dataxu whitepaper davenport 3 steps programmatic

8/12/2019 DataXu Whitepaper Davenport 3 Steps Programmatic

1/10

Three Steps toProgrammatic Marketing

THOMAS H. DAVENPORT

Founder International Institute for Analytics (IIA), Visiting Professor, Harvard

Business School, Babson Distinguished Professor for Information and Management.
http://www.dataxu.com/


2/10

2

2013 DataXu, Inc. All rights reserved.

DataXu | 281 Summer Street, 4th Floor | Boston, MA 02210

www.dataxu.com

Three Steps toProgrammaticMarketing

Contents

Executive Summary 3

Three Steps to Effective Programmatic Marketing 4

About The Author 9

About DataXu 9
http://www.dataxu.com/http://www.dataxu.com/http://www.dataxu.com/


3/10

3



www.dataxu.com

Executive Summary

Technologies for managing marketing data, generating marketing

analytics, and driving marketing decisions have proliferated rapidly

over the past several years. At the same time, digital marketing

has become increasingly important, and has dramatically

accelerated the pace of marketing decisions. Many marketing

organizations and executives have had difculty keeping up

with the changing landscape. There are many opportunities to

make better, faster, and more cost-efcient marketing decisions

through the use of programmatic marketinga combination of

technology, people, and processes that leverage data science to

better understand and engage consumers and maximize return

on investments. However, many rms and executives still make

only intuitive, experience-based decisions. Nevertheless, its

important to move toward programmatic marketing, and this

paper outlines three steps that executives can take to position

their organizations to win in a digital world.

There should be little doubt that marketing is moving from a

purely intuitive, creative function to one that is also data-driven

and computational. There is still the need for creativity and

intuition, but as media increasingly become digitized, and as

customers increasingly become addressable across all channels,

marketing decisions can increasingly be based on big data and

algorithms. When thousands of decisions on digital and socialmedia need to be made every second, there would seem to be

little choice but to embrace tools that make rapid decisions about

which brands and campaigns relate to which customers. Yet many

marketing executives are still attempting to base their decisions

solely upon intuition and gut feel. They may acknowledge the

value of data science and analytics in principle, but they struggle

to identify and implement an appropriate solution.

The balance between art and science in

marketing is undeniably shifting toward

sciencedecisions based on data.

A recent survey illustrates how poorly prepared many

marketers are for dealing with the data-driven transformation

of the function. The Corporate Executive Board surveyed 800

marketers at Fortune 1000 companies. The survey revealed

for example, that marketing executives depend on data for

just 11% of all customer-related decisions. When asked what

type of information supported a specic recent decision about

customers, data was actually last on the list, after such anecdota

sources as conversations with colleagues, advice from experts,

and interactions with single customers. Only 6% of the marketers

could correctly answer ve basic questions about statistics, and

only 5% of the sample owns a statistics textbook.

Of course, there are still some intuitive and ad hoc decisions to

be made in marketing. If there isnt much data available, or if

the decision involves close human relationships, human intuition

may be the only resource. Decisions on whether to run a Super

Bowl ad, which agency to partner with, or how to create the big

idea still rely on human deliberation by Chief Marketing Ofcers

(CMOs) and their teams. The people that CMOs hire and the

relationships that he or she chooses to build inside and outside

the corporation are also undeniably human decisions based on

experience. Although these decisions may be unstructured, even

they can benet from data and analysis when they are availableand feasible.

No matter what the type of decision, the balance between art

and science in marketing is undeniably shifting toward science

decisions based on data, algorithms, and formalized business

rules. Many observers are beginning to refer to this approach

to marketing as programmaticone that is rapid, repeatable

and based on continuous testing, learning and optimization. This

approach allows marketers to make the most repetitive decisions

through automation, which frees their attention for the strategic

and experience-based ones.

