2018_Smarter Decisions in Mobile Marketing

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How Brands Can Make Smarter Decisions in Mobile Marketing Strategies for Improved Media-Mix Effectiveness And Questions for Future Research Vassilis Bakopoulos

Editors’ Note:

Mobile Marketing

In 2014, the Mobile Marketing Association launched a research initiative to help individual brands

Association

improve the efficacy of their mobile-marketing efforts. Each case study addresses marketers’ core questions in a unique way: What share of their overall advertising spend should be allocated to mobile marketing?

[email protected]

How should they use mobile formats and targeting methods more efficiently to maximize the performance

John Baronello

of their media investments? Although the authors acknowledge that their findings “might not provide

Allstate Insurance

definitive answers of long-term effect for all marketers,” they do offer insight into how mobile marketing

John.Baronello@

can be optimized on a case-by-case basis. In 2017, campaign case studies with Allstate Insurance and a

allstate.com

major U.S. fast-food, or quick service restaurant (QSR), chain were the latest additions to a body of work with AT&T, the Coca-Cola Company, MasterCard, Walmart, and Unilever. The most striking results

Rex Briggs

came from the QSR study, which estimated an optimal allocation to mobile for that campaign at 33 percent

Marketing Evolution

of the total media mix—the highest allocation ever recommended in this research program. In the pages

rex.briggs@

that follow, the authors describe their methods and findings, and propose best practices and questions for

marketingevolution.com

future research.

INTRODUCTION

do not have a formal mobile strategy for their

Since the arrival of the smartphone in 2008, con-

brand, and only one out of three say they are ready

sumers have embraced mobile technology faster

for mobile adoption.5 Marketers, moreover, are

than any previous technology. Smartphones

unclear about the degree to which mobile drives

have become the most important device to access

revenue and profitability6 and its impact within

the Internet. Consumers increasingly use these

the context of their overall advertising mix. Under-

mobile devices to make purchases; mobile usage

standing the value of mobile is part of a much

accounts for at least half the traffic and one-third

bigger challenge of accurate attribution across

of the revenue of e-commerce.3 Mobile’s share of

all marketing channels—the process of assigning

total advertising spending has increased rapidly

value to a set of events or touch points that contrib-

and is projected to reach 36 percent by 2020, sur-

ute in some manner to a desired outcome.

1

2

passing television’s share of spending, according to eMarketer.4

Traditional top-down, aggregated approaches, such as marketing-mix modeling, have addressed

The rapid pace of change has left many marketers

the topic of budget allocation for decades.

unprepared for the new environment; two-thirds

About 80 percent of marketers currently use this

  Benedict Evans for Andreesen Horowitz. (2016, March 29). “Presentation: Mobile is eating the world.” Retrieved May 3, 2017, from http://benevans.com/benedictevans/2016/3/29/presentation-mobile-ate-the-world. 2   Office of Communications, United Kingdom. (2016, February). “Ofcom Nations & Regions Tracker: Main set.” Retrieved October 4, 2017, from https://www.ofcom.org.uk/__data/assets/pdf_ file/0030/68358/ofcom_technology_tracker_h1_2016.pdf. 3   Benedict Evans for Andreesen Horowitz. (2016, March 29). “Presentation.” 4   eMarketer. (2016, November 1). “US Ad Spending: eMarketer’s Updated Estimates and Forecast for 2015–2020.” Retrieved May 3, 2017, from https://www.emarketer.com/Report/US-Ad-SpendingeMarketers-Updated-Estimates-Forecast-20152020/2001915. 1

approach, according to Mobile Marketing Association (MMA) data.7 More recently, some marketers   WARC. (2017, May 17). “State of Industry: Mobile Marketing in North America.” Retrieved May 18, 2017, from http://bit.ly/2rx3hsY. 6   CMO Survey. (2016, August). “CMO survey report: Highlights and Insights.” Retrieved May 3, 2017, from https://cmosurvey.org/wpcontent/uploads/sites/11/2016/08/The_CMO_Survey-Highlights_and_ Insights-Aug-2016.pdf 7   Mobile Marketing Association. (2016, October). “Marketer Research Study: Marketing Productivity Assessment Attitudes.” Retrieved May 3, 2017, from http://www.mmaglobal.com/files/documents/marketing_ productivity_assessment_marketer_study_july_2016.pdf. 5

DOI: 10.2501/JAR-2017-052December 2017  JOURNAL

OF ADVERTISING RESEARCH  447

How Brands Can Make Smarter Decisions in Mobile Marketing

have striven to measure media effective-

• How should we plan and execute for

of various mobile tactics. An extensive

ness at a more granular level, favoring

mobile in the context of an integrated

review of published research (Grewal, Bart,

multi-touch attribution approaches that

campaign?

Spann, and Zubcsek, 2016) provides an

8

appear more relevant in a cross-platform

• Should we use mobile for branding,

context. Although these multi-touch attri-

direct response, or both?

bution methods show some promise, they

• Are there mobile tactics that are more

have their own limitations of validation,

suitable for the upper funnel versus the

data quality, transparency, and the abil-

overall framework to better understand the role of mobile in the mix and the key factors that influence it, including the following: • specific tactics, such as mobile pro-

lower funnel?

ity to unify data across channels.9 Adding

• Which specific key performance indica-

motions (Andrews, Luo, Zheng, and

to the attribution problem is the down-

tors (KPIs) should we measure and opti-

Ghose, 2015), mobile gaming (Hofacker,

side of mobile innovation. The increas-

mize against, and how?

Manchanda, Ruyter, Donaldson, and

ing number of formats, platforms, and

Lurie, 2016), mobile-shopper marketing

targeting methods has made it extremely

Acknowledging these knowledge gaps, the

(Shankar et al., 2016), mobile coupons

difficult for marketers to decide where

MMA in 2014 initiated an industry-wide

in different contexts (Ghose, Han, and

to focus.

research program called Smart Cross-

Park, 2013), and mobile-display adver-

Mobile’s key opportunity for market-

Marketing Effectiveness Research (SMoX),

tisements (Bart, Stephen, and Sarvary,

ers—data that can tell them when, where,

a series of individual-brand case studies.

