Poole Artificial Intelligence and Media Buying 051516

Running head: LUCY: IBM WATSON ANALYTICS MEETS MADISON

AVENUE

Lucy: IBM Watson Analytics meets Madison Avenue –

Considering Cognitive Computing Artificial Intelligence Tools

Employed for Advertising Media Planning and Buying

by

James Raphael Poole

Project Committee:

Melissa Goodson, Sponsor

Joanne Smith, Reader

Approved:

Submitted in partial fulfillment of the requirements for the degree of Master of Business

Administration, The College of St. Scholastica, Duluth, Minnesota.

LUCY: IBM WATSON ANALYTICS MEETS MADISON AVENUE 1

Acknowledgements

This project would not have been possible without the extraordinary access and counsel

provided to me by Equals 3 Media. In particular, I want to recognize their CIO, Marc

Dispensa, and their CEO, Dan Mallin. They are brilliant marketers and fearless

entrepreneurs in the embrace of innovative technology. I am grateful to them for having

created the opportunity for this research. In addition, Dr. Donald Wortham, now

President at Hodges University , in Naples, Florida, when he was Vice President, Strategic

Initiatives at The College of St. Scholastica, was my first sponsor and encouraged and

guided this work’s gestation. Dr. Melissa Goodson has been an insightful reader and

advisor of my direction, since Dr. Wortham’s departure from The College of St.

Scholastica. Joanne Smith was my reader who imposed a coherent style on my writing. I

am eternally grateful to my dear wife, Deborah Leuchovius, who has been the rock on

which I have built my intellectual explorations since we met as undergraduates 42 years

ago, and to our son, Fred, who inspires me to be persistently curious, to try what I

haven’t, to never give up, and to perform what I do with grace and joy. I thank my

mother, Helen Poole, who, at 97, never stops working for others and encourages me to

enjoy my work but to get it done.


Abstract

This project examined how an artificial intelligence service can help marketing

professionals to create more effective and efficient media plans free from human

biases. The paper began with a review of the advertising media planning process and

looked at current media buying practices. It examined the potential for human biases to

influence optimal media buys, especially those caused by interpersonal relationships

between media buyers and media sellers. Artificial intelligence as an evolving

management support was described and analyzed. The power of artificial intelligence to

support market research, market segmentation, and media plan development was

examined. Finally, an alternative to traditional media buying processes, a process

employing artificial intelligence in regards to effective targeting based on the

consideration of vast amount of data and the elimination of human bias, was described in

detail.


Table of Contents

Literature Review ................................................................................................................ 6

Advertising And The Role Of Media Planning And Buying .................................. 6

How Advertising Works .......................................................................................... 6

The Advertising Agency’s Planning Process ........................................................ 12

Role of Advertising Agency in Curating Meaning of Value ................................. 13

Issues at the Interface of Advertisers and Media Buyers ...................................... 14

How Advertising’s Media Buying is Measured .................................................... 15

Increasing Complexity of Media Buyers’ Challenge ............................................ 16

Counter-indicators to Traditional Media Buys ...................................................... 19

By Comparison: Lucy’s Media Buying Model ..................................................... 20

Artificial intelligence ............................................................................................. 21

How Watson Came to Be ...................................................................................... 23

How Watson Works ............................................................................................... 23

The Promise: Decisions Made Without Bias ......................................................... 25

Discussion .......................................................................................................................... 26

Conclusion ......................................................................................................................... 33

References ......................................................................................................................... 35


Lucy: IBM Watson Analytics meets Madison Avenue –

Considering Cognitive Computing Artificial Intelligence tools

Employed for Advertising Media Planning and Buying

The purpose of this project is to compare how media buyers make the complex set

of decisions that result in a media buying plan and how a media planner might use

artificial intelligence (AI) to develop a media plan. In this case, the artificial intelligence

is “Lucy,” the brand name used by Equals 3 Media, a marketing technology company in

New York that developed a media solution using IBM Watson Analytics’ (Watson) vast

computing power and language APIs to provide an optimized media campaign planning

process. Lucy employs Watson’s AI tools with the intention of increasing persuasive

marketing insights, decreasing decision-making time, improving accomplishment of

marketing objectives, reducing the effect of bias, and increasing confidence in the

efficacy of a brand’s media spend. It has been announced that Equals 3 Media is using

their Lucy solution for some of Havas Media’s clients’ major brand campaigns

(Yudelman, 2016).

In September 15, 2015, trade publication eMarketer reported, “In 2015, total

worldwide ad spending will reach $569.65 billion” (Anonymous, 2015). Nearly $570

billion dollars is a significant amount of money. How accurately those media buys

achieve their goals is a non-trivial problem. Media buys have been made based on

complex algorithms applied to the historic performance of a medium and vehicle given

what is known about a product’s specific audience and their purchasing behaviors and

media consumption. The exact relation between volume of media spending and increased

sales, while assumed to be knowable, is often less than optimally correlated. Since media


spending represents a large cost, a service that could improve efficiency and efficacy

would represent a significant benefit to brand marketers.

In the following review of the relevant literature, I will look at what advertising is,

how it works, the advertising agency’s media planning process, how media buying is

measured, and some of the factors that influence objective media buying. By way of

comparison, I will consider an alternative to traditional media buying, that is, Lucy, her

conceptual model, artificial intelligence is, how IBM Watson’s artificial intelligence came

into being, how it works, the promise it makes of media buying decisions made without

bias.