The limited adoption of programmatic marketing is not simply

because some marketers have their heads in the sand; there are

good reasons for having difculty with data-driven marketing

For example, the data and systems for marketing decisions are

piecemeal and fragmented. In digital media management alone

there are diverse sets of tools and capabilities for optimization

analytics, testing, demand-side platforms, networks and exchanges

social, video, mobile, and creative. For marketing and custome


4/10

4



www.dataxu.com

relationships overall, there are CRM systems, campaign

management systems, online marketing, data and attribution

management and web analytics platforms, and a variety of

other tools. The panoply of agencies, consulting rms, and

other marketing services organizations is just as fragmented.

At the moment, at least, one-stop shopping for programmatic

marketing is a painful illusion. Even for those organizations thatcan handle this complexity, the resources necessary to acquire

and integrate all these tools may be difcult to muster.

All of these new software capabilities have become available at

a time when there are also unprecedented opportunities for le-

veraging new hardware and processing alternatives. Analytics in

the cloud has become a viable option for many organizations,

and allows for low levels of initial investment with easy-to-add

capacity. For organizations wanting to compute their marketing

analytics on premise, there are much faster (and in some cases,

cheaper) tools for doing so, including big data server clusters

running Hadoop, grid computing, in-memory analytics, and in-da-tabase processing. All these new tools simply mean more choices

and decisions for CMOs.

These questions are not going to be resolved overnight. Bill

Pearce, the former CMO of Del Monte who now teaches marketing

at the University of California, Berkeley business school,

commented, I think we are a decade out from having this

solved. Yet some rms, such as Procter & Gamble, are moving

aggressively on data-driven marketing and analytics. P&G has

already undertaken initiatives in the following areas:

Accelerated marketing mix analysis to a more continuous,

scenario-based analysis (versus the common annual approach);

Introduced new approaches to e-commerce on social sites, e.g.

Facebook;

In-house development of a digital media buying optimization

solution (Hawkeye);

Consolidated relationships with digital marketing services

providers, including Nielsen and Accenture;

Holding an annual internal conference (Signal P&G) on digital

and social media;

Development of an online social analytics tool on more than 50,000

employees desktops (Consumer Pulse);

Textual analysis of social media comments and buzz;

An overall initiative driven by CEO Bob McDonald to make P&G one

of the most digitally-oriented companies in the world.

Of course, marketers cant leave the aggressive innovation to

P&G. CMOs across companies and industries need to act nowto

acquire new technologies, manage marketing data, and make

better strategic and tactical decisions. Given this shift and these

obstacles, how can CMOs and marketing organizations plot an ef-

fective course, and follow the lead of pioneers in programmatic

and data-driven marketing?

To determine these issues, I interviewed a dozen senior marketing

executives across a variety of industries. The primary topic

was the shift toward programmatic marketinghow they are

handling the changes, what specic steps they are taking, whichtechnologies they are counting on, and how they are nding

people to drive the changes.

Three Steps to Effective ProgrammaticMarketingIn the rest of this report, I describe three steps to effective

programmatic marketing. These are not necessarily sequential,

although completion of the later steps cant happen without

substantial progress being made in the earlier ones.

Step 1: Understand your customers

At the core of any effective programmatic marketing strategy

is a clear 360 degree understanding of customer attributes and

behaviors across all channels on and ofine. Given the rapid

proliferation of channels and customer information sources, this


5/10

5



www.dataxu.com

is a challenging and ongoing step. The perfect, fully integrated,

omni-channel customer database may never exist, but companies

need to make considerable progress toward this goal.

When a new customer channel or touch point emerges, the natural

tendency is to separate it from previously existing programs and

data. The new channels are unproven, and typically involve typesof data with which your organization will not be familiar. Its

challenging to embed the new data types with the tried-and-true.

However, it should be clear by now that we live in an omni-channel

world and that online and ofine messages must coexist. Similarly,

social and mobile channels are here to stay. Of course there are

privacy-related issues to consider, and most organizations avoid

joining personally identiable information (PII) about customers

with their specic browsing behavior. Companies that are fully

committed to omni-channel relationships, such as Vistaprint and

1-800-Flowers.com (perhaps the only rm with an omni-channel

name!) are developing or have already developed integratedcustomer data environments.