2014);

and how to communicate with consumers

In total, 11 have been conducted in four

• the importance of context (environmen-

in order to maximize the impact on their

countries, including AT&T, MasterCard,

tal, technological, or consumer), and

decision making—has made budgeting

the Coca-Cola Company (four studies),

how location, time, and weather influ-

that much more complex. The “when,”

Walmart (two studies), and Unilever. Stud-

ence consumers’ reactions to mobile

“where,” and “how” represent different

ies with Allstate Insurance (both auto and

advertising (Molitor, Reichhart, and

dimensions of advertising delivery, and

home) and a major U.S. fast-food or quick

Spann, 2014). Research that preexisted

each comes with a price (for media, data,

service restaurant (QSR) followed in 2017.

the mobile “revolution” has examined

production, technology, etc.) that often is

Ford Motor Co. and MillerCoors studies

the impact of other variables, such as

difficult to justify. Marketers thus ask:

are in progress for 2018, and future work

weather, on consumer behavior (Hirsh-

is earmarked with a major U.S. bank and a

leifer and Shumway, 2003);

• What share of our overall advertising

10

leading fashion retailer.

spend should be allocated to mobile

• how mobile-display advertisements

Each study measures the effectiveness of

work in different industries (high ver-

a real cross-marketing campaign against its

sus low involvement), yet with conflict-

• How should we use these formats and

own marketing goals and media approach.

ing results (Bart et al., 2014; Shankar and

targeting methods more efficiently to

The current article focuses on results from

maximize the performance of our media

the Allstate and QSR studies. References to

• the impact of mobile on conversion

investments?

some of the earlier SMoX studies provide

goals, mainly in the context of promo-

context and comparison.

tional campaigns (Grewal et al., 2016).

marketing?

Balasubramanian, 2009); 

Questions about measurement, attribution, and mobile fragmentation might have dif-

BACKGROUND

There also is some evidence about the role

ferent answers, depending on the type of

What We Know

that mobile can play as part of the overall

campaign or even the industry and prod-

Mobile still is considered an emerging

advertising mix, and the multiple benefits

uct type:

channel within a growing body of research.

of advertising across multiple platforms

Researchers have explored, among other

(with or without mobile) versus a single

themes, how this channel influences the

platform. A recap of such benefits (Neijens

purchase-decision processes (Shankar

and Voorveld, 2015) includes the ability to

  Coalition of Innovative Media Measurement. (2017, February). “Current Practices in Attribution and ROI Analysis” (white paper). Retrieved May 3, 2017, from http:// cimm-us.org/wp-content/uploads/2012/07/CIMM-4AsWhitepaper_Current-Practices-in-Attribution-and-ROIAnalysis_February-2017.pdf 9   Mobile Marketing Association. (2016, October). “Marketer Research Study.” 8

448  JOURNAL

OF ADVERTISING RESEARCH  June 2017

and Balasubramanian, 2009), and there is increasing evidence about the effectiveness

• increase reach (e.g., Briggs, Krishnan, and Borin, 2005; Enoch and Johnson,

  The QSR company requested its name not be disclosed.

10

2010; Fulgoni and Lipsman, 2014; Taylor,

How Brands Can Make Smarter Decisions in Mobile Marketing  thearf.org

Source: Mobile Marketing Association, SmoX Study

Figure 1 Overview of Campaigns and Media Tested in the Smart Cross-Marketing Effectiveness Research Program (SMoX) Kennedy, McDonald, Larguinat, et al.,

emphasized the role of mobile (Snyder and

return on investment (ROI), with mobile

2013);

Garcia-Garcia, 2016):

video advertising being more effective

• take advantage of unique strengths of

in driving ROI than desktop video (with

individual media (e.g., Dijkstra, Buijtels,

• Mobile particularly is effective for

some exceptions, e.g., financial services)

and van Raaij, 2005; Okazaki and Hirose,

established brands, which consumers

and mobile video delivering higher ROI

2009; Tsao and Sibley, 2004);

have less need to research or validate.

compared with mobile banners.

• facilitate information encoding in a more

This aligns with previous work (Steele,

• Mobile banners, although less effective

complex way (Laroche, Kiani, and Econ-

Jacobs, Siefert, Rule, et al., 2013) sug-

than video, can benefit when placed in

omakis, 2013; Stammerjohan, Wood,

gesting that online environments are

contextually relevant environments (e.g.,

Chang, and Thorson, 2005; Tavassoli,

less able to invoke nonconscious emo-

related magazine or newspaper article).

1998; Vandeberg, Murre, Voorveld, and

tional connections, which are important

Smit, 2015; Voorveld, Neijens, and Smit,

components of media-delivered brand

What We Don’t Know

equity.

There has been far less research answering

2011; Voorveld and Valkenburg, 2015); • reduce wearout (e.g., Navarro-Bailon, 2012; Stammerjohan et al., 2005);

• Advertising in multiple platforms is

the question of optimal media allocation

more effective than advertising on a

derived from analysis at the individual

• create synergy, in terms of recall due to

single platform. These findings specifi-

user level. When marketers try to build

exposure to multiple media (e.g., Chang

cally emphasize the impact of the order

a zero-based budget, what should they

and Thorson, 2004; Dijkstra, 2002; Edell

of exposure, with stronger results when

do with specific mobile tactics in the con-

television came before mobile.

text of the overall media mix to optimize

and Keller, 1989; Voorveld et al., 2011).

• Different types of digital advertising

their business results? What percentage

Further evidence about the benefits of

(i.e., desktop, mobile) and formats (i.e.,

of their total advertising budget should

advertising across multiple platforms has

banner, video) deliver different levels of

go into mobile, and which advertising June 2017  JOURNAL

OF ADVERTISING RESEARCH  449

How Brands Can Make Smarter Decisions in Mobile Marketing

formats, targeting methods, and other tac-

• The Coca-Cola Company (2015–16):

Findings from previous tests with

tics should they use? Marketers need more

Mobile video and social advertising are

Coca-Cola and Walmart demonstrated

empirical evidence to address these ques-

very efficient drivers of purchase intent,

that richer advertising experiences, such

tions for their own campaigns, but attribu-

a finding validated in multiple studies

as mobile audio and video, usually more

tion across marketing channels remains a

(four video-related, two social media-

than justified their price premium in rela-

big challenge, and its solution becomes

related) conducted in North America,

tion to mobile display when it comes to

even more difficult with the increased pro-

Brazil, the United Kingdom, and China.

shifting perceptions and driving purchase

11

liferation of advertising channels.