Literature Review

Advertising and the Role of Media Planning and Buying

Aaker and Stayman (1992) developed the concept of transformational advertising,

which argues that in addition to informational or emotional, advertising can be “trans-

formational” meaning that it works by helping to develop associations with the use

experience and amplify that experience into something that it otherwise would not have

become. Advertising that is transformational can work to manage how viewers of the

advertising learn from their product use experiences. It may tailor the post-use attitudes

about the experience. It may create, alter or intensify the feelings that actually occurred

during use and thereby have a significant impact on resultant satisfaction (Aaker &

Stayman, 1992). Projective techniques developed for use in clinical psychology may be

useful in the development of transformational advertising. It may take many repetitions

for transformational effects to emerge, and there may be a lag between the viewing of the

advertising and the effect. As a result, transformational advertising may be difficult to

measure (Aaker & Stayman, 1992). For anyone to be affected by advertising, they would

have to first perceive it. Media placement is the science of choosing into which vehicles

ads should be placed so that they may be viewed by their intended audiences, the creative

can have its intended effect, and they would execute the intended action with the product.

How Advertising Works

To define terms, the “advertiser” is the buyer of the media – the company that is

ultimately paying for the advertising, the company that owns the product, service, or

brand to be advertised. The advertiser is the one for which the agency works. The media

owner is the “vendor,” and the advertising agency is an “intermediary” that may buy the


media, create the advertising messages, and control the process. The agency typically

wins the right to produce (create) and place an advertising campaign for an advertiser

through a contest process by which the advertiser evaluates the competence, thinking, and

style of each competing agency. The winning agency will act as an intermediary and

ultimate deciding agent between the advertiser and the media owners, through which it

will place the advertising it creates.

Vakratsas and Ambler (1999) posit that message content, media scheduling, and

repetition pattern are all components of “advertising input.” Human audiences have filters

(motivation, ability, level of involvement) that affect how they perceive advertising

inputs. When consumers perceive an advertising input that has passed through their

filters, they will realize a cognition, it will affect their thinking, an affect will occur, it

will move them in some way, and the totality of it will induce an experience, the end

result of the encounter with the advertising input. As a result, the consumer will exhibit a

consumer behavior involving choice, consumption, loyalty, habit, etc. which are the

consequential effects of the advertising input (Vakratsas & Ambler, 1999). In their

model, Vakratsas and Ambler (1999) note that the mediating factors of their filters and

their cognition, affect, and experience have a profound impact on the target consumer’s

response to the advertising input.

There are a wide range of media alternatives for the media buyer to choose from,

and each has a unique set of advantages and disadvantages that must be weighed. Some

of the criteria that must be considered range from the tactic’s and vehicle’s ability to

aggregate the kind of audience the marketer seeks, the particular stage in the buying

process the marketer is targeting, reach, frequency, and cost. Yet, many of the


computerized planning applications that bring the strengths of heuristic and simulation

models add complexity beyond the ability of most professional marketers to practically

manage (Coulter & Sarkis, 2006). Thomas Saaty developed the supermatrix analysis

media selection process also known as the analytic network process (ANP) (Saaty, 2006).

While powerful, the tool is practicable for media planners (Coulter & Sarkis, 2006).

When ANP is used for media planning, its primary variables consist of Quality, Time,

Flexibility, Coverage, and Cost. The alternatives that were tested against those factors

were Direct Mail, Internet, Magazines, Newspapers, Outdoor, Radio, and Television. The

product categories considered were airlines and financial services (Coulter & Sarkis,

2006). Interestingly, internet advertising had not been considered for the ANP test

because of the influence of competitors’ buying habits, the habitual tastes of management

for television and print, and reluctance to change (Coulter & Sarkis, 2006). This failure

within the experiment seems to provide evidence of the typical influence of human bias

in the media selection processes.

Consider how the advertising media planning and buying works as implied in

“Figure 1: Media Planning and Implementation Process.” I created the illustration from

my own experience in advertising agencies, based on a model from Arul (2014) from

which I freely quoted. In it, we see the advertising agency is the curator of information

from both the advertiser and the media owners. The agency develops an advertising plan,

which empowers and challenges the agency’s media and creative departments. The

creative department will create and test various campaign concepts and after approvals

will blow out the ideas into a campaign of related concepts that will be applicable in

various media. Creative will then supervise the production of the actual advertising that


will be placed. At the same time, the media department, through a series of research,

analysis, and conclusion steps will arrive at a media strategy, which will have component

strategies for target, media, reach and frequency, and timing. The media plan will place

the appropriate ads in the various media using insertion orders, which are contracts for

space associated with a price for each. Then, over time, the media department will

associate invoices with insertion orders and report progress in the campaign as well as

results. The campaign will be evaluated against the advertising objectives of the plan and

modified as necessary (Arul, 2014). In this way, ads are created, placed, and the space

used is paid for. Thereafter, spending on advertising can be compared to various metrics,

especially sales, to determine its efficacy.


Figure 1. Media planning and implementation process.