Therefore, organizations should combine customer information

from online and ofine contexts whenever possible. In most cases

this will take the form of an integrated customer data warehouse

of some sort, incorporating online and ofine behaviors,

offers and promotions, responses, and the more traditional

demographic and segmentation data. Using multiple sources of

data about customers not only provides a more complete picture,

but also allows marketers to separate the signal from the noise

to identify truly meaningful trends.

There are lots of subsets of this big data

areaproduct performance, consumer

behavior, other inputs. Were knitting

together the data, multiplatform stuff,

CRMthere are so many different threads to

pull together.

Julie Cary, CMO, La Quinta

The integration of data should encompass not only online and ofine

sources, but also data from a variety of customer intelligence

sources: CRM, ERP, web transaction logs and clickstreams, social

analytics, and even call center activity. It should also incorporate

data types across geography, business units and product groups,

and purchasers, prospects, and inuencers. There are generally

no good reasons for sprinkling customer data in small databases

or marts around an organization, making them inaccessible to

broad campaigns.

The head of marketing for an entertainment company commented

on progress in this regard:

We are stitching things together all the time. Its not

extraordinarily difcult, but its a lot of work. There are lots of

subsets of this big data areaproduct performance, consumer

behavior, other inputs. Were knitting together the data

multiplatform stuff, CRMthere are so many different threads to

pull together.

Many problems in customer analytics come about because of

fragmented organizational responsibilities. For example, the

Service organization controls the CRM system and data, and they

are reluctant to share it with us in Marketing.

Julie Cary, CMO of hotel chain La Quinta, pointed out in an

interview that data integration sometimes requires organizationaintegration. She commented:

We have two different databases that arent fully integrated

and have disparate data sources that inform all aspects of

our marketing. Integrating this data is time consuming but

has provided valuable insights. I also see more overlap in the

functional roles on my team loyalty, online media, e-commerce

etc. As a result, we restructured our department. It is important

for each functional part of our marketing team to see the

customer all the way through the life cycle. We are also trying

to integrate and collaborate with more travel partners to jointly

advertise and target certain groups online, and contain theinformation for later remarketing.

A customer intelligence initiative should address the importance

to your business of relatively new forms of marketing data,

such as social, mobile, and locational data from and about


6/10

6



www.dataxu.com

your customers. Its sometimes difcult to identify customers

consistently across all channels, but companies need to begin

incorporating data from these new touch points. Some retailers

are even beginning to identify customers though their mobile

devices when they enter storeswith their customers permission,

of course.

Step 2: Invest to Maximize Return

With some degree of integrated customer intelligence in place,

companies should invest in programmatic marketing capability

with a constant focus on maximizing return on investment (ROI).

In order to maximize returns, its important to create a broad

platform for programmatic decisions, and to understand how

to attribute sales and results. Its also important to invest in

programmatic marketing skills to augment creative skills.

In todays marketing environment, the insights arrive at a furious

pace, but most organizations do not have the structures and tools

in place to effectively manage them. Unfortunately, analysis

paralysis is a crippling disease. There are simply too many

variables and too many decisions to be made in contemporary

marketing for humans to deal with them allor even for humans

to employ traditional hypothesis-based analytics. Therefore it is

necessary to invest in programmatic approaches that can operate

at the speed of todays digital marketing.

Integrating this data is time consuming buthas provided valuable insights.

Julie Cary, CMO, La Quinta

Digital and social marketing alone can involve literally thousands

of decisions a day about what content to run on what sites.

Prices for display ads change by the second, and vary widely

across publishers. The only way to handle this frequency and

complexity of decision-making is through the use of decision

automation and algorithms.

To maximize ROI, marketers must invest resources in customers

likely to deliver a high lifetime value (or invest appropriately

given a consumers potential lifetime value). A solid foundation

of omni-channel customer intelligence makes it possible to build

respective audience segments. Many marketers identify a set o

customer segments, but they dont have the ability to consistently

treat each segment differently. In order to realize the potentia

of customer intelligence, marketers must understand how, when

and where to engage each of their subsets of customers.