• Unilever Magnum Ice Cream (2016):

intent. Yet, in some instances, simple ban-

The MMA’s case-study work with

Mobile social video advertising can be

ners were more efficient in terms of driving

individual brands has filled some of this

a strong driver of ROI. In this case, it

awareness (new awareness or top-of-mind

knowledge gap. One could argue that

was much more efficient than video in

awareness, in the sense of reminding con-

there are limitations to making broad

other screens, including desktop and

sumers of a brand message).

empirical statements, given that these

television.

studies span three years and a number of

Findings in 2017 from two companies in very different categories—Allstate auto

product categories. The state of the market

Research Questions

and home insurance (high-involvement,

and how marketers approach mobile also

The researchers posed three key questions:

infrequent purchase) and the fast-food

continue to evolve (See Figure 1), changing the nature of the questions and the context

(QSR) chain (lower-involvement, frequent RQ1

of some of these tests.

Path to purchase: How can

impulse purchase)—offered additional

a brand most effectively use

insight into the path-to-purchase ques-

The previous studies conducted as part

mobile advertising tactics to

tion. The goal was to provide an even more

of this series reached the following conclu-

engage consumers across the

granular read into how these formats affect

sions (with limitations; for deeper discus-

funnel?

attitudinal (upper funnel) or actual behav-

sion on AT&T, Walmart, and Unilever, see pages 455–458):

ioral (lower funnel) metrics—namely sales RQ2

Size, depth, and repetition: How

and, in the QSR’s case, foot traffic.

can a brand most effectively • AT&T (2014): Mobile display advertising can be a very efficient driver of brand

communicate a message on a

Size, depth, and repetition. Conven-

mobile platform?

tional wisdom suggests that attention

awareness for a new product launch.

span in mobile advertising is shorter, yet Targeting: What is the value of

the screen also is smaller. The size, depth,

cation to mobile in the total mix of that

different data signals—digital or

and repetition question aimed to reveal the

campaign.

physical—for improving target-

implications for the advertising units that

ing in mobile?

marketers use in mobile video and display

The findings justified a 16 percent allo-

• MasterCard (2015): A combination

RQ3

of mobile display, mobile video, and

while trying to communicate their adver-

mobile social advertising can be very

Path to purchase. Marketers have two

tising message. Should they design their

efficient in terms of driving brand image,

goals: strengthen consideration for the

mobile video to be shorter and their mobile

even among an older demographic of

brand by reinforcing image perceptions

display to be more “visible”? What does

nesters and empty nesters.

(top of the purchase funnel), and drive

that mean in terms of frequency of expo-

• Walmart (2015): Mobile advertising can

sales (lower funnel). There are different

sure and consumer experience?

be a very efficient driver of sales; in this

options provided by mobile in order to

The AT&T study provided early evi-

study, it was twice as efficient as the

support these goals, often with trade-offs.

dence that larger banners were more effi-

average of that campaign. Proximity

Simple, lower-cost mobile banners are

cient for driving awareness, compared

location targeting, when matched with

limited in their ability to communicate a

with the smaller “pencil” mobile ban-

expandable mobile display units, also

compelling message, yet richer experi-

ners. Findings from AT&T, Coca-Cola,

improves the impact of advertising in

ences, such as mobile video, are priced at

and Walmart also illustrated that banners

terms of driving foot traffic.

a premium. How can marketers use these

tended to have a “linear” relationship with

tactics to drive different KPIs? Should the

frequency, which meant that each addi-

tactics vary by product category?

tional exposure continued to build impact,

  Mobile Marketing Association. (2016, October). “Marketer Research Study.” 11

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How Brands Can Make Smarter Decisions in Mobile Marketing  thearf.org

even at a higher frequency, especially in

• measure mobile and other media down

optimization—including specific target-

relation to awareness. For the Allstate and

to the individual tactic and message;

ing using location, web behaviors, time

QSRs studies, the goal was to measure

• analyze the interactions among differ-

of day, weather, and other variables—

and assess more advertising formats and

ent media of very different individual

the analysis needed to connect directly

subformats, especially richer formats (e.g.,

brands;

to person-level media-planning and

video lengths of 15 seconds versus 30 sec-

• be applied across a wide range of prod-

onds), and to gain perspective from differ-

uct categories, each of them bringing its

ent product categories.

own complexity and data availability.

buying systems. • Specifically for Allstate, given that the purchase cycle for insurance can be long and the decision considered, the

Targeting. Mobile advertising exempli-

To have a meaningful degree of certainty

approach needed to include measure-

fies what is possible in targeting, given

about the causality, the method needed to

ment of advertising’s influence on con-

most of the time. With that in mind, in

• link a person’s activities across devices

• Because the product (insurance) can be

addition to considering traditional demo-

(e.g., a consumer who is exposed to a

sold through the physical location (in

graphic, income, or other consumer char-

message on a mobile device, who sub-

this case, a local agent), the analysis

acteristics, marketers increasingly think

sequently calls an insurance agent, and

needed to incorporate location analysis

in terms of “need states,” occasions, and

uses a desktop computer to request a

and location-based media optimization.

that people carry their phones with them

sideration, as well as on behaviors.

“key moments” in the customer journey

quote);

(Moran, Muzellec, and Nolan, 2014). Iden-

• incorporate the design of experiments to

tifying those moments and understanding

control and isolate the impact of expo-

Modeling

the “customer journey” is an important

sure to mobile media.

The MMA concluded that traditional

first step, yet marketers need evidence about these moments’ impact on ROI.

The Limitations of Marketing-Mix

marketing-mix modeling approaches The Allstate Insurance study focused

could not address all the above goals. As

By investigating targeting at individual

on understanding the path to purchase,

Forrester noted in its Fall 2016 Measure-

brands, the current researchers could

which required measuring both attitudinal

ment & Optimization Wave: “Market-

assess a variety of different “data signals”

survey-based metrics (consideration) and

ing mix models aren’t fast or detailed

used in mobile. The Walmart study in

behavioral metrics (sales).

enough…These models can’t provide

2015 shed light on the value of location targeting, and in 2016 Unilever’s Magnum

There were other methodological considerations across the various brands:

study assessed the impact of weather tar-

campaign performance detail that drives media buying: the networks, programs, publishers, and sites that will optimize

geting for an ice cream brand. In 2017, the

• Because of the nature of mobile, the

Allstate and QSR studies have built on

method needed to rely on something

Moreover, as noted in a 2013 Coun-

that knowledge covering a combination of

more permanent than cookies, because

cil on Research Excellence report, given

data signals —some of them digital (i.e.,

mobile users often delete cookies,12 and,

that mobile is still a relatively small part

online search behavior, genre of content

therefore, the connection between expo-

of the mix, marketing-mix modeling

sure and sales might be lost.

often suffers from what is referred to

in which the advertisement appears), and

return on ad spend.”

some physical (i.e., location history and

• To measure mobile in-app advertis-

as the small-reach problem.13 If, on the

movement patterns)—and their impact

ing (which comprises 90 percent of the

one hand, only a small fraction of peo-

on marketing KPIs in relation to their

mobile advertisements and does not

ple is reached and the impressions are

premium.

accept cookies), it was important to con-

fairly evenly spread across the country, a

nect a device ID to a person.

marketing-mix model unlikely will pick

CASE-STUDY METHODOLOGY

• Because the ultimate action from the

up the impact purely, because the impact

measurement would be a detailed media

occurs among a small percentage of the

  Davis, W. (2013, January 23). “Study: 44% of Adults Opt Out of Targeted Ads, 66% Delete Cookies.” From MediaPost website: https://www.mediapost.com/publications/article/191809/study-44of-adults-opt-out-of-targetedads-66-d.html.