Advertising Plan

Media Planner Creative Director

(Initial Plan /

(Information

Analysis) (Setting Media

(Analysis of budget, product, creative, sales, consumer behavior, communication

plan) Media Strategy

(Target Audience

Selection) (Media Selection

Strategy) (Reach, frequency

Strategy) (Timing

Strategy) Media Negotiations, Buying Space, and Inserting Ads

Monitoring & Comparing Invoices against Insertion

Orders Evaluation of Campaign

Media Owners Agency Advertiser


Jha, Aggarwal, and Gupta (2011) have noted that promotional messages have to

be constructed and delivered through a range of media outlets in order to achieve

marketing objectives for brands, products, and services. What we call advertising consists

of those messages that use imagery, words, sound, pictures, and video delivered to their

intended audience through such vehicles as websites, online advertising, social media,

and more traditionally, billboards and other out-of-home vehicles, magazines,

newspapers, radio, television, and cable. Advertising brings messages about the

existence, attributes, and benefits of a brand, product, or service to those audiences

deemed most likely to be consumers of them (Jha et al., 2011).

The work of media planning and buying is determining how to optimally deliver a

promotional message to a target audience (Ots, 2009). The goal of media planning is to

achieve the optimum of maximum advertising effect at a minimum cost to the advertising

budget (Jha et al., 2011). What we call “media” are vehicles that deliver engaging content

to audiences and in so doing, aggregate audiences of various kinds according the

attractiveness of the content. As a result, marketers have assumed that different forms of

content necessarily attract categorically different audiences (Nelson-Field and Riebe,

2011). Audience metrics of the given vehicles are important and to decide between

vehicles, media buyers need to better understand and qualitatively appreciate the

advertisers’ consumers beforehand (Ots, 2009). Those vehicles that have proven to

aggregate audiences of certain demographics (age, gender, income, education level,

geographic location) for the advertisers will be chosen for the media plan.

A medium provides engaging content for its audiences such that they are attracted

to the medium. Advertising delivers promotional messages to the audiences aggregated


for the content on the medium. If the creative is designed to appeal to the tastes of a

medium’s consumers, they should be receptive to the advertising’s message. Testing of

various permutations of audiences, media vehicles, and buys will be evaluated and

analyzed to arrive at an optimal media mix whose audiences will be balanced between

focus and size (Arul, 2007). Media planners and buyers secure and schedule space

through a complex dynamic of scheduled messaging by market, date, and vehicle,

designed to maximize impact at the minimum expense. The product of this process is the

media plan (Ots, 2009). While media buying twenty years ago involved bidding for a

scarce supply of media time, today media outlets have proliferated to the point where

supply can no longer be called scarce except for special events (Arul, 2007).

Media buying is also lucrative to the agency. Bichard, Chambers, and Patwardhan

(2007) point out that while the most celebrated in an advertising agency is its creative

department that produces the advertising that is the public product of the agency, it is the

media planning and buying departments (for those agencies that provide them) that

produce most of the agency’s revenue over time.

The Advertising Agency’s Planning Process

The agency, during successive meetings with the advertiser’s brand managers,

will develop a plan called a creative brief that defines the audience, their tastes, how they

consume media, what the product can mean to them, what the advertising should make

them do, and the big idea that will motivate them (Ibach, 2009). While the resultant

advertising messages and their look and feel are being created and tested, presented, and

approved by the agency’s creative department, at the same time, in the media department

of the agency, the target audience’s media consumption habits will be examined and used


to decide which media vehicles to employ and how much to weight the spending on each

based on each medium’s ability to aggregate that target audience, and its reach,

availability, cost, and effectiveness (Jha et al., 2011).

Media buying functions are collaborative negotiations among client marketers,

advertising agencies, media owners and their sales representatives, and sometimes,

independent media buying companies (Arul, 2007). The rapid growth of media choices

since the pervasion of many cable channels and broadband’s access to a plethora of

websites and social media have made media planning more challenging (Arul, 2007). To

make the evaluations and decisions necessary to develop the media plan, media planners

and buyers access advertiser-gathered research, perform their own research, buy

independent third-party research, and rely on the research of the representatives of the

various media outlets they will employ. In relying on the research of the media outlets,

trust, credibility, and personal aspects of the relationships involved are factors unrelated

to the data provided that affect the decision to employ a medium, what weight to give it

in the plan, when to employ it, and how to increase exposure while discounting cost

through volume deals (Bichard et al., 2007). These factors can be sources of non-

objective bias.

Role of Advertising Agency in Curating Meaning of Value

The advertiser is the buyer of advertising and advertising media and therefore the

customer of the advertising agency. The advertising agency will define what customer

value means to the advertiser that hired it, and promulgate that idea to its subsidiaries,

whether internal department, or outsourced media buyers (Ots, 2009). If we define

customer value as the best interests of the advertiser, we may find that the various actors


in the media buying constellation have unique and different ideas about what constitute

customer value (Ots, 2009). Value has historically been defined as measurable media

characteristics such as numbers of appropriate audience reached (Ots, 2009). Various

categories of customer value (value as perceived by the advertiser) are: audience value,

vehicle value, process value, emotional and organizational value, output and effect value,

financial value, and relationship value (Ots, 2009). The agency will assign an indicator

for customer value discernable through the various metrics of rational analysis so that it

may be measured and reported (Ots, 2009). The agency will seek to demonstrate that it

has optimized the chosen metrics of value. Over the past fifteen years, more agencies’

compensation have included an incentive component, but the metrics for performance are

reported by the agencies and can be manipulated (Parekh & Bruell, 2013).

