marketing data are immense:
• Real-time data for real-time
decisions. Marketing teams
move fast and make decisions
at a breakneck speed. Our
data has to move as fast as
we do. When cross-channel
and cross-platform spend and
performance data at both the
campaign and content level is
delivered daily, we have 365
chances per year to optimize.
• More time for insight and action. Automating our cross-channel marketing data
frees our people from data chaos so they can focus on gathering more insights
and making better decisions.
• Accuracy. No question, automation is more accurate and reliable than a
manual approach.
• Flexibility. Count on the fact that the business questions we need to answer
will always change. Technology lets us pull together metrics at any level of gran-
ularity we can imagine—we can pivot, slice and dice our data instantly from any
angle. Humans working with spreadsheets are simply not as flexible.
To use marketing data to act, it must be accessible, real-time, trustworthy
and accurate. That requires ongoing, consistent ETL—a term familiar to IT
departments, but relatively new to marketers. It stands for extract, transform
and load.
For decades, IT departments—in service to finance, operations and human
resources—have partially or fully automated the task of extracting data from
a number of native tools, transforming that data so it’s all associated, and
loading it back into a single, structured repository for reporting. Thanks to
the recent explosion of available marketing channels, marketing departments
suddenly face an enormous ETL challenge as well—arguably, the most complex
and extensive ETL challenge ever. Traditionally, the business sends IT to
the rescue. But because the ETL process is especially complex for modern
marketers—involving countless KPIs across dozens of channels—old-school,
IT-style ETL typically misses the mark. The reports delivered are too generic,
lacking the marketing-specific insight we need.
Extracting, transforming and loading marketing data is a unique use case with
very specific requirements. For more, see Is ETL Outsourcing Right for You?
At this point, with an automated solution integrating our marketing data, our
cross-channel visibility is accurate, complete and real-time. We can identify
trends early, recognize mistakes quickly, optimize continuously, and spot
opportunities in time to act on them. What’s more, we’re perfectly positioned
to move on to Stage 3: proactive planning.
18
You’re almost there—on the doorstep of predictive analytics. You have an
integrated view of the past and you can use data to act and plan. Your data
house is now in order—and just in time. According to the Accenture Analytics
in Action survey of 600 business executives, the use of forward-looking data
analysis has tripled since 2009.
Predictive modeling is
forward-looking—the process
of determining the most likely
outcome based on historical
data sets. It’s the ability to say,
“If we do X, Y will likely happen.”
For marketers, that translates
into knowing, for instance, that
increasing our paid search
spend by X will likely increase
organic search traffic by Y. Or that, yes, out-of-home advertising in San Francisco
will make all the direct marketing tactics in the region more effective and drive
up regional sales by 16%.
The more data points we have to extrapolate from, the better our predictive
ability will be. Say we spent $5M on a back to school initiative in 2013 and got
$25M in sales. If that’s the only data we have, it will be hard to predict with any
confidence what will happen to 2014 back to school results if we increase spend
to $6M. But if we have back to school spend and results for 2011, 2012 and 2013,
our predictions for 2014 will be much more accurate.
Predictive analytics requires
that we 1) have a solid history
of having “done X”, and 2) have
accurately recorded all the “Y”
values that resulted. We need
to have been tracking long
enough to have confidence in
the data models we generate.
Remember, we have to flip a
coin many times before we see
that heads and tails eventually
come up evenly. If we only observe a few flips, we might conclude that tails
comes up twice as often as heads. When it comes to using our marketing data
to predict the results of our actions, the same principle applies. The longer we
do it and the more structure we bring to tracking and measuring over time, the
more accurate and meaningful our insights will be.
GO FORTH AND MEASURE
At the moment, predictive analytics is the shining goal, the Holy Grail, for many
marketers. It’s easy to see why.
But many of us (if we’re honest) are still flying blind, with data locked away
in disparate silos. Or we’re nobly, but manually, trying to cobble together an
integrated picture, even though the number of man-hours this takes ironically
prevents us from telling marketing’s story well. Consider that only 25% of mar-
keters can answer the question, “What is marketing’s impact on the business?”
according to the VEM/ITSMA Marketing Performance Management Survey.
The bottom line is that marketers
face ever more pressure to
quantify their contribution to the
business. Cultivating a marketing
measurement capability that’s
best in class enables us to answer
the call.
But we can’t install that capability
overnight. It requires a commitment to a process. A process of gathering our
data and structuring it so that it’s associated and aligned with the business. It
means gathering data consistently over time so it becomes trustworthy. Only
then can we even begin to think about the marketing Holy Grail—reliably
predicting the impact of our various marketing actions. But the good news is, it’s
there for us—any of us—if we want it. All we have to do is reach for it.
85% of marketers see a future of only more pressure to describe marketing’s value and contribution to the business.
—VEM/ITSMA Marketing Performance
Management Survey
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RESOURCES
1 MIND THE MARKETING GAP, The Economist Intelligence Unit
2 YOUR COMPANY CAN SEE THE FUTURE WITH PREDICTIVE ANALYTICS,
Forbes
3 ANALYTICS AND ACTION: BREAKTHROUGHS AND BARRIERS ON THE
JOURNEY TO ROI, Accenture
4 MARKETING PERFORMANCE MANAGEMENT SURVEY, VEM/ITSMA
5 STUDY REVEALS WIDESPREAD DIGITAL DYSFUNCTION AMONG
MARKETERS, Domus with Harris Interactive
6 DIGITIZING THE CONSUMER DECISION JOURNEY, McKinsey & Company
7 FROM STRETCHED TO STRENGTHENED, 2014 IBM Global CMO Study
8 ANALYTICS THE MOST DESIRABLE AND LARGEST TALENT GAP FOR 2014,
The Future Buzz
9 MARKETER’S GUIDE TO ACTIONABLE DATA, MarketingProfs
19
SALES@BECKON.COMWWW.BECKON.COM
http://bit.ly/1nNDaL3
http://bit.ly/18s3DWS
1 MIND THE MARKETING GAP, The Economist Intelligence Unit
2 YOUR COMPANY CAN SEE THE FUTURE WITH PREDICTIVE ANALYTICS,
Forbes
3 ANALYTICS AND ACTION: BREAKTHROUGHS AND BARRIERS ON THE
JOURNEY TO ROI, Accenture
4 MARKETING PERFORMANCE MANAGEMENT SURVEY, VEM/ITSMA
5 STUDY REVEALS WIDESPREAD DIGITAL DYSFUNCTION AMONG
MARKETERS, Domus with Harris Interactive
6 DIGITIZING THE CONSUMER DECISION JOURNEY, McKinsey & Company
7 FROM STRETCHED TO STRENGTHENED, 2014 IBM Global CMO Study
8 ANALYTICS THE MOST DESIRABLE AND LARGEST TALENT GAP FOR 2014,
The Future Buzz
9 MARKETER’S GUIDE TO ACTIONABLE DATA, MarketingProfs
20
SALES@BECKON.COMWWW.BECKON.COM
ABOUT BECKON
Beckon is omni-channel analytics software for marketing in all its modern
complexity. Our software-as-a-service platform integrates messy marketing
data and delivers rich dashboards for cross-channel marketing intelligence.
Built by marketers for marketers, Beckon is the dashboard to the
CMO—industry 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
217 SOUTH B STREET, SUITE 4
SAN MATEO, CA 94401
21
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