13   Sequent Partners. (2013, July). “Current State of Marketing Mix Models: A Report for the Council for Research Excellence.” Retrieved May 30, 2017, from http://sequentpartners.com/wp-content/uploads/2016/11/CRE-ModelingWhite-Paper-CRE-Branded.pdf.

The MMA research team evaluated a wide range of vendors in search of a method that would best address the goals of the current case-study research program. In particular, the method needed to

12

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How Brands Can Make Smarter Decisions in Mobile Marketing

Table 1 Test Requirements and Methods’ Capabilities Comparison Requirement

Media-Mix Modeling

SMoX/Marketing Evolution Method

Measure detail down to the individual tactic and message

No

Yes

Measure at a speed at which insights could be acted on while the campaign was still live

No

Yes

Ability to link behaviors of individuals across devices (e.g., exposure on mobile, sale via call center)

No

Yes

Ability to overcome the small-reach problem and use control–exposed measurement for clear read on causality or validation of models

No

Yes

Ability to measure the effect of advertising on consideration and other perceptions over a long purchase cycle

Possible, but not with the same precision as person-level analysis

Yes

Ability to measure the relationship between brand perceptions and purchase behavior

No

Yes

Method can track person exposures and behaviors over time, without losing data as a result of cookie deletion

N/A

Yes

Use of specific physical location data (spatiotemporal analysis)

No

Yes

Output of analysis directly connected to person-level targeting, digital buying segments, and other media implementation

No

Yes

Note: SMoX = Smart Cross-Marketing Effectiveness Research.

overall population. On the other hand,

use of design of experiments with logistic

advertising servers to target people with

when it is possible to run an experiment

regression and elastic net regularization.

known identities to run experiments.

in which those exposed and those given

Specifically, the approach combines

Working with personally identifiable infor-

a control can be known, one can put a

logistic regression statistical analysis—to

mation (PII) safe-harbor identity-matching

magnifying glass on the small percent-

isolate the contribution of media expo-

firms (e.g., LiveRamp and Neustar), Mar-

age of people and see the impact that was

sure as it applies to driving sales (or other

keting Evolution and MMA enlisted

missed with a mix model. Although any

KPIs)—with controls for behavioral and

mobile-advertising platforms to integrate

analytic technique can be adapted to deal

other demographic characteristics in the

identity data in a PII-protected way. This

with special cases, marketing-mix mode-

sample. The model’s elastic net is applied

allowed the marketer to measure tactics

ling was not an obvious fit for this study’s

to identify the advertising messages and

for which the reach would have been too

requirements.

the people influenced by those messages.

small to measure with other methodolo-

This information is used to optimize mes-

gies. The approach accordingly overcame

An Analytics-Driven Approach:

sage rotation, targeting, and, ultimately,

the small-reach problem that would have

People Data Focus

media mix. Experiments are used to vali-

plagued mix-modeling measurement with

The MMA researchers selected Marketing

date the model and to measure small-reach

targeted exposed–control media delivery.

Evolution’s ROI Brain™ analytic platform

activities.

This field experiment approach had the

to conduct the omnichannel attribution

combined benefits of a laboratory experi-

and optimization analysis for each case

Addressing Small Reach

ment’s test–control design and of real-

study. The person-level analysis allowed

Although the method included many of

world interactions with other media and

measurement of every message in every

the capabilities the researchers desired,

marketing activity.

medium and included location analysis,

the small-reach problem was a concern

The role of individual-level data and the

brand-perception measurement, and sales

with respect to measuring certain tactics,

use of PII safe harbors simplifies the abil-

measurement. An expanded measurement

especially in mobile. The vendor thus

ity to merge data from different sources.

model, furthermore, integrated continuous

revived the technique of connecting with

This approach, moreover, can lead to the

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How Brands Can Make Smarter Decisions in Mobile Marketing  thearf.org

creation of detailed datasets that include media-exposure inputs along with behavioral and attitudinal outcomes, including sales data or store visitation. Without this approach and the ability to integrate mobile media sellers, link device IDs to people, and target them with specific media, the researchers believe they could not have measured mobile in-app advertising accurately. By using this analytics-driven, individuallevel data approach, the researchers could overcome some of the limitations of traditional marketing-mix model methods. Additional adjustments were made to address some of the unique challenges of

Note: An MMA-derived index measures the relative efficiency of each advertising medium as it drives consideration or sales per advertising dollar spent. Consideration measured respondents’ answers to a survey question, while sales were measured by direct matching to Allstate data. Source: SMoX Allstate study.

Figure 2 Allstate: Relative Efficiency of Mobile Formats Versus Campaign Average (Index Measures)

mobile (e.g., cookies) and to measure tactics that typically have too small a reach to be

media approaches of marketing to con-

number of people “reached” by each

measured in the field with other analytic

sumers in a high-involvement, infrequent-

medium (as measured by cost per 1,000

techniques (See Table 1).

purchase category—in this case, home

impressions on a web page [CPM]) but

and auto insurance. Given the high brand

in relation to how these media influenced

RESULTS

awareness of most established brands in

the actual KPIs of each campaign. In that

In this section, the authors address their

the industry, the two main communication

sense, the impact of advertising that was

research questions by comparing results

opportunities for Allstate were

attributed to each medium was divided by

from case studies of three different advertisers (references to the earlier AT&T and Walmart studies add further context): • Allstate, for which the product (insurance) requires high-involvement purchase decisions;

the cost of these media. The authors meas• to reinforce brand image and build

ured impact by comparing the lift between

consideration among broader groups

the exposed group and the control group.

