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Artificial Intelligence Applied
to Digital Marketing
Tiago Ribeiro
1
and JoséLuís Reis
1,2(&)
1
IPAM, Portuguese Institute of Marketing, Porto, Portugal
tiagoribeiro0698@gmail.com
2
ISMAI, Maia University Institute,
Research Units UNICES/CEDTUR/CETRAD, Maia, Portugal
jreis@ismai.pt
Abstract. Based on the theory that both manual and cognitive tasks can be
replaced by Artificial Intelligence, this study explores, using a qualitative
research method, the impact of Artificial Intelligence (AI) in Digital Marketing.
An analysis of interviews with 15 experts from different industries related to
Marketing and AI shows that AI have impact in Marketing processes and the
impact will be bigger in the future. The study reinforces that many of the manual
and repetitive tasks of a marketer’s life can already be replaced by AI, and the
use of machines working together with humans are the key to better marketing
results. The challenges and ethical aspects that lead to a slow or non-adoption of
AI have been addressed, and one of the major obstacles is that humans aren’t yet
confident in technology and, they are not yet ready for this cultural change.
Based on these findings, business decision-makers and managers need to pre-
pare their companies and employees for the implementation of AI in Marketing.
Keywords: Artificial Intelligence Marketing Digital Marketing Machine
learning Integration
1 Introduction
Artificial Intelligence is integrated into our lives, although many people are unaware of its
presence. This misconception is evident from the fact that only 50% of responses from the
PRNewswire (2018) consumer awareness study state that they have never interacted with
AI technologies and 23% are unsure whether they have ever interacted with AI. tech-
nology. There are many examples of AI that operate in the background of most modern
technologies (smartphones, computers, smart TV’s, etc.) revealing an apparent lack of
knowledge about what consumers think AI is and how AI is applied daily [1].
This paper presents the results of an exploratory study with a quantitative
methodology, based on 15 interviews with specialists, which provided a better
understanding of the impact of AI on digital marketing. The article presents the main
aspects related with Artificial Intelligence and Digital Marketing, the used methodol-
ogy, the analysis and discussion of results of the research and finally the study
conclusions.
©The Editor(s) (if applicable) and The Author(s), under exclusive license
to Springer Nature Switzerland AG 2020
Á. Rocha et al. (Eds.): WorldCIST 2020, AISC 1160, pp. 158–169, 2020.
https://doi.org/10.1007/978-3-030-45691-7_15
2 Artificial Intelligence and the Digital Marketing
AI is present in the daily lives of people and businesses, an example of which are voice
recognition, image recognition and handwriting suggestions available on today’s
smartphones [2]. Kietzmann, Paschen and Treen (2018) report that in order to deepen
understanding of consumer decision-making, there are very useful AI systems for
marketers [3], of which the following points should be highlighted.
2.1 Artificial Intelligence
According to Russell and Norvig (2016), Artificial Intelligence is computerized sys-
tems that capture data to perform tasks of intelligent beings in order to maximize their
chances of success [4].
Strong AI (Artificial General Intelligence) is a machine with consciousness and
mind, and this machine has intelligence in more than one specific area. Weak AI
(Narrow AI) focuses on specific tasks (autonomous cars derive from Narrow AI) [5]. In
addition, there are authors who hypothesize that computers may be better or smarter
than humans, so there would be a new AI term, called Artificial Super Intelligence, but
right now it’s hypothetical [6].
According to Rosenberg (2018), based on the Constellation study, looking at
investment in all sectors of the market, there will be an investment of over 100 billion
euros per year in Artificial Intelligence in 2025, while in 2015 only 2 billion was spent.
The Marketing industry will be no exception and there will be increasing investment in
AI [7]. From McKinsey & Company’s analysis of more than 400 AI use cases in 19
industries and 9 business functions, the authors Chui, et al. (2018) found that the
greatest impact on the potential value of AI use is in marketing and sales, supply chain
management and production. Consumer industries, such as retail and high tech, tend to
see more potential in AI applications in marketing and sales because frequent, digital
interactions between companies and customers generate larger datasets for AI tech-
niques. E-commerce platforms can benefit from AI because of the ease with which
these platforms collect customer information, such as click data or time spent on a
website page, and can customize promotions, pricing, and products for each customer.
dynamically and in real time. The study uses cases that using customer data to cus-
tomize promotions, for example, using individual offer personalization every day, can
lead to a substantial increase in sales [8].