There are a variety of approaches to implementing the

necessary algorithms and programmatic processes. A key

objective is to automate frequent tasks such as digital media

buying, while maintaining tight integration with custome

data management. Some organizations may choose to developtheir own systems, although this would require a very high

level of analytical, marketing, and technical expertise. Some

may employ consultants or agencies for this purpose. Since

the vendor environment for marketing software tools is highly

fragmented, sourcing different algorithms in different tools

may lead to a very complex and siloed set of models and vendo

relationshipsdefeating information technologys fundamenta

purpose of making things more efcient. Whenever possible

seek technologies that are exible in terms of their optimization

capabilities and can work with a variety of marketing entities

data types and marketing objectives.

We have people with great creative

skills, other people with analytics

expertisebut very few with both. At a

minimum we need to get those two sets

of people talking with each other.

In order to understand the returns on your marketing investments

companies need an effective model for omni-channel attribution

The attribution of results to particular customer contacts and

relationships is an important issue for any marketing organization

If you dont know what combination of creatives, messages

or customer contacts leads to a sale, then you have no means

of knowing whats working in a campaign. Attribution models

became popular with digital marketing, but the need to attribute

results to a marketing initiative is as old as direct mailthe rst

addressable technology in marketing.


7/10

7



www.dataxu.com

In a complex omni-channel environment with a variety of

digital inuences on a sale, attribution becomes substantially

more difcult. Many organizations are still using a last click

attribution model, even though there may be many other

customer touches that inuence a sale. Models based on the last

click dont allow an understanding of the multi-channel inuences

on a sale or conversion, and are likely to be found wanting in anassessment. Increasingly, sophisticated marketing organizations

understand what channels and touch points are used for each

unique customer journey in their campaigns. Otherwise it is

difcult to optimize variables that havent been dened by the

organization. Some organizations optimize only on cost, but wise

organizations will optimize both cost and benets.

Assuming that the organizations current attribution model

isnt able to assess all of its programs and channels, at this step

the organization should begin generating a variety of different

models and comparing the results and their implications for

marketing decisions. While the perfect attribution model is anelusive concept given the difculty of tracking every customer

contact and understanding all customer behaviors, almost

every organizations model could be improved. A key tool

to allow multi-channel attribution is the demand-side data

management platform (DMP), which should be licensed and

controlled by the CMO.

Using loyalty data from retailers has helped.

We are doing a lot of digital experimentation,

and that is helping us refne our model.

Some organizations have noted that the problem with attribution

is less a matter of analytics, and more an issue of getting access

to the necessary data. At a global consumer products rm, for

example, a senior marketing executive noted, For us the

problem of attribution is less about analytics, and more about

getting a line of sight to the information. That is particularly

difcult in industriesas in consumer productswhere there are

intermediaries between the company and its consumers. The

same executive noted, Using loyalty data from retailers has

helped. We are doing a lot of digital experimentation, and that is

helping us rene our model. Its not impossible, but it has to be a

very focused task. A nal point here is that rms should beware

of agencies, service providers, and partners who do not have, or

are unwilling to provide a vehicle to ow the needed information

back to the enterprise. As clients, they have leverage with those

providers and should use it.

In order to effectively make and monitor these investments in

programmatic marketing, many organizations will also need to

invest in analytical marketing skills. One of the greatest barriers

to the use of marketing analytics is a lack of skills. As one CMO in a

media company put it, We have people with great creative skills

other people with analytics expertisebut very few with both

At a minimum we need to get those two sets of people talkingwith each other. Some organizations have developed major

initiatives to address this issue, including a week-long Marketing

Boot Camp at Nationwide Insurance; the same company has

sponsored a similar program at Ohio State University, a schoo

from which it recruits heavily.