of consumers, who might not be in the

For the purposes of confidentiality, the

market at this very moment but would

authors agreed not to reveal actual cost and

be soon in the future;

sales information for each brand. Instead,

• to identify consumers at the right

they calculated an efficiency index for

• QSR—fast food—a frequent, low-

moment—when they are in the market

each study, to illustrate how different tac-

involvement purchase, for which driv-

for auto insurance—and use media to

tics performed without releasing sensitive

ing consumers to physical locations is

trigger immediate response and drive

information (See Figure 2). The campaign

key;

acquisition.

average includes all the media—digital

• Unilever’s Magnum Ice Cream, a lowinvolvement impulse brand. RQ1

and traditional—in relation to the specific Allstate’s media approach combined high-

KPI (brand consideration versus sales).

impact messaging to drive consideration

The findings of the study validate the

Path to purchase: How can

(at the top of the path-to-purchase funnel)

overall media approach used for Allstate

a brand most effectively use

and more direct, response-focused mes-

(combining high-impact messaging with

mobile-platform options to

saging for acquisition and sales (at the

direct response) and suggest the following

engage consumers across the

lower part of the funnel). Within that con-

for mobile:

funnel?

text, Allstate wanted to understand better how to utilize its existing mobile-platform

Allstate Study Findings

options.

• At the top of the funnel (influencing consumers’ perceptions and driving

The Allstate study methods were repre-

The current authors defined “media

consideration for brand), the richer

sentative of the specific conditions and

efficiency” not simply in relation to the

mobile formats (audio and video) June 2017  JOURNAL

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How Brands Can Make Smarter Decisions in Mobile Marketing

Efficiency Index on Store Visitaon Targeted Mobile Display Social Mobile Mobile Video Total Media (digital and traditional)

357 127 62 100

Note: Index measures the relative efficiency of each media’s ability to drive foot traffic for QSR marketer per advertising dollar spent. Foot traffic was passively tracked and matched to ad exposures. Source: SMoX QSR study.

video, mobile video, and television). And although mobile overall emerged as the most efficient driver of foot traffic compared to other media (digital and traditional), not all mobile formats performed the same. (See Figure 3). Among the QSR study findings: • Targeted mobile display placements emerged as the most efficient driver per advertising dollar spent for both Allstate and the QSR. The authors propose that recency and context of message were

Figure 3 Fast-Food (QSR): Relative Efficiency of Media Compared with Campaign Average

more important drivers of conversion, which is why targeted mobile display and radio ranked at the top of MMA’s efficiency index.

resulted in greater ROI (See Figure 2).

In other words, a simple, direct-response

• Richer video experiences (in mobile or

When the authors accounted for the

banner message, delivered to those con-

television) were not as efficient, given

number of people affected in “brand

sumers who already were in the market

that the message was easy to commu-

consideration” by each type of mobile

for auto insurance, was a more efficient

nicate and there was preexisting aware-

format and factored in the cost of buy-

approach for driving short-term sales. This

ing the specific media, they found the

was the case despite the proven effective-

• Using location and other data signals

following:

ness of mobile video (Snyder and Garcia-

allowed the QSR company to target key

—— Mobile audio was about 30 percent

Garcia, 2016) to shift perceptions and drive

segments of consumers who much more

consideration for the brand.

likely would respond positively to the

more efficient than the campaign average (which included all media,

ness about the promotion.

promotional offer, such as commuters,

including television) for driving con-

Fast-Food Chain (QSR) Findings

who tend to travel the same route every

sideration for Allstate.

The results from the QSR research were

day. Despite the additional cost of data

—— Mobile video was 85 percent more

directionally aligned with the Allstate

to improve the targeting of these place-

efficient than the campaign average,

study—sharing a marketing goal for lower

ments, advertisements that were served

given its very high effectiveness and

funnel conversion. But fast food, a low-

to these segments were more than five

lower cost compared with television.

involvement category, is a very different

times more cost effective at driving

• At the bottom of the funnel (driving

market from insurance, requiring differ-

foot traffic than the campaign average.

sales), targeted mobile banner units

ent metrics to evaluate mobile marketing

Similarly, targeting a segment of coupon

emerged as more efficient—by 12 per-

effectiveness (foot traffic versus sales for

users (based on personal level data that

cent, on average—compared with the

insurance). The QSR tested a promotional

showed these consumers had redeemed

campaign average. In this case, the

campaign for choosing a handful of menu

coupons in the recent past), performed

analysis took into account the total sales

options at a discounted price. The specific

almost as well—about five times higher

that were attributed to each medium

promotion had preexisting awareness from

than the average.

and the cost of buying the media. By

a previous launch. The goal of this cam-

contrast,

paign was to use the same promotional

The most important result came in deter-

—— mobile video appeared less efficient

platform in order to drive additional foot

mining the optimal allocation for mobile

compared with the campaign average

traffic to various restaurant locations.

in this QSRs campaign media mix. The

(index of 76), and

Similarly to the Allstate campaign, a

researchers took into consideration the per-

broad mix of advertising media was used

formance of the various mobile tactics and

(targeted mobile display, radio, desktop

its contribution to the total, as it was derived

—— mobile audio was close to the average (index of 98).

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Allstate, AT&T, and Unilever: Size, Depth, and Repetition Findings Video emerged as a strong driver of consideration for Allstate, but not all video units and assets performed the same. Length and placement made a difference, but mobile video and mobile audio showed varied outcomes of driving conNote: Index measures the relative efficiency of each media’s ability to drive consideration for Allstate per advertising dollar spent. Consideration was measured by a survey question. Source: SMoX Allstate study.

sideration, in relation to the length of the creative asset that was used (See Figure 4). The authors proposed that because attention span is shorter for mobile adver-

Figure 4 Allstate: Relative Efficiency of Video and Audio by Length

tising, marketers could expect stronger results if they can articulate their product or brand story in a shorter time. A 15-second commercial was more efficient, particularly in the case of mobile video. The gap between 15 and 30 seconds was a lot larger when it came to mobile video. This possibly suggests that when consumers were watching video on their phone, they had a lower tolerance for longer commercial interruptions. For mobile audio advertising, the 15- to 30-second difference

Note: Index measures the relative efficiency of each media’s ability to drive purchase intent for Unilever’s Magnum per advertising dollar spent. Purchase intent was measured by a survey question. Source: SMoX Unilever study.

was less significant, likely because a listening activity does not require consumers to hold the device in their hands. Size and scale mattered in different ways