2.2 Natural Language Processing –NLP
Natural Language Processing (NLP) enables AI systems to analyze the nuances of
human language to gain meaning, among others, from blog entries, product reviews,
billions of daily tweets, Facebook posts, etc. Swedbank, a Swedish bank, uses a virtual
assistant with NLP to answer customer queries on its website’s home page, allowing
customer service employees to focus more on sales without sacrificing service [3].
Artificial Intelligence Applied to Digital Marketing 159
2.3 Image and Voice Recognition
Image recognition helps marketers understand images and videos that people share on
social networks and “show”consumer behavior. Consumers identify details about the
offerings pictured in the image, and marketers benefit from the details of contextual
consumption. Selfies reveal the marks used, even when not explicitly mentioned in the
publication, and the personal details of users. When a celebrity shares a photo about an
unidentified product, image recognition recognizes both the product and a potential
social media influencer [9].
San Diego-based Cloverleaf uses image recognition on its smart shelf display
platform. Equipped with optical sensors, the display collects customer demographics
such as age and gender and analyzes shoppers’faces to gauge their emotional reaction to
the product. The closer consumers are, the more personalized the content will be [3,9].
Speech recognition allows AI to analyze the meaning of the words reproduced.
Sayint, a call center service provider, uses voice recognition to monitor and analyze
customer calls. Technology helps Sayint understand customer needs, improve caller
performance, and increase customer satisfaction and Artificial Intelligence in Business
gets real [10].
2.4 Problem Solving and Reasoning
Marketers implement AI to understand hidden insights into consumer-generated con-
tent, narrowly defining the problem they want to solve and how they will approach data
analysis. These core processes generate pattern detection in the data, improving the
ability to predict future behavior. Marketers may want to segment their market based on
the varying psychography of their customer base, possibly to determine who their
“best”customers are and why those customers would buy their offers against com-
petitors. The personality traits that are important in people’s lives eventually become
part of their language [10]. AI can “reason”with comments and posts on people’s
social networks, and can reveal personality trends, values and needs. AI-based profiles
derived from consumer analysis may be relevant to future marketing decisions. North
Face, using IBM Watson, uses AI to determine which jackets consumers may be
interested in, based on available data. The system begins by asking where, when, and
what activities the consumer will be wearing the jacket and based on the weather
forecast for that location and the wearer’s gender, narrows the search to six options.
Based on activity, rearranges alternatives from “high match”to “low match”. This will
save the wearer time by avoiding hundreds of jacket options, many of which would not
even meet your functional needs. This is a way to increase the quality of the customer
experience throughout its decision-making journey [3,10].
2.5 Machine Learning
Machine learning is a subcategory of AI that uses computer programs to learn and
improve throughout experiments, processing huge amounts of data. It is the fastest
form of AI and is the primary source in the AI industry for marketers. By detecting
patterns in data, machine learning systems can “reason”and propose the best options
160 T. Ribeiro and J. L. Reis
for the stated consumer needs, more efficiently than humans. In addition, the system
remembers everything that was previously calculated, storing all memories in a
knowledge base and uses machine learning to learn from your previous experiences
and problem solving (Big Data).
The more unstructured data a machine learning system processes, the smarter and
more insightful the subsequent positive results for marketers. Just as a bank without a
database cannot compete with one in which they are present, a company without a
machine learning (AI subcategory) cannot keep up with another that makes use of it.
While experts in the former write thousands of rules to predict what customers want,
second algorithms learn billions of rules, an entire set of them for each customer.
Machine learning is a new and bold technology, but this is not why companies adopt it,
but because they have no choice in relation to the benefits that technology offers [11].
Marketers use machine learning to monitor consumer behavior. Develop algorithms for
discovering websites visited, open emails, downloads, clicks, etc. They can also ana-
lyze how the user behaves across channels, which accounts they follow, posts they like,
ads they interact with, etc. [12].