Advertisers are becoming more interested in considering the effect of the media

buy by measuring the effect in the audience – not only measuring reach and frequency

and who is in the audience of the media bought, but also taking into consideration how

impactful the advertising was. Measurement of purchase effect can be difficult because of

the lag between viewing an ad and taking action because of it, and because of

uncontrollable factors outside of the advertising that could cause or prevent sale (Ots,

2009).

Issues at the Interface of Advertisers and Media Buyers

Professional media buyers seek objective, rational, and informed choices. The

advertisers who hire them may lack the necessary depth of knowledge about vehicle

performance and may be more often guided by gut instinct and personal prejudices about

media choices (Ots, 2009). While most advertising agencies have integrated media


research, buying, and planning departments, specialized media buying services compete

with them for the advertisers’ contracts. Media sales representatives for specialized media

buying services call on media buyers with information competitive to the expertise of the

advertising agency and adjustments need to be made for their influence (Arul, 2007).

Whether integrated or independent, it may be hard for the media buyer to distinguish the

actual needs of the advertiser from the positions of the advertising agency. Agency

intermediaries tend to provide media sales representatives with more highly skilled and

informed buyers but can also insert an obfuscatory layer between the media seller and the

advertiser (Ots, 2009). Media sales representatives, with their own biases may have a

significant influence over the final decisions, media plan, and media buying. A social

bond grows up between media sales representatives and media planners, and the

personalities of the media sales representative have been shown to have consistent

influence over the media buying decision. Relationship marketing may have a greater

role in the information-providing aspect of media sales representatives than is appreciated

(Bichard et al., 2007).

How Advertising’s Media Buying is Measured

Media planners need to posit how much repetition of an ad (frequency) is

necessary to achieve the advertising objectives (Sissors, 1982). In deciding which media

to employ and how frequently run in them, media buyers need to be careful to distinguish

between advertising frequency, that is, how frequently one’s ad is looked at (which is

nearly impossible to measure, even online) and media vehicle frequency, that is, how

many exposures the media vehicle provided viewer to view the ad, also referred to as

“opportunity to see.” Effective frequency implies that some known frequency is optimal


for communicating the advertising message to the viewer, and therefore the media spend

should approach that number. Effective reach refers to the percentage of target audience

reached at an effective frequency (Sissors, 1982). Exposure to an advertising message

actually measures the audience of the vehicle, not the actual audience for a specific

advertising message. Exposure measures possibility, not reality (Sissors, 1982).

Advertisers have chosen media largely based on the vehicle’s self-reported ability to

deliver a particular audience demographic aligning with a target segment of the

advertiser. (Nelson-Field and Riebe, 2011) Media vehicle frequency can be extrapolated

from studies for comparative purposes. We need to be clear that these two topics are not

the same, although many planners act as if they were (Sissors, 1982).

Increasing Complexity of Media Buyers’ Challenge

In addition to making the media planner’s computation more complex, media

proliferation has fragmented the market for audiences. Seeking cost efficiencies, media

buyers seek to target their audience strategies, aligning the profile of the advertising

medium and vehicle to that of their segmentation. Media segmentation is akin to

traditional product segmentation processes (Nelson-Field and Riebe, 2011). Within the

total audience for the product, there are smaller cohorts of more intense consumers who

cluster into demographic groups called segments. To maximize effect on the group most

likely to consume an advertiser’s product, its audiences will be segmented so that the

most appropriate media can be selected for that and other groups (Jha et al., 2011).

Segmentation criteria include demographic factors such as gender, age, sometimes race,

income, geographic location, income level, education attainment, and certain

psychographic factors if identifiable and aggregable that may influence preference and


the desire for the product. The same product may be used differently or for different

reasons by different segments (Jha et al., 2011). Segments will be constructed in such a

way that different media can be bought so that each different segment can be aggregated

and marketed to appropriately. Different media buys will be selected for each segment.

Multi-segment marketing can become very complex. In recent years, multi-criteria

decision-making models have been developed to help employ software in the selection of

media buys. The media plan will include solutions for each segment before media buying

negotiations begin. (Jha et al., 2011)

There are other perspectives about the importance of segmentation. Size of

audience may still be the best way to differentiate among vehicles within a mass medium.

Audiences for mass programming are less easily differentiated than their sellers would

contend. Research by Nelson-Field and Riebe (2011) has shown little difference in the

performance of so-called segmented television programming, with the exception of

language-based programming, or certain special programming, compared to mass buys.

In their study, they found a “distinct absence of audience segmentation” on the basis of

types of programming. Claims of given target’s representation are often belied by more

than half of the audience being outside the target. This has led some researchers to

contend that television audiences are largely un-segment-able with few exceptions

(Nelson-Field & Riebe, 2011). If this is true, the premium paid for targeting may result in

more advertising waste than would have been obtained had the media buy been made for

size alone. This research also indicates that demographics may not be a sufficient

indicator of likelihood of product usage (Nelson-Field & Riebe, 2011). The cost of

advertising is a significant expense on the balance sheets of the advertiser.