Figure 5 Unilever Magnum: Price and Effectiveness Comparison Of Large and Small Mobile Banners

in the Allstate, AT&T, and Unilever campaigns. Although shorter appeared to be more effective for mobile video, bigger was better for mobile banners. The authors com-

by the logistic regression analysis. They also

because this specific “use case”—combi-

pared two different-sized banners that were

compared the cost of mobile to other media,

nation of product category (low involve-

tested in the Unilever Magnum Ice Cream

and they ran various optimization scenarios

ment, frequent purchase), campaign

study (See Figure 5). The results showed

in order to understand the optimal media

message (simple, promotional, immediate)

that the larger banner, by far, was more

combinations that would maximize the per-

and KPI (foot traffic)—uniquely played to

effective and efficient—the most dramatic

formance of the QSR campaign.

mobile’s strengths. Nevertheless, part of

difference observed among the “bigger is

The researchers recommended for the

the upside also came from the ability of the

better” pattern found among the other stud-

QSR campaign an estimated optimal

company to experiment with various data

ies in this research program. A possible rea-

allocation of 33 percent of the media mix

signals and test innovative tactics and data

son for this is the type of product category

to mobile—significantly higher than the

combinations.

(impulse) and the way the advertisement

actual allocation that the company had

real estate was utilized to create desire with

used, (mid-20s percentage), and the high-

RQ2: Size, depth, and repetition: How

impactful imagery. By contrast, the differ-

est recommended allocation among all

can a brand most effectively

ence in banner size effectiveness that was

previous studies in this program. The

communicate a message on a

reported for telecoms (AT&T study) was

authors proposed that this was largely

mobile platform?

much lower, although still compelling, June 2017  JOURNAL

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How Brands Can Make Smarter Decisions in Mobile Marketing

advertising, because the results are not completely consistent across all of the brands. The preponderance of findings, however, suggests that optimal ROI is achieved with less frequency with mobile video and other highly noticeable advertising units. This might be explained by the engagement level with video consumed on a mobile screen, and it is consistent with empirical research on matters of frequency. Note: Index measures the relative efficiency of each media’s ability to drive consideration for Allstate per advertising dollar spent. Consideration was measured by a survey question. Source: SMoX Allstate study.

An Advertising Research Foundation study in 2016 found that “while impression levels delivered to consumers via digital can range in the hundreds per month,

Figure 6 Allstate: Relative Efficiency of Targeting Approaches In Mobile Video

more than 40 impressions per month can actually have a negative impact on a brand’s goals” (Snyder and Garcia-Garcia, 2016, p. 361). RQ3

Targeting: What is the value of different data signals, digital or physical, for improving targeting in mobile?

Allstate, Unilever, and Walmart Targeting Strategies Allstate tested a variety of targeting approaches leveraging a wide spectrum Note: Index measures the relative efficiency of each media’s ability to drive sales for Allstate per advertising dollar spent. Sales were measured by direct matching to Allstate data. Source: SMoX Allstate study.

of data signals, both digital and physical. There were differences in the impact of those approaches, depending on the KPI (consideration versus actual sales).

Figure 7 Allstate: Relative Efficiency of Targeting Approaches In Mobile Banners

The researchers compared the efficiency of behavioral targeting and contextual targeting approaches used to deliver mobile video to the right consumers and

with the larger unit being more than twice as effective as the smaller unit. Findings from Allstate, Unilever, and

• Larger size is better, especially for

drive consideration for Allstate (See Fig-

image-driven categories, when it comes

ure 6). The study demonstrated that both

to banners.

approaches justified their incremental cost

AT&T suggest that different rules apply

and improved the performance of mobile

when it comes to using mobile video and

“If bigger generally is better,” the research-

video. The authors specifically found the

mobile banners to deliver a message.

ers asked, “is more also better?” The

following:

impact of frequency was different for • Shorter clearly is better for video and is

banners versus video and other, richer

• Brands used contextual targeting to

preferable, although not as much of an

advertising units. More research is needed

identify consumers who were brows-

issue, for audio.

into the optimal frequency and pacing of

ing relevant content about the category

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How Brands Can Make Smarter Decisions in Mobile Marketing  thearf.org

• By contrast, when historical location data were used to define an audience (of past shoppers), the impact on store visitation was lower for the expandable (larger) advertising unit, and there was zero incremental impact for the small, static banner (“pencil unit”). Whereas Allstate and Walmart used locaNote: Store visitation lift was measured using actual foot traffic data. Advertiser did not approve disclosure of the exact lifts, only the directional findings illustrated above.

tion data to define audiences (for Walmart, to target consumers relative to their proximity to a store), Unilever focused on the

Figure 8 Walmart: Store-Visitation Lift Caused by LocationTargeting Approach and Advertisement-Unit Combination

weather conditions in a given location. For Unilever’s Magnum Ice Cream, mobile banners, even with no weather targeting, had emerged as an efficient driver of

on their mobile devices. This approach

for video advertisements, did not really

brand awareness (75 percent more efficient

improved the results of mobile advertis-

move the needle on sales when it was

compared with the campaign average; See

ing by more than 90 percent.

used with mobile banners. The authors

Figure 9). There was no impact in terms of

• Brands also used behavioral targeting,

proposed that this approach was more

driving short-term sales in the target group

relying on first-, second-, and third-party

suitable for upper-funnel metrics (e.g.,

of that campaign, which resulted in zero

data about key behaviors that illustrate

image, consideration), especially when

ROI. When the same advertisements were

that a person is in-market to buy home

matched with a rich experience, such as

served targeting locations where the out-

mobile video.

door temperature was above 80°F, however,

or auto insurance. This approach further improved results of mobile video

• Location targeting emerged as another

the results changed, and a sales impact was

advertising by more than three times,

efficient driver of sales, next to retarget-

measurable in the short term. The reason

compared with when no behavioral tar-

ing and behavioral targeting. In this case,

for the drastic turnaround, the researchers

geting was applied.

it improved the results by two times.

believe, was relevance.