Depending on studies and industry, acquiring a new consumer is between 5 to 25
times more expensive than maintaining an existing one. Because you don’t waste time
and resources looking for a new customer, the focus is simply on keeping the existing
customer satisfied. Machine learning through predictive models can help predict
Customer Lifetime Value (CLV) and through clustering models make targeting more
accurate, fast and effective. CLV is the value of all a customer’s interactions with the
company over time. By focusing on CLV, brands attract more important customers,
encourage continued engagement, and increase audience retention. By analyzing pat-
terns and learning from data about past consumer behavior, machine learning can
predict the future value of a customer. It is a system that can make predictions, for
example predicting consumer retention rates. Consumer retention rate is the metric that
measures the percentage of consumers who break up with a business over a certain
period, or how long a user spends on a landing page. Machine learning gives the
marketer the information foreseeing possible customer abandonment, so marketers can
use strategies to keep them interested in the brand [13].
3 Methodology
This work is an exploratory and descriptive study on a specific theme. The method-
ology that supports the research is qualitative and, above all, descriptive. Based on the
context of the AI tools applied in marketing, presented in the previous points, this study
made an analysis focused on the perspective of the people who work with AI, although
consumers always assume themselves as central and structuring figures in research, due
to their constant relationship with them [14].
As this is an exploratory and descriptive study, intend to understand the strategies
of companies that use AI, the benefits, the challenges presented, the ethical issues and
to understand the impact that these practices are having on companies’income. It is
considered relevant to understand which elements are considered essential for the
successful implementation of an AI strategy in Marketing, as this research aims to be a
Artificial Intelligence Applied to Digital Marketing 161
contribution to companies and a supporting document in the implementation of a
successful AI strategy in marketing.
The first part of the study provided the theoretical underpinnings based on sec-
ondary information from scholarly articles, journals, reports and books. In the second
part, the primary data collection was performed to be analyzed together with the
theoretical bases.
3.1 Research Objectives
The research developed allowed to understand the strategies of companies that use AI,
the benefits, the challenges presented, the ethical issues and to understand the impact
that these practices are having on companies’income. It was considered relevant to
understand which elements are considered essential for the successful implementation
of an AI strategy in Marketing. This research work is a contribution for companies to
support the implementation of a successful AI strategy in Marketing.
3.2 General and Specific Objectives
The purpose of this study is to understand the current situation of Artificial Intelligence
in Marketing, analyzing how AI currently impacts Marketing and the impact it will
have in the future.
The specific objectives of this work are as follows:
–Identify the key benefits of implementing AI in Marketing.
–Understand the key challenges and ethical aspects of integrating AI in Marketing.
–Assess how companies are using AI in Marketing and what are the uses of AI
applications and what problems they solve.
–Check if Small and Medium Enterprises (SMEs) are able to integrate AI into
Marketing.
–Understand the impact AI has on marketing today, and what it will have in the
future.
3.3 Interview Data Collection
To collect primary data to meet the objectives, interviews were conducted as a qual-
itative study method. For semi-structured interviews, the interview script was not rigid,
and the answers were open.
The questions asked were based on the knowledge obtained during the literature
review. The choice of specialists was made through contacts via LinkedIn or by con-
tacting companies directly. In the profiles of respondents there are computer science
professionals, data scientists, consultants and marketers. Notes were taken during the
conversations with the experts and were extracted and summarized the essential content,
and then analyzed according to the research objectives. The evaluation and discussion of
the results was guided by the research questions defined and the literature review.
Table 1show the specialists profile, with information about their country of origin,
their professional area, organization and their acronym.
162 T. Ribeiro and J. L. Reis
4 Analysis and Discussion of Results
After collecting data obtained from the interviews with the specialists, data were
described and analyzed. The analysis was structured according to the research objec-
tives. First, the benefits of integrating AI into Marketing will be cited by respondents
and compared with data gathered from the literature review. Next, all factors that
influence the slow integration or non-integration of AI in Marketing will be collected
and described. It will then show how companies are using AI in their marketing
strategies, and whether SMEs are able to integrate AI into their Marketing processes
and finally will be made an analyze of the impact that AI has on marketing costs and
revenues.