Step 3:

Engage customers more effectively

The overall payoff of these investments in customer data andprogrammatic marketing is better customer engagement. Given

the very fast pace of change in the marketing domain today

most organizations will benet from simply getting started

with programmatic software and processes, customizing the

implementation, and learning from the experience. This is no

era for long-term planning, but any steps taken should build a

broader capability, moving the organization toward a more

platform-oriented approach to programmatic marketing, with

less fragmentation and more broadly-capable tools.

Consistent with the idea of making rapid progress, many

companies are adopting more agile methods for implementingnew technologies, making changes rapidly, and generating

insights. A director of marketing analytics at a consumer goods

company commented, Its easier to build models than to get

everybody aligned on them in advance. So we use scrum methods

to build new model prototypes quickly, and then people can see

what they will get with them. The head of marketing analytics

at an insurance company commented, If we wait for the perfect

model well all be dead. So we try to generate an insight per

week for our internal customers. Of course, with programmatic

tools and processes in place, the rate of model generation could

approach many insights per second.

Its easier to build models than to get

everybody aligned on them in advance.


8/10

8



www.dataxu.com

Figure 1 displays typical cycle times for traditional market

testing (one month at best), operational campaign analysis

(one week at best), and programmatic marketing, in which

decisions can easily take place in micro- or milliseconds.

Figure 1Typical Cycle Times for Marketing Decisions

The key to success is the speed at which an organization can not

only generate new insights, but absorb and act on them going

forward. Marketing campaigns that are driven by analytics andautomated decision-making can generate a lot of insights quickly

that can be used to inform future campaigns. For example, in

one campaign by a nancial services company using the DataXu

platform, the company learned a great deal about marketing to

students, such as:

Consumers who were targeted for the Student Checking Account

converted at a higher rate than those who were targeted for

similar banking services in other nancial services campaigns.

The volume of applications signicantly dropped in the second

week of the school year, indicating that students made nancial

decisions within the rst week of arriving on campus.

Creatives geared towards parents elicited a positive response,

indicating their involvement in their childrens nancial decisions

into early adulthood, as well as the students reliance on their

parents for nancial advice.

Female consumers aged 18-22 had a higher response than their

male counterparts.

Each of these insights could greatly inform future campaigns to

the same audiences.

Not only marketers, but also marketing analytics and technology

people can learn from these early experiences at a time when

everything is moving quickly. As Bill Pearce, the former CMO of

Del Monte put it:

It will take some leading rms to align all of us on how to do

data-driven marketing, and to generate a common lexicon for

everybody. The schools arent teaching it yet, and right now

the top 100 leading national advertisers have 100 different

approaches. It will take a while to be sorted out.

The only real mistake is to do nothing. An analytical

manager at a large oil company, for example, bemoaned

their companys situation:

Were doing nothing except running broadcast ads, and we

dont even know that they work. Were not experimentingwith social or digital. Our marketers dont know anything

about these new approaches; they were trained in a different

era and they are scared of anything new. Frankly, nothing short

of an entire generational change in our Marketing organization

will make a difference.

It will take some leading frms to align all

of us on how to do data-driven marketing.

Bill Pearce, former CMO of Del Monte

Execution

Insight

Operational Stage

(1 week cycle)

Testing Stage

(1 month cycle)

Programmatic stage

(0.1 second cycle)


9/10

9



www.dataxu.com

Summary and Conclusions

The three steps outlined in this report may seem straightforward, but they can be complex to accomplish. Each of them will be

undertaken not once and for all, but in an ongoing fashion. Investment in IT and human capabilities will be required throughout the

journey to programmatic marketing. However, it is also clear that a move to more integrated and platform-based data and software

environments can yield cost efciencies in terms of personnel reductions, elimination of unnecessary intermediaries, increased lift

from marketing and media investments, and data rationalization and redundancy avoidance.

The keys to success are to get started, to involve the entire organization, to view the customer as a single entity with a variety of

attributes and touch points, and to learn and adapt from initial experiences. It may be a confusing time to be a marketer, but it is also

one of the most exciting and change-lled periods in the history of the function.

