The Marketing Measurement Journey White Paper by BECKON

HOW TO GO FROM MARKETING DATA CHAOS

TO PREDICTIVE ANALYTICS

THE MARKETING

MEASUREMENT JOURNEY

THE MARKETING

MEASUREMENT JOURNEY

[email protected]

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

Bonnie Thomas

STAGE 1: Disparate reports

Bonnie Thomas

STAGE 2: Manual Integrated Reporting

Bonnie Thomas

STAGE 3: Automated Integrated Dashboards

Bonnie Thomas

STAGE 4: Proactive Planning

Bonnie Thomas

STAGE 5: Modeling

[email protected]

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

[email protected]


























maturity. What we can do is understand where on that journey our organization



best in class.













journey at all.

4

Bonnie Thomas

STAGE 1: DISPARATE REPORTSNo integrated view—flying blind

Bonnie Thomas

Text

Bonnie Thomas

STAGE 2: MANUAL INTEGRATED REPORTING

Bonnie Thomas

STAGE 3: AUTOMATEDINTEGRATED DASHBOARDS

Bonnie Thomas

STAGE 4: PROACTIVE PLANNINGusing data to plan and experiment

Bonnie Thomas

STAGE 2

Bonnie Thomas

STAGE 5: MODELING

Bonnie Thomas

INSERT CHART HERE

Bonnie Thomas

describes the five stages

Bonnie Thomas

those of us at Stage 1

Bonnie Thomas

Bonnie Thomas

[email protected]


























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



best in class.













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.

Bonnie Thomas

STAGE 1:DISPARATEREPORTSNo integrated view—flying blind

[email protected]

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

Bonnie Thomas

Stage 1 is the reality

Bonnie Thomas

Bonnie Thomas

Stage 2

Bonnie Thomas

[email protected]

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.


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.

Bonnie Thomas

STAGE 2: MANUALINTEGRATEDREPORTING

Bonnie Thomas

Bonnie Thomas

Stage 2

[email protected]



















prohibitive.









possible.


































comparisons.
























of Integration.









Matter.









for technology.























manual approach.








and load.



















8

Bonnie Thomas

Stage 2

Bonnie Thomas

[email protected]



















prohibitive.









possible.


































comparisons.
























of Integration.









Matter.









for technology.























manual approach.








and load.



















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

[email protected]

http://bit.ly/1v5ErdD

http://bit.ly/1svqV1c



















prohibitive.









possible.


































comparisons.
























of Integration.









Matter.









for technology.























manual approach.








and load.



















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

[email protected]










for technology.























manual approach.








and load.



















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

Bonnie Thomas

STAGE 3: AUTOMATEDINTEGRATEDDASHBOARDS

[email protected]









for technology.























manual approach.








and load.



















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

[email protected]









for technology.























manual approach.








and load.



















http://bit.ly/1pRj6TB

13

Bonnie Thomas

Stage 4

Bonnie Thomas

[email protected]









for technology.























manual approach.






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.




and load.



















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

Bonnie Thomas

STAGE 4:PROACTIVE PLANNINGUsing data toplan and experiment

[email protected]









for technology.























manual approach.





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.




and load.



















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

[email protected]









for technology.























manual approach.








and load.



















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

Bonnie Thomas

STAGE 5: MODELING

[email protected]









for technology.























manual approach.








and load.



















17
























































the call.








“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

[email protected]









for technology.























manual approach.








and load.



















18
























































the call.








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

[email protected]

http://onforb.es/1l7Rc53

http://bit.ly/1pDdOOy

http://bit.ly/1tMmP6n

http://bit.ly/1vbfvkZ

http://bit.ly/1kMm08c

http://ibm.co/KJZByM

http://bit.ly/10vpnP3

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

[email protected]

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

[email protected]

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.

[email protected]

217 SOUTH B STREET, SUITE 4

SAN MATEO, CA 94401

21

Bonnie Thomas

remove hypen in omnichanneladd “and scorecards”after “rich dashboards”remove the word “industry”before “best-practice”