Ting (2010) found that a correlation exists in media ad space pricing, that

placement price increase varies directly with audience exclusivity (non-duplicative

audience). This is important because advertisers don’t place the same value on all

audience members. Those with certain characteristics that align with the advertiser’s

market segmentation strategy will be worth more and those without will be worth less.

Therefore, not all advertising exposures are of the same value to the advertiser (Ting,

2010). Duplication of audience exposure is another concern because it represents

competitive cost. Where two outlets provide duplicate audiences, to the extent that they

do, those two outlets will have to compete to sell their duplicated audience. This will

have the effect of driving price down (Ting, 2010). Advertisers will instinctively seek a

larger total audience, but Ting’s research argues that they should balance size with the

greatest possible exclusivity (Ting, 2010).

Koschat and Putsis (2000) found that the market price for an ad in a magazine is

grossly determined by the factors of supply of advertising pages in a market and the

demand that advertisers create for that space. Advertisers seek magazine advertising

space because exposure to its readership might induce purchase or, if having already

purchased, of inducing brand loyalty. Advertisers seek to maximize their efforts toward

what they perceive as their most desirable customer segments. Advertisers will tailor

their messaging and media buying to attract the greatest number of a desirable customer

segment at the lowest price (Koschat & Putsis, 2000). Those magazines that have higher

percentages of readers that are in high demand should command higher ad rates for the

same size ad than their competitors with lower percentages of high demand readers.

Magazines that earn the greatest premium over their competitors are those with a very


homogeneous readership base in two specific demographic categories – age (29 to 39)

and income (> $56,000 in 1993). These premiums are not in alignment with the actual

consumption probabilities of that age group or income group. There is an element of the

irrational in media buying (Koschat & Putsis, 2000) in that optimal practices may not

produce optimal results.

Danenberg, Kennedy, Beal, and Sharp (2016) have noted that brand managers are

expected to be able to justify that their advertising spend is sufficient to achieve their

marketing purposes, with little waste. Danenberg, et al. (2016) posited the existence of an

advertising intensiveness curve, which demonstrates that brands with a larger share of

market can spend less on share of voice than the equivalent fraction of their share of

market. Conversely, brands with a smaller share of market need to spend more than a

fraction equivalent to their share of market on share of voice. The advertising

intensiveness economy of scale curve holds across countries, across categories from

consumer packaged goods to services, and across media, including new media

(Danenberg, et al., 2016). Employing the advertising intensiveness curve, marketing

managers may predict effect on their share of market to the impact of additional

advertising dollars that may be spent.

Counter-indicators to Traditional Media Buys

Media is not the only solution to a marketing problem. Brands need to reach not

just more people, but more of the right people. As media consumption evolves and more

people watch “television” on what are actually computers through apps, measuring media

effectiveness may only become more complicated and difficult. Media optimization tools

are software programs that link Nielsen Media Research with a media plan to evaluate


how best to choose buys among various television options, including options to buy

syndicated shows (Kiley & Conrad, 1998). From 1992 to 1997, BMW achieved a 100

percent sales increase with only a 13 percent increase in advertising spending. In what

was an anomalous time in media history, DeWitt bought the media for BMW and stopped

buying prime network space altogether for BMW, making up the difference with cable

and syndication (Kiley & Conrad, 1998). “You don’t need an optimizer to know that

cable and syndication are more efficient than network,” said Gene DeWitt, chairman of

DeWitt media in 1998 (Kiley & Conrad, 1998). Outside of remarkably favorable media

circumstances, however, a brand may need more than horse sense to justify media

spending. For most media buyers, it’s getting harder to perceive the media environment

as it evolves so quickly.

By Comparison: Lucy’s Media Buying Model

I will look at artificial intelligence, what Watson is, how it works, its freedom

from bias, and then describe how Lucy works for a media planner. Equals 3 Media is a

media technology company in New York that had secured a contract with IBM Watson

Analytics to develop a media planning artificial intelligence application employing IBM

Watson Analytics’ AI tools. Equals 3 would teach its application, called Lucy, how to use

Lucy to segment markets, choose tactics, and develop plans for its clients that it hoped

would prove to be superior to those made by conventional planners.


Artificial intelligence

In the Spring of 1955, McCarthy, J., Minsky, M., Rochester, N., Shannon, C.

(1955) sent an invitation to about 50 colleagues in industry and academia around the

world, to attend a Summer Research Project they would host to investigate an idea:

“The study is to proceed on the basis of the conjecture that every aspect of

learning or any other feature of intelligence can in principle be so precisely

described that a machine can be made to simulate it” (McCarthy et al., 1955).

According to Myers’ (2011) obituary of McCarthy, that 1955 Dartmouth

Artificial Intelligence Conference appears to have been the first use of the term artificial

intelligence. Today, a computer system may be said to have artificial intelligence

characteristics if it applies human-like characteristics to perceiving, analyzing,

manipulating, synthesizing, and reporting concepts. Artificial intelligence, or more

commonly, cognitive systems, have the ability to create and test hypotheses, to work with

context and create inferences, and to extract and evaluate indexing information that is

implied within the data (High, 2012). In the case of IBM’s Watson Analytics artificial

intelligence service, the cognitive system selects a decision from candidate answers based

on the confidence it has in them, which is a measure of the evidence that supports them

(High, 2012).