About the Author and Sponsor

Thomas H. Davenport is a Visiting Professor at Harvard Business School, a distinguished professor of IT and Management at

Babson College, co-founder and research director of the International Institute for Analytics (IIA), and a Senior Advisor to

Deloitte Analytics.

This independent research study was conducted by Thomas Davenport, and was sponsored by DataXu, a leading provider of digital

marketing management platforms. To learn more about DataXu, visit the companys website at www.dataxu.com.

For more information on this topic or the research, please contact Tom Davenport at [email protected].

About DataXuAt DataXu, we are transforming the way global brands market to consumers in the digital world. An enterprise data and analytics

company, DataXu offers DX3, the rst omni-channel marketing management platform. DX3 helps you easily manage the deluge of

big data, turning insights about consumer behavior into immediate decisions that impact Return on Marketing Investment (ROMI)

We help brands to more effectively and efciently reach and engage consumers, resulting in improved brand engagement, customer

acquisition, and sales.

The private company is backed by Atlas Venture, Flybridge Capital Partners and Menlo Ventures. For more information, visit www.

dataxu.com or follow us at Twitter.com/dataxu.

281 Summer Street

4th Floor

Boston, MA 02210

Phone: 857-244-6200

dataxu.com/about-us/contact

DataXu Tops 2013 Inc. 500 as #1 Fastest GrowingAdvertising & Marketing Company and #5 overall.
http://www.dataxu.com/http://www.dataxu.com/http://www.dataxu.com/about-us/contacthttp://www.dataxu.com/about-us/contacthttp://www.dataxu.com/about-us/contacthttp://www.dataxu.com/about-us/contacthttp://www.dataxu.com/about-us/contacthttp://www.dataxu.com/inc500/http://www.dataxu.com/inc500/http://www.dataxu.com/http://www.inc.com/profile/dataxuhttp://www.dataxu.com/inc500/http://www.dataxu.com/about-us/contacthttp://www.dataxu.com/about-us/contacthttp://www.dataxu.com/about-us/contacthttp://www.dataxu.com/about-us/contacthttp://www.dataxu.com/about-us/contacthttp://www.dataxu.com/about-us/contacthttp://www.dataxu.com/


10/10

10



www.dataxu.com

DataXu Ofces

BOSTON HEADQUARTERS

281 Summer Street

4th Floor

Boston, MA 02210

Phone: 857 244 6200

Fax: 617 426 5971

CHICAGO

101 N. Wacker Dr.

Suite 606

Chicago, IL 60606

Phone: 917 746 8193

DETROIT

333 West Seventh St.

Suite 280

Royal Oak, MI 48067

NEW YORK

373 Park Avenue South

4th Floor

New York, NY 10016

Phone: 917 338 0703

SAN FRANCISCO

433 California Street

Suite 1010

San Francisco, CA 94104

Phone: 415 684 7124

COLOGNE

Innere Kanalstr. 95, 50823

Kln, Germany

Phone: +49 (0) 221 888 829-0

LONDON

4th Floor Fitzrovia House

153-157 Cleveland Street

London W1T 6QW

UK

Phone: +44 (0) 20 7636 8010

MADRID

Plaza Sta Maria Soledad Torres Acosta 1, 4

28004 Madrid

Spain

Phone: +34 (0) 91 758 96 46

MILAN

Via Marco Formentini 1, 20121

Milan, Italy

Phone: +39 366 43 61 775

PARIS

12, rue La Fayette

75009 Paris

France

Phone: +33 (0)1 84 16 34 08

SAO PAULO

Rua Augusta, 2690 | 3 andar | Galeria Ouro Fino

Jardins | So Paulo | SP | 01412-100

Brazil

Phone: +55 11 98747-1987

WARSAW

ul. Krucza 16/22, pitro 5

00-526 Warszawa

Poland

Phone: +48 602299880

dataxu whitepaper davenport 3 steps programmatic

Documents