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NUDGING CREATIVITY IN DIGITAL MARKETING
WITH GENERATIVE ARTIFICIAL INTELLIGENCE:
OPPORTUNITIES AND LIMITATIONS
Research in Progress
Kowalczyk, Peter, University of Würzburg, Würzburg, Germany, peter.kowalczyk@uni-wuerzburg.de
Röder, Marco, University of Würzburg, Würzburg, Germany, marco.roeder@uni-wuerzburg.de
Thiesse, Frédéric, University of Würzburg, Würzburg, Germany, frederic.thiesse@uni-wuerzburg.de
Abstract
Generative artificial intelligence (
AI
) achieves remarkable results in the form of synthetic images, texts,
audio, or even video. Therefore, it is particularly suited to nudge creative tasks as required in the design
of marketing campaigns. Here, innovative concepts draw attention to stand out. Despite its prospect,
generative
AI
is yet to be applied in digital marketing on a broad scale. Against this backdrop, we consider
the marketers’ view of opportunities and limitations associated with the technology to finally develop a
research agenda and thereby contribute its evaluation and adoption in practice.
Keywords: Generative Artificial Intelligence, Digital Marketing, Interview Study.
1 Introduction
Generative
AI
produces remarkable results for synthetic images, texts, audio, or even video. Regardless
of the desired context, state-of-the-art tools like DALL-E 2 (Ramesh et al., 2022) or Stable Diffusion
(Rombach et al., 2022) are highly capable of creating convincing new images from a single text prompt
(Marcus et al., 2022). First works even guide how to effectively tweak the prompts to make the output
match desired aesthetics (e.g., Parsons (2022)). Likewise, current research concerns the generation of
entire videos from equally sparse text inputs (Singer et al., 2022). Breakthroughs are also achieved
for complex text-based tasks such as translation, reasoning, or code writing through novel language
models such as PaLM by Google Inc. with its whopping 540 billion parameters (Chowdhery et al.,
2022). The recently published GPT-4 model is advertised to accept both image and text inputs therefore
exhibiting multimodal capabilities while providing human-level performance for multiple professional and
academic benchmarks (OpenAI, 2023). As for audio data, AudioLM, again by Google Inc., is capable to
generate a realistic continuation for an audio sequence of a few seconds (Borsos et al., 2022). With rapid
technological innovation and its democratization, practitioners are increasingly provided with powerful
generative
AI
tools to reshape creative processes (Anantrasirichai and Bull, 2022; Mazzone and Elgammal,
2019). Among others, marketing is a fruitful application domain for the content created with generative
AI
. Here, innovative and customized content helps to stand out from the competition (Kannan and Li,
2017; Leeflang et al., 2014). Therefore, generative
AI
with its ability to suggest outstanding material in
seconds and at very low cost is particularly suited for a marketer-machine collaboration in order to conduct
different kinds of effective marketing campaigns. Neglecting the technology could give competitors a
window of opportunity to stand out and thereby cause a detrimental effect on the own organization’s
success in the medium or long term.
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However, the full potential of generative
AI
in marketing is hardly recognized so far and still waits to
be explored from a practitioner’s view (cf. section 2.2). While this can be partly attributed to the sheer
novelty of the approaches to generative
AI
, it may also originate from the users’ hesitation due to the
negative headlines associated with the technology (e.g., deepfakes in social media) (Kietzmann et al.,
2020; Mirsky and Lee, 2020; Westerlund, 2019). Despite these issues, we argue that the technology bears
an unprecedented potential to change creative fields such as marketing. It is at the core of the information
systems discipline to examine the various facets of such novel technology, thereby contributing to its
reasonable application and democratization (Berente et al., 2021). Hence, at the crossroads of emerging
approaches to generative
AI
and the untapped potentials for marketing, we strive for an initial assessment
of opportunities and limitations from a practitioner’s perspective. Thus, we formulate our research question
as follows:
RQ
Which opportunities and limitations are associated with the use of generative
AI
in the design of
digital marketing campaigns?
To this end, we present results from qualitative interviews with experts from digital marketing, as these
are well suited to provide an initial assessment of the practitioners’ perspectives (Basias and Pollalis,
2018). The remainder of the article unfolds as follows. The subsequent section briefly elaborates on digital
marketing and generative
AI
. The next section concerns the method chosen for the study. Following up,
the results and the connected practical implications are presented and discussed. The article concludes
with the formulation of a research agenda to foster the use of generative
AI
in the field of digital marketing
as a commodity.
2 Background
2.1 Digital Marketing
Marketing is the key activity in business of "[...] attracting new customers by promising superior value
and to keep and grow current customers by delivering satisfaction" (Armstrong, 2009). For this purpose,
in-depth knowledge about the respective product or service and the corresponding market is required
(Kotler et al., 2015). Digital marketing can be described as an umbrella term for the use of digital
technologies in that vein (Kannan and Li, 2017). Digital marketing rose to popularity across the boards of
all organizations with the Dotcom hype in the early 2000s making it standard marketing practice since
(Ryan and Jones, 2012).