IBM Watson Analytics has become the brand of a commercial, deep data mining

service, an application relationship with which can now be had as a regular business

transaction (Foyle et al., 2014). Stratistics Market Research Consulting (Anonymous,

2015) reported that, considering all brands of artificial intelligence business services, the

market for artificial intelligence has been predicted to grow globally at a compound


annual growth rate of more than 25%, and total annual expenditures on artificial

intelligence services will reach $40 billion annually by 2022. Watson Analytics is

distinguished from other kinds of computer systems in that it employs deep natural

language processing, internal hypothesis generation and evaluation algorithms, and

dynamic learning so it becomes smarter with each trial. It is unique among computer

systems in that it employs each of these attributes at the same time (High, 2012).

Watson’s process consists of a series of phases: question analysis, hypothesis generation,

scoring based on evidence, and finally sophisticated merging, ranking, and choice (Shih,

2012).

The entire group of documents within a collection of documents available to

Watson for search and analysis is called its document corpus (Foyle et al., 2014).

Humans must choose the corpus for it to ingest. The corpus ingestion process indexes and

curates the information, eliminating any knowledge that is out of date, irrelevant, or from

untrustworthy sources (High, 2012). Given a reasonable baseline corpus, Watson will

attempt to expand that corpus by seeking related documents, and merging related nuggets

into an expanded corpus (Ferrucci et al., 2010).

Watson’s deep natural language processing allows it to assess context within

language, both from within a question and from within the corpus so that it can achieve

an accuracy to the intention of the poser of the question, and from within the corpus to

choose the answer (High, 2012). Various aspects of the document corpus are referred to

as facets; correlation is a measure of how strongly a facet is correlated to the selection

criteria (Foyle et al., 2014). Correlation is significant in determining Watson’s confidence

when it chooses from among its candidate answers.


How Watson Came to Be

IBM had attempted other cognitive computing initiatives that had not progressed

as quickly (Ferrucci, et al., 2010). In contrast to them, Watson came into existence

because David Ferrucci, Director of IBM Research’s Watson initiative, hypothesized that

if you could build a computer that could compete with on the TV game show Jeopardy!,

you would have built a machine that understood human language and nuance, and which

could learn over time from its mistakes, based on millions of probability assessments

(Shih, 2012).

Ferrucci’s team’s solution was to abandon structured queries and columns and

rows of databases on one hand, and the text search based on frequency that Google uses,

on the other. Rather, his team developed a machine ability to decode a imprecise question

into parts and launch many parallel investigations into the parts, generate a putative

intended meaning of the question, find relevant documents among a plethora of non-

indexed documents, locate specifically necessary information in those documents, extract

the key ideas in that information, rank possible right answers by evaluating how well they

addressed the point of the question, and then choose the answer that best correlated, all

within a few seconds (Shih, 2012). Watson did compete on Jeopardy! in 2011, and did

win.

How Watson Works

In its process of seeking an answer, Watson deconstructs the question and tests

each of its parts for clues. It performs a question and topic analysis, including which of

the keywords in a question were worth pursuing (Shih, 2012). After it has decomposed

the question, it begins to simultaneously generate hypotheses. Then it evaluates the


context of the question. It generates a set of hypotheses and seeks within its ingested

corpus for correlations. Nearly simultaneously, it begins to score the evidence it finds and

the various hypotheses it has generated. It employs reasoning algorithms to test results

found in the corpus against its hypotheses. The algorithms produce scores for each result,

which are weighted against a statistical model to summarize what IBM scientists call a

“level of confidence” that Watson has in each answer (High, 2012). Then a synthesis of

all the final confidence scores, rankings and evaluations are combined and balanced.

Watson then selects the answer in which it has the greatest confidence. From the time the

question is posed to the moment the answer is stated, a few seconds will have transpired

(High, 2012).

Watson’s source data or document corpus is finite, so the humans who curate the

information ingested by Watson play a crucial role in its success (Shih, 2012). A large

fraction of the information in a project exists as unstructured data, that is, free text, in

emails, memos, reports, correspondences, text fields on forms and other messages.

Watson can analyze both structured and unstructured content. Unstructured content

reflects all of the ambiguity of the language that is employed (Foyle et al., 2014).

Unstructured content is data outside of databases or websites that lack structure or meta-

tags and so has been difficult for search engines to index. Search engines like Google’s

depend on analyzing links to a document (to score popularity), which may not be

available outside the web (Foyle et al., 2014), but Lucy can work with documents without

links.


The Promise: Decisions Made Without Bias

Humans, overwhelmed by large volumes of unstructured data, may revert to their

biases (Foyle et al., 2014). Watson, however, balances different knowledge sources

without prejudice (Shih, 2012). This may be seen to be of interest when humans have to

parse vast amounts of data, hopefully without bias, as media planners are asked to do.