Table 1. Experts interviewed
Name Country Prof. area and organization Acronym
Mark
Floisand
UK Chief Marketing Officer at Coveo Exp 1
Stephanie
Ogando
Brazil Marketing Analyst at Alfonsin Exp 2
Peter
Mahoney
USA Marketing Intelligence Consultant at Plannuh Exp 3
Paul Rotzer USA AI Marketing Author/Consultant at Marketing
Artificial Intelligence Institute
Exp 4
Bernardo
Nunes
Brazil Data Scientist/AI Marketing Consultant at Growth
Tribe
Exp 5
Katie King UK AI Marketing Author/Consultant Exp 6
Christopher
Penn
USA Data Scientist/Digital Marketer at Trust Insights Exp 7
Jim Sterne USA AI Marketing Author/Researcher at Digital Analytics
Association
Exp 8
Patricia
Lorenzino
Brazil Head of Strategic Alliances - IA at IBM Exp 9
Nuno
Teixeira
Portugal University Professor/AI & BI Consultant at
ISCTE-IUL
Exp 10
Alex Mari Switzerland Researcher/Consultant at University of Zurich Exp 11
Sergio
Lopez
Bolivia AI Marketing/Marketer Consultant at AIMA Exp 12
Kevin Kuhn Switzerland AI Marketing Consultant at Jaywalker Digital Exp 13
Alexander
Avanth
Philippines Director of Innovation at PTC Holdings Exp 14
Tilak
Shrivastava
India Senior Marketing Manager at Ityx Solutions and
ThinkOwl
Exp 15
Artificial Intelligence Applied to Digital Marketing 163
4.1 Benefits of AI Integration in Marketing
The main expected benefits will be lower costs and higher revenues. AI delivers
benefits on acceleration, faster results, accuracy, better results and relief, reducing tasks
that it is not essential for people to do more because it is not a good use of their time
(Exp 2, Exp 4, Exp 7). Machines can identify and solve certain problems faster than
humans.
Machines can do better and on a much larger scale. A human can try to read 10 000
social networking posts in five minutes, but certainly won’t do it. The machine can
reduce and remove repetitive or unimportant tasks from marketers’lives, for example, a
report by a marketer that would last about eight hours can be done by a machine in eight
minutes. This way you can reduce repetitive task costs and direct marketers to tasks that
are more about creativity, strategy, and decision making (Exp 4, Exp 7, Exp 8).
AI’s main advantages in Marketing are: sales development through customization,
greater process effectiveness and greater efficiency in marketing investment allocation.
Marketers do not need to focus on segmentation, behavioral analysis, consumer jour-
neys. AI will “filter out”huge volumes of data and feed insights that can effectively
make a difference to the business (Exp 5, Exp 9, Exp 10, Exp 13). AI’s integration into
marketing produces benefits for consumers (relevance, convenience, consumer expe-
rience) and enterprise/marketers (predicting consumer behavior, anticipating consumer
trends, hyper-personalizing content). At the operational level, AI offers the opportunity,
through process automation and optimization, to increase the efficiency and effec-
tiveness of company strategy and the quality of work of people (Exp11).
AI enables the marketing team to deliver a personalized user experience without
being overly intrusive. Artificial Intelligence already enables marketers to optimize
websites by customizing them for different users, for example by offering them per-
sonalized messages and distinctive designs based on their profile and needs. AI will
enable organizations across all industries the ability to rebuild personal relationships
with their customers. Data provides powerful insight into customers’current needs as
well as valuable information about their future needs (Exp 6, Exp 14).
4.2 Challenges and Ethical Aspects of Integrating AI in Marketing
With all the benefits that come through AI, questions and problems also arise. In recent
years, according to respondents, marketers have wondered how marketing can deliver
value without being too intrusive (externally) and how marketing can reshape and
empower people within companies (internally) to work in this logic. A successful AI
strategy can only be effective when there is strong technical (technology, data, pro-
cesses) and organizational (people, capacity, culture) technical capability. Failure to do
so may result in poor performance, even if the company is working with partner
companies in some of its AI activities (Expert 10, Expert 11, Expert 12).
The first aspect mentioned in the interviews, and one mentioned by virtually all
respondents, is trust. Citizens must understand the value of the data they generate
(digital footprints) and understand what brands can do with these digital footprints. AI
is a relatively new technology and is complex, meaning that the general public (and
even technical employees who are unaware of AI) may suspect it exits. Consumers
164 T. Ribeiro and J. L. Reis
need to be aware of how companies and governments acquire and use data to determine
user behavior, such as purchases, recommendations, and voting decisions. Ethics and
digital privacy (General Data Protection Regulations - GDPR) are a concern of indi-
viduals, organizations and governments. People will be increasingly concerned about
how their personal information will be used by organizations in the public and private
sectors. For there to be confidence in technology, companies will need to proactively
address these issues. Transparency can do much to increase consumer confidence in AI.
