emarketer webinar: making attribution work in the age of big data

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Making Attribution Work in the Age

of Big Data

Lauren T. Fisher

Analyst

October 22, 2015


Agenda

Why attribution: The challenges created by big data

The current state of attribution

The challenges of moving to a more cross-platform

approach

Examples for dealing with today’s shortcomings


What is attribution?

A method of

assigning credit

to a particular

marketing- or

advertising-driven

action or other

brand-imposed

touchpoint

Company

Level

Is my

advertising/marketing

successful?

Department Level

Was this campaign successful?

Team Level

Was this tactic successful?


Big data is a

top attribution

challenge, but

it’s not the

only one


Attribution is the simple answer to big data

woes, but its implementation is anything but

“The need for cross-platform attribution is

completely clear, but also

unbelievably complex at the same

time. Everybody sees the potential power of

data to understand the contribution of every

device to an ultimate action. But

understanding how to track all the

data in a truly holistic way is a whole other

ballgame.”

—Cindy Gustafson, Managing Director, Invention

Studio, Mindshare North America


Attribution Today


Given the

confusion

around

attribution,

there’s little

alignment on

which

approach to

attribution to

take


But by 2017, half of all US digital marketers will

transition to multitouch approaches


Moving to a multitouch model is a must to see

the interplay between channels and tactics

Most multitouch

models are built

using either a

top-down or

bottom-up

approach


Two distinct attribution approaches:

Top Down

Branding, traditional media

(print, TV, etc.)

Econometrics, marketing

mix modeling

Uses GRPs/impressions,

spend, sales data, external

data to model attribution

Budget allocation and media

planning

Bottom Up

Native to digital

Path analysis

Uses cookies and other

web data to analyze

actual activity

Optimization,

performance


To answer the

ultimate question,

most

acknowledge a

blend of both is

required

Company

Level

Is my


successful?

Department Level


Team Level



But combining top-down and bottom-up

approaches isn’t a simple matching game

“Organizations just have too many

systems in place. Attribution is still

very technical and requires too many

people and too many hands in the pot.

I think that’s one of the reasons the

top-down and bottom-up

approaches have yet to meet in

the middle somewhere elegant.”

—Aaron McLean, Partner at Stuzo


The same big data challenges that demand

attribution are the ones that hinder it

Accounting for all the disparate data sets, channels and

reports

Arriving at a single audience view (universal identifier)

Reaping actionable insights from the data


Challenge #1: Organizing

Disparate Data Sets


Identifying

and gaining

access to all

the necessary

data can be

an onerous

task


Making sense

of that data

can cause

even more

frustration


What companies need is a single receptacle for

collecting, storing and normalizing all that data

Today, one of

the most

common

solutions is a

data

management

platform (DMP)


The utility of a

DMP extends

well beyond

organizing and

normalizing

data


Challenge #2: Lack of a

Universal Identifier


Attribution can be done without identity

matching, but bottom-up models really demand it

“Yes, you can look at some

correlations and make some

assumptions. But if you don’t

know it’s all one consumer,

the data starts to degrade in

usefulness pretty quickly because

you’re only attributing on a

portion of someone’s daily

life, not the entirety of it.”

—Nick Jordan, SVP, Global Strategy, Tapad


But so few

have that kind

of insight into

their data


Furthermore,

obtaining a

single,

universal view

of one’s

audience and

data is hardly a

standardized

practice


Most companies today use one or both of the

following identifying methods:

Deterministic identification

Probabilistic identification


Deterministic Identification

How it works: Relies on known-user information to recognize,

with near-certain confidence, an individual on each of his or her

internet-connected devices.

Examples of data used: Anonymized login data, CRM-level data,

email address or customer ID.

Companies taking a deterministic approach: Facebook,

Google, AOL, Amazon.com, The Weather Company, eBay, etc.


Probabilistic Identification

How it works: Uses a mix of publicly available

ad-serving data to assess the statistical probability that a

specific device belongs to a particular individual.

Examples of data used: IP address, browser version, operating

system data, device type, browsing behavior, content consumption patterns, location-based data and time of day.


Example: OEM uses DMP to tie digital influence

back to car dealership sales

The goal: To understand how a digital advertising campaign is

driving actual sales at the dealership

Data in: website activity, search, social, email, display, mobile,

cross-device IDs, third-party data, dealership data

The result: Ability to tie dealership sales back to digital

touchpoints (cross-device) to find influence of each exposure

and channel on the sale

Added benefit: Improved customer service experience at

dealer; knowledge of channel influence is used to optimize

customer experience


Challenge #3: Gaining

More Real-Time Insights


Looking

ahead,

it’s not just

about

attributing,

but taking

more

real-time

action on

that info


The real aim of attribution is to move from

something like this:

To this:

Company

Level

Is my


successful?

Department Level


Team Level


Company Level

Is my advertising/marketing

successful?

Department Level

Was this campaign

successful?

Team Level



The shift to more real-time use of attribution will

continue, though it will never be complete

“It’s unrealistic to expect 100% of all

channels will be optimized in real

time.”

—Allegra Kadet, Managing Director, neo@Ogilvy


Example: Taking a more “real time” approach to

TV advertising to drive in-store sales

The goal: Mindshare working with national advertiser

looking to drive in-store sales in an eight-week window

Data in: Marketing and advertising data (digital,

traditional, etc.), in-store purchase data, competitive data

Model used: Primarily top down (marketing mix model)

enhanced with digital and competitive data

Benefits of this approach: Ability to optimize in near real

time, even TV


Key Takeaways:

Big data is causing big problems for companies tracking

advertising and marketing success

Attribution is the answer, but transitioning to a holistic

approach is not without difficulties

DMPs are critical to organizing and normalizing data

Gaining a single customer view is critical for a bottom-up

approach, but it’s still a work in progress

Looking ahead, attribution will be just as much about

influencing success as it will be about measuring it

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Lauren T. Fisher

Cross-Platform Attribution:

Overcoming Big Data Challenges

Cross-Platform Attribution 2015: Device Identification, Big Data

Pose Continued Challenges

The Mobile Attribution Gap: Five Missing Links in Mobile That

Makes Attribution Harder

The State of US Digital Advertising 2015: Mobile Trumps Desktop,

People Trump Devices

Digital Display Advertising: Nine Things to Know for 2015

Cross-Device Targeting: Success Hinges on Device Identification

Methods