Discussion

Equals 3 Media has shared with me how it pitches Lucy, its media planning

artificial intelligence application employing IBM Watson Analytics’ AI tools, to

prospective clients. Equals 3 Media argues that the reason they have invested in such

powerful artificial intelligence was because they had perceived that media planners today

have more data, more media outlets to advertise on, and more systems to manage

advertising than is practicable by any human being. Marketing media opportunities have

multiplied in recent years with the growth of online advertising, social media, and

streaming channels. Moreover, additional channels are evolving constantly, and the

planner has to decide whether an advantage exists in testing a new vehicle before its

breakout success or disappointing failure. The new media plan has to have weighed each

of these against traditional media such as outdoor, print, TV, and radio. The total possible

number of channels available is finite but mind-numbingly large. The marketer’s budget

even if large is limited and must be spent optimally. To help, technology solutions have

been developed. The marketing technology landscape today is populated with a myriad of

solutions all of which promise improved power and convenience but which in exchange

for some improvements contribute a great deal more effort required and new workflows

to incorporate into an already overflowing work schedule. The complexity of their

multiple learning curves is unproductive. More is less.

Into this environment, Equals 3 Media offers Lucy, which it advertises as a

“cognitive companion” to the marketing professional, whether a brand manager at an

advertiser, or a media professional (strategist, planner, buyer) at a media company. Lucy

has no limit to the amount of data she can ingest and so she can replace dozens of


dedicated marketing solution applications for market research, market segmentation, and

media planning. We may recall that Turing (1950) famously proposed that we could test

whether machines could think and posited a series of imitation games to show whether an

unprepared audience might be deceived into believing that computing machinery could

understand English and think. Millions watching Watson compete and win on Jeopardy!,

while they knew that Watson was a computer, thought that it was understanding

language, thinking, and concluding, regardless of the actual processes it used. Korn

(2016) reported that the Georgia Institute of Technology has tested a Watson-powered

service called “Jill Watson” to interact with through messages posted in an online

artificial intelligence class, which convinced students that she was a human teaching

assistant (TA) responding to their questions. In her article, Korn quoted Jennifer Gavin, a

student in the class as saying that, “It seemed very much like a normal conversation with

a human,” (Korn, 2016). One student reported intending to nominate Jill for an

outstanding TA award because of her helpfulness. Georgia Tech developed Jill, much as

Equals 3 Media developed Lucy, using Watson tools but without IBM’s direct

involvement by ingesting 40,000 previous TA postings into Jill’s corpus (Korn, 2016).

Whether a media buyer would be confused about Lucy’s humanity is outside the scope of

this paper, which is solely interested in artificial intelligence as a tool to improve media

buying. However, it’s worth noting that a baseline for human satisfying interaction with a

Lucy-like tool in other arenas has been achieved by Watson and Jill.

Let us examine how Lucy would support a media buyer, providing research,

analysis, targeting personas, creating and refining segments, and aligning vehicles to

segments and their psychographics. In a typical scenario, an account director creating a


media strategy may have access to unstructured but licensed market data, for example

from, WARC, Nielsen, and Gartner, and structured, licensed market and media data from

Advertising Research Foundation and Kantar Media. Lucy can quickly report on what

has been gathered about competitors’ media spending patterns, which States are the best

markets for which brands, which are the top spenders on a given media outlet and

vehicle, and a competitor’s reconstructed media plan from Kantar feeds. All that data

existed before but each data point had to have been searched for and found and

constructed into a report. Lucy does it in response to a question and would incorporate

her learning into a media buying recommendation.

In addition to structured, licensed data Lucy may have access to proprietary,

unstructured data from a brand’s Twitter accounts as well as Wikipedia and Google

Scholar. Lucy’s goal is to use all the information available to her improve the correlation

between buyers’ feelings, the media they interact with, and the decisions they make.

Hamby, Daniloski, and Brinberg (2015), have examined the narrative-creating

opportunities inherent in understanding how online consumer reviews impact the buyer’s

decision. Before Lucy, there were few good techniques to account for the impact of

online reviews on the buyer’s decisions. These now will be considered de rigueur

according to their correlation with results and will impact both media buying and

narrative generation.

To consider another relationship of data points, if the data exists, (and it usually

does for national brands), Lucy can show how a brand’s sales and social media reputation

have correlated with seemingly unrelated news items (weather, war, elections)


historically and suggest strategic messaging and media buying in response to possible

national and brand category events.

For example, Lucy can employ its AlchemyData tools to find the top three

sources that are reporting about the brand or its company or its category and then analyze

reports from outlets like Bloomberg and StreetInsider.com to analyze tone and

demonstrate how the news is treating the brand. While Markham, Kowolenko, and

Michaelis (2015) argued that the process for unstructured data-enabled decision making

should be iterative, Lucy, in what occurs so quickly as to appear to be a single step

process, constantly gathers unstructured information from live feeds in the background,

presents a recommended solution on demand and includes her degree of confidence in its

performance.

Lucy has worked with structured, proprietary data that resulted from custom

qualitative studies from entities like Blue Sky Research Group. Lucy can also ingest

unindexed data from the advertiser’s internal Word, Excel, and Powerpoint documents.

Lucy can have ingested commissioned custom market research from Ipsos and online

marketing and web analytics from Omniture to include in her corpus. Then there are the

constant structured data report feeds she can receive from Media Ocean, Nielsen, and

Salesforce. How could a human or team of humans find a correlation, trend or pattern

among all of these data? Real human media planners have to pretend that they have

considered all this data. Lucy can and does, quickly. This is quite different from a

separate big data analysis of finite data (Markham, Kowolenko, & Michaelis, 2015).

Lucy is working in real time and adjusts her conclusions as new data comes in.