By explaining how Artificial Intelligence algorithms use customer data to make their
decisions (when, how, and where the customer provided that data), it helps to build
confidence (Exp 1, Exp 3, Exp 4, Exp 5, Exp 7, Exp 9, Exp 10, Exp 11, Exp 12, Exp
13, Exp 14, Exp 15).
Another of the most mentioned aspects is data quality and what companies do with
data. Many companies have no idea where data is generated and what they can do with
data, they do not have data that has a unique view of customers and is properly
validated and sanctioned by the company. For AI to be successful, it requires large data
sets. However, most large companies have a lot of data locked in various marketing
systems they already use. The key is to be able to connect to systems, use this data and
unify it - since data is unified around individual customer profiles, AI can tailor
campaigns and marketing experiences specifically for everyone (Expert). 3, Expert 7,
Expert 10, Expert 12, Expert 13, Expert 14).
4.3 AI Applications in Marketing
The largest use of AI in Marketing is through machine learning. In the old days the
brute force of computational power was used, all movements had to be defined, but
with the use of machine learning the algorithms learn for them. Machine learning is an
important underlying AI technology that is used to create models that can identify
patterns in complex data sets. Marketing is more about personalizing content, the best
techniques are based on it (Exp 3, Exp 4, Exp 5, Exp 7, Exp 11). Analyzing the
respondents’answers, the main uses of AI are the predictive models, clustering and
recommendation systems. Predictive models are used to predict and anticipate con-
sumer movements and behaviors along the stages of the customer journey, to lower
dropout rates, identify factors of customer dissatisfaction, manage best customers, and
prioritize business.
Clustering models use unsupervised algorithms to do segmentation, that is, they
calculate how much one client looks like another and put them in the same cluster if
there are similarities. These models improve the customer attraction process - they
automate the process, identify audiences and similar targets, and enable marketing to
spend to be optimized by segmenting, predicting, and identifying segments more
efficiently. They are used to perform more accurate, fast and effective segmentation and
targeting. These are the must-have models, meaning that virtually every company
should have today (Exp 5, Exp 7, Exp 10, Exp 15).
Artificial Intelligence Applied to Digital Marketing 165
4.4 Capacity of SMEs to Integrate AI in Marketing
There are two possibilities, companies can choose to develop and run their own AI
marketing solutions or use AI-based marketing tools developed by other companies.
In the past, personalization was very expensive, but in recent years personalization
has become cheaper, due to the existence of machine learning algorithms. Building a
model is cheaper, as universities and programmers make these algorithms available in
open source, and computing power is more affordable. Formerly you had to use uni-
versity servers to train algorithm models, now with Google, Amazon.com, and
Microsoft data clouds accessible to any company, you can use servers to train models
without spending a lot of money. The most advanced model of computer vision is
inexpensive because major AI-based companies (Google, IBM, Facebook, Amazon.-
com, and Microsoft) have turned these models into cognitive services. These compa-
nies provide AI tools, some even automatic, the user sets the objective variable, the
data that he has available and wants to relate to the point, and the process is done
automatically and made available by the cloud. As it is getting cheaper, companies are
expected to use more and more. By having the time and knowledge of business human
resources, you can make your own AI solutions without much technology spending.
The greatest difficulty will always be the time spent and the ability to have qualified
human resources. Companies must create their own solutions if time and qualified
human resources are available. If they want faster results and have the money to invest,
they should choose to use tools from other companies (Exp 5, Exp 7, Exp 10).
In Experts 4, 8, 11 and 12 opinion, SMEs should always buy rather than build.
They should not hire a team of scientists and data engineers. The costs will be very
high, and hard to find. Instead, it is preferable to use machine learning tools being
incorporated into systems such as Adobe Sensei, Salesforce Einstein, or Shopify, and
be mindful of the tools being created by startups. There are many tools that solve
certain problems. In addition to the number of solutions, marketing technology is also
growing at its level of sophistication as smart algorithms are becoming essential for
these services (Exp 4, Exp 8, Exp11, Exp 12). SMEs should rethink their marketing
strategies and adopt marketing technologies that integrate AI solutions that can deliver
high value without significant upfront investment and, most importantly, without
having a huge amount of individual-level data (Exp 3, Exp 8, Exp 11, Exp 15). Small
and midsize businesses will mostly use marketing software that fills a business need
such as lead generation, email marketing, search engine optimization or online chat.