This is particularly important, because it

The

weather-targeting

approach

The authors also compared the efficiency

means that marketers can utilize physi-

ensured that the mobile banners matched

of various targeting approaches that were

cal data signals, such as location data,

a relevant “need state” predicted by

used to deliver mobile banners, with the

in order to define elaborate audiences

the high temperature. Combined, the

ultimate goal of driving sales (See Figure

and expand the reach of targeting “in-

weather-targeting strategy and a message

7). Behavioral targeting emerged as the

market” consumers beyond the behav-

of indulgence led the mobile banners to

most powerful tool to identify consum-

ioral and retargeting approaches.

become one of the most efficient drivers

ers in the right moment when they are

of short-term sales—about 50 percent

in-market for auto and home insurance

The findings added to knowledge (from

more efficient in relation to the campaign

and to drive acquisition. Retargeting (i.e.,

the previous case studies in the current

average. Recall that the analytic platform

targeting consumers who previously were

program) about location-targeting meth-

methodology employed in this research

interested in Allstate) also delivered great

ods for driving foot traffic to physical store

program includes use of control groups.

results, more than twice the return as when

locations. The Walmart study (See Figure

This enabled the researchers to assess

it was not applied.

8) specifically suggested the following:

that the same advertisement, when served

The authors, moreover, found the following: • Contextual targeting, although an efficient driver of consideration when used

without weather targeting, had not gener• The strongest impact on foot traffic came

ated a sales lift compared with the control.

from proximity targeting when combined

This suggests that it was the combination

with expandable banner units, generating

of the mobile advertising and weather tar-

the strongest lifts in foot traffic.

geting that generated sales. June 2017  JOURNAL

OF ADVERTISING RESEARCH  457

How Brands Can Make Smarter Decisions in Mobile Marketing

The interaction between need state and advertising is not a new marketing concept. Yet traditional media are limited in their ability to be present in places and times where these need states materialize—for instance, the increased desire for ice cream when temperature goes above 80°, as in the Magnum Ice Cream example. In these situations, mobile advertising has a unique advantage because of the pervasiveness of mobile phones, which

Note: Index measures the relative efficiency of each media’s ability to drive awareness or sales for Magnum, per advertising dollar spent. Awareness was measured by a survey question, while ROI takes into consideration the estimated profit due to media, using actual sales lift analysis. Source: SMoX Unilever study.

allows marketers to act on their consumer insights and engage consumers in the moment. BEST PRACTICES IN MOBILE MARKETING The findings from this body of research can serve as a guide to marketers who

Figure 9 Unilever Magnum: Relative Efficiency of Mobile Banners in Relation to Campaign Average

want to improve the effectiveness of their mobile-advertising investments. Although

of the above levers “in-market” through

take into account the impact of advertis-

the rules for mobile-marketing efficiency

programmatic advertising. Starting with a

ing and how it drives their campaign KPIs

can be applied in different ways depend-

broader menu of possible ways to engage

when considering cost. Most of the data-

ing on the brand, product category, and

with mobile-specific content, advertis-

targeting approaches assessed in the cur-

medium, the authors suggest the follow-

ers can run massive experiments with

rent case-study series more than justified

ing best practices:

dynamic creative optimization, measure

their premium (cost) and significantly

what works, and optimize accordingly

improved the impact of mobile advertis-

while the campaign is live.14

ing per dollar spent. This validates the

Produce Creative Content

theory that marketers can find great value

Specifically for Mobile Mobile’s smaller screen offers the oppor-

Target More Deeply

in understanding the customer journey

tunity for more purposeful engagement,

Mobile creates a much richer dataset than

and operationalizing planning insights to

so repurposing creative content from other

traditional media, allowing marketers

target “moments of relevance” across the

platforms misses the opportunity to fully

to capture the context, intents, and need

customer journey. In those instances, the

leverage context and customize accord-

states of individuals. The historic location

results from applying such targeting not

ingly. Previous research provided evidence

of a consumer can reveal a great deal about

only are significantly greater but also cre-

about the benefit of unifying creative strat-

his or her passion points (visited a golf

ate a “multiplier” effect and drastically

egy across different platforms but also cus-

course, went skiing, attended a live music

change the impact of a given creative asset.

tomizing execution to the platform level

performance), income (lives in an afflu-

(Snyder and Garcia-Garcia, 2016). The cur-

ent neighborhood), or even current need

Adapt to Continuous Optimization

rent research validates that marketers can

state (find out today’s weather). Unifying

Mobile targeting optimization may prompt

maximize the impact of their advertising

and activating these data potentially can

marketers to ask, “Can we act fast enough

when they align creative concept, format,

increase the actual CPM paid by advertis-

on the optimization insights?” Analyt-

advertising unit, data, and delivery to com-

ers when targeting broader demographic

ics technology does the heavy lifting, yet

municate a brand message that is relevant

audiences. Advertisers, however, should

the practice of changing in the middle of

in the specific moment in the customer

14

journey. Alternatively, marketers increasingly can assess the impact of multiple combinations

458  JOURNAL

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  Fondon, C. J. (2014, October 20). “Dynamic Creative Optimization: What is it?” Retrieved May 26, 2017, from ProgrammaticAdvertising.org’s website: http://programmaticadvertising.org/2014/10/20/ dynamic-creative-optimization-what-is-it/.

a campaign might be new to a marketer. Once a marketer begins to optimize mobile while the campaign is live, he or she might want to optimize as many other media as

How Brands Can Make Smarter Decisions in Mobile Marketing  thearf.org

possible. And, as data-analysis and meas-

typically requires a year or more of obser-

the previous studies, these marketers will

urement technology continue to evolve,

vation. Each case study is a single cam-

explore “unique-to-mobile opportunities,”

the issue becomes marketers’ and agency

paign, and, therefore, the benchmark

including:

orientation toward agility. This research,

conversion applied is based on the ven-

therefore, has implications for how a mar-

dor’s database. The database of norms

• the impact of “mobile-first” creative

keter organizes teams and agencies around

might not be applicable directly to any

content and its impact on ROI. This

continuous optimization, as opposed to an

single brand and, as a result, might not

annual planning cycle.

will include the impact of “nonworking

provide definitive answers of long-term

cost”—in other words, the cost of pro-

effect for all marketers. More emphasis is LIMITATIONS

placed on the sales benefit achieved within

In the authors’ view, the biggest limita-

one purchase cycle, and some might view

tion to the current research program is the

this as a limitation.

match rate of identity data. With a current rate of 40 percent and growing, researchers

FUTURE RESEARCH

should be careful to examine whether scal-

More work is needed to better understand

ing up the 40 percent to 100 percent of the

the increasing variety of mobile tactics at a

population aligns with total sales. If so, the

more granular level:

identity data that can be matched can be viewed as representative, and a marketer

• Factors such as auto-play versus opt-in,

can have more confidence in the findings.

sound-on versus sound-off, and skip-

If not, a researcher can consider weighting

pable versus nonskippable advertising

but will need to understand whether cer-

sometimes are intertwined with vari-

tain groups are misrepresented and assess

ables, such as video length and target-

the impact of specific variables to achieve a

ing method applied, creating an infinite

projection that aligns with total sales.

number of combinations.