Moldovan, Bacali, Vaida, and Lakatos, (2015) have described the advantage to

the marketer of having performed a psychographic analysis of the buyer, based

specifically on Jungian archetypes. The research of Barry and Weinstein, (2009) has

suggested the importance of considering psychographics even in business-to-business

marketing. Psychographic analysis, based on behavioral or attitudinal drivers has been

less than effective in predicting buying behavior. Jungian archetype analysis, on the other

hand, is less superficial, takes into account the diversity of buyers in a given segment,

inhering contradictions, but, significantly for the marketer, aligns on purchasing decision

probability (Morris & Schmolze, 2006). Equals 3 Media has loaded Jungian archetypes

into Lucy as a way to correlate psychographics with buying behavior and media outlets.

Lucy can analyze buyers’ historic typologies based on market research and group

generated personas according to type. For a selected target archetype, Lucy defines

orientation, suggests personas based on celebrities, analyzes why each would buy, and

even generates key word copy content that would resonate in a given media vehicle.

Assael (2011), found that over a 50-year period before its publication, very few

studies sought to establish media allocation criteria based on the relationship of media

interactions to corporate return on investment (ROI). Media allocation based on multiple

criteria including ROI is built into Lucy. In developing a Media plan, Lucy offers a

dashboard whereon the media planner can select between three and ten channel allocation

criteria that are deemed most important. These may include such criteria as GRPS

(quantifying impressions as a percentage of the target population, with a goal of

maximizing the impressions) and Cost (with a goal of minimizing the spend). Other

selections might include particular traits exhibited in actual social media postings. One


could, for example, compare three cable channels against the criteria selected, and add in

traits to see how they continue to perform based on the changes, until the optimum outlet

and vehicle and spend are determined for the criteria of a specific persona.

If we return now to examine Figure 1, we can see that the same steps would exist

were Lucy involved, but that they involve fewer people and are not iterative. Consider

how the process has evolved into Figure 2, on the following page. Many humans

performing many intermediate steps will have had the demand for their services

eliminated as Lucy performs their steps as part of her process.

In Figure 2, Lucy performs as a companion to the media planner providing

capabilities and ease-of-use that have never been available before. Lucy-supported plans

should be more accurately targeted, more efficiently spent, optimized, and require fewer

staff-hours to prepare. At the same time, Lucy virtually eliminates human bias because

each decision is based only on data. In this case, artificial intelligence is clearly superior

to her human alternative.


Figure 2. Lucy’s media planning and implementation process.

Media Negotiations, Buying Space, and Inserting Ads

Monitoring & Comparing Invoices against Insertion Orders

Evaluation of Campaign

Media Owners Agency Advertiser

Advertising Plan

Media Buyer Creative

Lucy


Conclusion

This process has uncovered how artful, non-objective, and non-data-driven the

work of media planning and buying can be. Media buyers have sources, they analyze

data, but in the end, they make educated guesses, few of which are testable for optima.

Moreover, there are multiple opportunities for bias to pervade the decision-making

process.

Binet and Field (2007), in their work for the Institute for Practitioners in

Advertising, have developed a series of balanced econometric tests arguing that profit

generated by a campaign should be the ultimate test of its success. They recommend

applying a balanced scorecard of measures of financial return when evaluating the

success of a media campaign. Overall, they found five relevant measures to balance:

validated ROI, market share gain, reported intermediate effects, reported business effects,

and a measure of accountability success rate. In addition, the authors found that

emotionally-based influence models were more effective in yielding strong business

results than performance, information, or reinforcement models, but the fame model,

which gets the brand talked about, was the most effective of any of the models (Binet &

Field, 2007). Correlations between campaign exposure and brand performance should be

tested on campaigns year-over-year before and after Lucy is applied to the media buying

process, as a test of advertising effectiveness. Binet’s and Field’s model seems a viable

program for testing Lucy’s efficacy compared to the same campaign planned by humans.

Advertising agencies and media buying is about to change. Media plans will be

recommended by artificial intelligence, probably at the behest of the advertiser rather

than an advertising agency. Media planners will become engineers, media budgets will be


spent more scientifically, and even copy may become more strangely compelling to those

of us who have been targeted, but the art form that was Madison Avenue advertising, as

an art form, will have faded into legend.

I have been privileged to watch Lucy learn how to answer media questions as she

ingested her corpus. Through this process, I have watched her progress as one might a

child progressing through her grades from pre-school to college, to graduate school to

post-doc, professional competency, to unique wizardry, all in the space of a few months.

Watching the breadth of her capabilities, the ease with which she receives

requests, her constantly improving expertise, has been nothing short of stunning. It’s hard

to imagine how any human agency could compete with how Lucy optimizes choices

based on many sources of vast amounts of structured and unstructured data. She has

clients today and they will engage her with great expectations and will see her

performance in their sales figures in the near future. As Lucy succeeds for her clients,

media planning may well become dominated by artificial intelligence. Lucy’s (and her

ilk’s) placing of ads where we seek them will touch our needs to buy as never before, and

we may begin discovering offers just when we are ready to receive them and prepared to

act on them. We won’t lose our will, but when we do have a want, we will find

opportunity waiting for us, just where it should. In the end it will be for us to perceive

that we are being sold and we will have to consider whether being accurately targeted is

what we want. However, satisfying Turing’s test, we will not realize by whom we have

been targeted.


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