HOW TO GO FROM MARKETING DATA CHAOS TO PREDICTIVE ANALYTICS

THE MARKETING MEASUREMENT JOURNEY

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CONTENTS

INTRODUCTION 3

STAGE1:DISPARATEREPORTS 6

STAGE2:MANUALINTEGRATEDREPORTING 8

STAGE3:AUTOMATEDINTEGRATEDDASHBOARDS 12

STAGE4:PROACTIVEPLANNING 15

STAGE5:MODELING 18

RESOURCES 21

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INTRODUCTION

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.

64% of marketers claim their companies

suffer from “digital dysfunction”—

uncertainty about how to integrate digital

strategies into the marketing mix.—Domus with Harris Interactive

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

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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 five stages we must go through to develop an analytics

framework that measures and predicts marketing’s impact on the business.

STAGE 1: DISPARATE REPORTSNO INTEGRATED VIEW—FLYING BLIND

STAGE 2: MANUAL INTEGRATED REPORTINGUSING DATA TO DESCRIBE THE PAST

STAGE 3: AUTOMATED INTEGRATED DASHBOARDSUSING DATA TO ACT

STAGE 4: PROACTIVE PLANNINGUSING DATA TO PLAN AND EXPERIMENT

STAGE 5: MODELINGUSING DATA TO PREDICT

1DISPARATE REPORTS

2MANUAL

INTEGRATED REPORTING

3AUTOMATED INTEGRATED

DASHBOARDS

4PROACTIVE PLANNING

5MODELING

No integrated view. “I’m flying blind!”

Use data to describe the past

Use datato act

Use data to plan, experiment

Use datato predict

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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 1, who have yet to embark on any measurement

journey at all.

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STAGE 1: DISPARATE REPORTS

No integrated view—flying blind

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.

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Stage 1 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.

Companies must develop ‘empirically-based

engagement strategies’—strategies informed

by data around past customer experiences

and behaviors.— Digitizing the Consumer Decision Journey,

McKinsey & Company

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 2.

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STAGE 2: MANUAL INTEGRATED

REPORTING

Using data to describe the past

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.

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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 2 and

attempt to figure out where we’ve been by using data to describe the past. We

hire an agency 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 2 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.

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• 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.

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

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.

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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.

… ‘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

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.

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STAGE 3: AUTOMATED INTEGRATED

DASHBOARDS

Using data to act

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.

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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:

45% of executives now view

‘marketers’ limited competency in data

analysis as a major obstacle to implementing

more effective strategies.’—The Economist Intelligence Unit

• 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.

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• 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 4: proactive planning.

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STAGE 4: PROACTIVE PLANNING

Using data to plan and experiment

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.

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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 understand 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.

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

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.”

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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.

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STAGE 5: MODELING

Using data to predict

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.

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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

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

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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.

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

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.

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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

ABOUTBECKON

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