In the current case-study series, impact

• Location targeting sometimes is viewed

was measured for both brand equity

as “proximity targeting,” or geofencing,

and sales behavior, but might it miss the

and solely is associated with market-

longer-term impact of advertising? The

ers who want to drive foot traffic to a

method of the primary vendor selected

physical location. There are promising

for this study series (Marketing Evolution)

opportunities, however: using histori-

for quantifying the relationship between

cal location patterns to define elaborate

brand-equity measures and a person’s

audiences around behaviors (e.g., people

purchase activity for months, and in some

who commute using a specific route),

cases years, has been applied to customers

passion points (e.g., people who go to

in the automotive, retail, and financial-

music venues), or even income segments

services categories. Within each sale, the

(e.g., affluent consumers who go to ski

method allows the researchers generally

resorts). Some of these location-targeting

to trace back to a set of beliefs about the

tactics are assessed further in the context

brand. When advertising enhances these

of the QSR study.

beliefs, sales follow. The researchers refer

ducing the actual creative assets, which often is left out of media-efficiency analysis. • using mobile for integrated communications: how mobile can enhance television or out-of-home advertising and the role of creative message and sequencing. This could build on knowledge from a 2016 Advertising Research Foundation study that found a “kicker effect” when digital-advertising investment was added to television, resulting in an ROI increase of about 60 percent (Snyder and Garcia-Garcia, 2016). Finally, there is an interest in further examining social media and other “walled gardens” that account for a large share of the mobile-advertising spend. Some work in this case-study series (Unilever and the QSR) provides isolated cases of measuring some of these platforms, but further investigation is needed. As the industry conversation about transparency and data sharing continues to evolve, marketers will get better at predicting what is feasible and how advertising spend in these platforms will be assessed in the years to come.  ACKNOWLEDGMENTS The Mobile Marketing Association thanks

More MMA-led studies will follow,

the following companies that support

The current research program meas-

including Ford Motor Co. and Miller-

its Smart Cross-Marketing Effectiveness

ures brand beliefs as well as sales and

Coors (results expected in 2018). Work

Research (SMoX): Jun Group; PlaceIQ;

can apply a formula of brand beliefs and

with a major bank and leading fashion

The Weather Company; xAd, Inc.; ESPN;

their conversion to longer-term sales and

retailer also is in progress. Beyond the

Foursquare; Pandora; Turner; Ubimo; and

profits. Arriving at this formula, however,

more granular questions addressed in

Verve.

to these measures as leading indicators.

June 2017  JOURNAL

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How Brands Can Make Smarter Decisions in Mobile Marketing

About the authors

Briggs, R., R. Krishnan, and N. Borin. “Inte-

Research Agenda.” Journal of Interactive Market-

grated Multichannel Communication Strategies:

ing 34 (2016, May): 3–14.

Vassilis Bakopoulos is the head of insights and research

Evaluating the Return on Marketing Objec-

for the Mobile Marketing Association in New York

tives—The Case of the 2004 Ford F-150 Launch.”

Hirshleifer, D., and T. Shumway. “Good Day

City. Previously, Bakopoulos worked for Digitas and

Journal of Interactive Marketing 19, 3 (2005): 81–90.

Sunshine: Stock Returns and the Weather.” Jour-

for Kantar’s Added Value and Research International

nal of Finance 58, 3 (2003): 1009–1032.

divisions in New York and Europe, specializing in

Chang, Y., and Thorson, E.“Television and Web

advertising effectiveness, customer segmentation, and

Advertising Synergies.” Journal of Advertising 33,

creative strategy. His research has been published in the

2 (2004): 75–84.

Journal of Applied Marketing Analytics and the Journal of

Dijkstra, M. “An Experimental Investigation

Brand Strategy.

of Synergy Effects in Multiple-Media AdvertisJohn Baronello is director of marketing analytics at Allstate Insurance in Northbrook, IL. His career of nearly 20 years has focused on developing analytics solutions for organizations’ use of data to change customer behavior and grow revenue. Before joining Allstate, Baronello held senior roles at Cardinal Path and Walgreens, developing advanced retail analytics.

ing Campaigns” (doctoral dissertation). Tilburg University, 2002. Dijkstra, M., H. Buijtels, and W. F. van Raaij. “Separate and Joint Effects of Medium Type on Consumer Responses: A Comparison of Televi-

Hofacker, C., P. Manchanda, K. D. Ruyter, J. Donaldson, and N. Lurie. “Gamification and Mobile Marketing Effectiveness.” Journal of Interactive Marketing 34 (2016, May): 25–36. Laroche, M., I. Kiani, N. Economakis, and M. O. Richard. “Effects of Multi-Channel Marketing on Consumers’ Online Search Behavior: The Power of Multiple Points of Connection.” Journal of Advertising Research 53, 4 (2013): 431–443.

sion, Print, and the Internet.” Journal of Business Research 58, 3 (2005): 377–386.

Molitor, D., P. Reichhart, and M. Spann. “Location-Based Advertising: Measuring the

Rex Briggs is founder and chief executive officer of

Edell, J. A., and K. L. Keller.“The Information

Impact of Context-Specific Factors on Consum-

Marketing Evolution, a New York City-headquartered

Processing of Coordinated Media Campaigns.”

ers’ Choice Behavior” (working paper). Munich,

analytics firm specializing in omnichannel marketing

Journal of Marketing Research 26, 2 (1989):

Germany, Ludwig Maximilian University, 2014.

attribution and optimization. Briggs’s work can be found

149–163.

in the Journal of Advertising Research and the Journal

Moran, G., L. Muzellec, and E. Nolan. “Con-

of Interactive Marketing, among other publications. He

Enoch, G., and K. Johnson.“Cracking the Cross-

sumer Moments of Truth in the Digital Con-

is the coauthor of What Sticks: Why Advertising Fails

Media Code: How to Use Single-Source Meas-

text.” Journal of Advertising Research 54, 2 (2014):

and How to Guarantee Yours Succeeds (New York:

ures to Examine Media Cannibalization and

200–204.

Kaplan, 2006), and he serves on the editorial board of

Convergence.” Journal of Advertising Research 50,

ESOMAR’s Research World magazine. 

2 (2010): 125–136. Fulgoni, G., and A. Lipsman. “Digital Game

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