With AI, SMEs can find smarter tools that use Artificial Intelligence to create their
solutions. So most SMEs have to look at the technologies they use today and see if
there are smarter ways to do each of these things, ensuring that they are using the
smartest tools available to reduce their business costs and increase revenue (Exp 4,
Exp 12).
4.5 Impact on Marketing Costs and Revenues After AI Integration
Initially, implementing AI in marketing will have a big impact on the business until
they can figure out what works best and what is the best solution for solving the
problems they have defined. But once that is done, the other steps will be easier and
166 T. Ribeiro and J. L. Reis
less expensive. This is because they will have their quality data and can easily develop
new solutions (Exp 12).
For most marketers, AI does not change the level of marketing spend. It simply
improves the performance of marketing efforts. It enables marketers to be more effi-
cient, it also allows brands to be more selective about the content they reproduce,
helping them prioritize content that is most valuable to their visitors. Most companies
maintain the same volume and marketing expenses but increase the accuracy of their
marketing efforts by being more targeted, faster and more effective, thereby delivering
better results (Exp 1, Exp 4, Exp 5, Exp 9, Exp 10, Exp 15).
With a well-implemented AI-based approach there will be cost savings, opti-
mization and increased ROI. As the Boston Consulting Group and MIT Sloan Man-
agement Review report found, companies that customize their communications can
increase their revenues by up to 20% and reduce costs by up to 30%. One of the main
technologies being used in this process is Artificial Intelligence [13].
Rumelt (2011) defines that there are three fundamental steps to a good strategy. The
diagnosis - where the business strategy is evaluated. Political orientation - where the
challenges related to governance, culture and ethics are perceived. Coherent action plan
-definition of aspects such as: resource allocation, implementation, purchase/build
decisions, processes, talent development/hiring/retention, change management within
the company related to people’s culture [15].
5 Conclusions
From the data obtained from both the consulted studies and the interviews carried out
within the context of this work, it is concluded that AI will have more impact on the
future of marketing and that even SMEs can implement AI. Companies that are cur-
rently conducting marketing activities without AI-based solutions must be prepared for
change. Developing training for a successful AI strategy in Marketing can only be
effective when there is strong technical (technology, data and processes) and organi-
zational (people, skill and culture).
The first step in any AI Marketing strategy is to review the company’s business and
communication strategy. Once the company’s business and communication strategy are
clear, the best use cases should be identified to help the company achieve its objectives.
That is, what are the problems the company wants to solve that with the help of AI can
help achieve the company’s strategic goals.
In the implementation phase, the company needs to think about how to turn its
artificial intelligence strategy into reality. Companies need to understand how AI
projects will be delivered; those responsible for each action; actions/projects that will
need external support. Companies must consider what technology is required to
achieve their AI priorities. Companies must understand and define whether it is best for
their business objectives, to have an AI team within their own company, or whether
will use solutions designed by other companies.
Artificial Intelligence Applied to Digital Marketing 167
5.1 Limitations of the Study
Since this is an exploratory study, there is a certain degree of description in the analysis
of the results. Although the qualitative methodology is not the best in the generalization
of the results, the analysis intended to be done in this study was more indicated using a
qualitative approach.
This study could have been carried out with a different methodological approach,
but it would not have been possible to understand so well the reasons behind these
results. It is important to note that, in this work, when it comes to convenience sam-
pling in the interview process, it influences the reliability of the results, because if other
interviewees were chosen, the answers could be different.
In addition, it should be considered that this paper consists of statements from only
15 respondents, which makes it difficult to say with certainty that the results of this
research are comprehensive and complete. Also, because of the small sample size, it is
not possible to project a perspective that accurately reflects. However, as it was
diversified among various professional areas, it is believed that data quality was
assured.
5.2 Future Work
To have a better understanding the impact of AI on business on the marketing, this
study must be completed through with testimonies from business managers more
conclusive about the picture of AI’s impact. On the other hand, as demonstrated
throughout this paper, the target client is always present, and in future work it is
important to understand the impact on their lives.
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