Successful marketing is heavily dependent on the organization’s ability to address markets effectively
(Baker and Cameron, 2008). In this context, market segmentation is key. As early as 1956, Smith in-
troduced the concept which concurs with the markets’ heterogeneity. Following Dibb (1998), market
segmentation is achieved in three stages—namely, (i) segmenting, (ii) targeting, and (iii) positioning. Five
possible dimensions for segmentation are products and services,demographics,geographics,channels,
psychographics (McDonald, 2012). The first dimension is naturally given by the item or service itself
(McDonald, 2012). For example, a full-stack software subscription besides a free basic option rather
attracts more professional users than price-aware customers looking for specific services resulting in
two distinct groups. Similarly, demographics such as, for example, age, gender, socio-economics, ethnic
background, or marital status can be used for market segmentation (McDonald, 2012). Since customers
can be locally or globally dispersed, geolocation might be a useful dimension for segmentation (Mc-
Donald, 2012). In addition, the means for reaching the customers can be used for differentiating market
segments (McDonald, 2012). Because psychographics help understand the customer’s inner feelings and
prepositions, they can play a crucial role in addressing the customer in the right way and are therefore
recommended to be factored in (McDonald, 2012). The overall goal of segmentation based on dimensions
as above is to aggregate customers into various groups while maximizing homogeneity within and at
the same time heterogeneity in between (Dibb, 1998). Given the segments, marketers decide on which
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segments should be targeted and how (Dibb, 1998). Lastly, in the positioning step, the marketing mix is
devised in concordance with the selected targets (Dibb, 1998).
2.2 Generative Artificial Intelligence
In short, generative
AI
describes the use of generative models such as e.g., generative adversarial networks
(
GAN
s) (Goodfellow et al., 2020) or variational autoencoders (
VAE
s) (Kingma, Welling, et al., 2019) to
produce novel content. Approaches to generative
AI
can provide versatile and realistic data at virtually no
cost and in no time. As generative
AI
with its breakthroughs resembles a rather new field to academia,
approaches to generative
AI
are in constant flux (Walters and Murcko, 2020). For further technical insights
into the field of generative
AI
, we refer the reader towards the work of Harshvardhan et al. (2020) as a
starting point.
Given its versatility, generative
AI
is capable to drive a plethora of applications stemming from various
disciplines such as e.g., healthcare, engineering, or business (Aggarwal et al., 2021; Kowalczyk et al.,
2022; Pan et al., 2019). To identify related work in the realm of digital marketing in particular, we
conducted a brief review of academic literature. By searching for the two concepts "marketing" as well
as "generative
AI
"within the abstracts of scientific articles
1
, we did not find a single article dealing
with the practitioner’s view of the opportunities or risks associated with generative
AI
in the field of
digital marketing. In total, the search yielded 61 results of which eight just hint at the possibility to
deploy generative
AI
in content marketing (Agarwal and Nath, 2023; Dwivedi et al., 2023; Kalpokas and
Kalpokiene, 2022; Kietzmann et al., 2021; Mayahi and Vidrih, 2022; Miao et al., 2022; Mustak et al.,
2023; Nowroozi et al., 2022) and five use the technology to enhance a predictive analysis in the context of
marketing (Butler et al., 2022; Chu et al., 2022; Li et al., 2022; Vamosi et al., 2022; Wünderlich et al.,
2022). In fact, only the work of Sivathanu et al. (2022) can be regarded as somewhat relevant. Here, the
authors investigate the customers’ online shopping intention after watching
AI
generated advertisements
(Sivathanu et al., 2022). They found perceived media richness to positively and perceived deception to
negatively influence a customers’ online shopping intention (Sivathanu et al., 2022). However, the authors
focus on the customers’ side rather than the marketers’ perspective who is in charge of designing an
effective marketing campaign beforehand. Consequently, there is an evident gap regarding related research
from an organizational perspective.
3 Method
To capture the practitioners’ perspective on the opportunities provided by generative
AI
for digital
marketing and its limitations, we conducted an exploratory interview study (Myers and Newman, 2007;
Schultze and Avital, 2011). Based on the foundations of digital marketing and generative
AI
, we devised
a semi-structured interview guide. This ensures the general structure of the interviews but at the same
time opens up the opportunity to include further relevant aspects. The resulting guide consists of three
blocks—namely, (i) general research topic explanation and interviewee introduction, (ii) application
opportunities, as well as (iii) limitations. Whereas the second part concerns the interviewee’s perspective
on the use of generative
AI
in digital marketing with its potential, the last block focuses on any criticism
or ethical concerns regarding generative
AI
as well as its technical or legal limitations. We selected the
interview partners (
IP
s) such that they meet two crucial criteria—technical affinity and a strong affiliation
with digital marketing in their respective organization. In total, we solicited nine experts with advanced
knowledge holding mostly senior or leadership positions (cf. Table 1). We conducted the interviews in July
2022 with an average duration of 60 minutes. After the initial section, we followed the semi-structured
1
Search query on the 14th of March 2023 for the databases Google Scholar, AIS electronic Library, IEEE Xplore, ACM Digital
Library, EBSCOhost and EconBiz: ("advertisement" OR "marketing") AND ("deepfake" OR "generative ai" OR "generative
artificial intelligence" OR "synthetic data")
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guide to have fruitful discussions with the practitioners regarding their perceptions, ideas, and thoughts.
We recorded and transcribed all the interviews for subsequent exploratory analysis by three researchers.
Given the excerpts, we deductively categorize and assign the statements to marketing application contexts
as well as limitations. This is done by the researchers via intensively discussing and unanimously agreeing
on the foremost relevant aspects of the interviews for the findings (Mayring, 2021).
# Job Title Industry
1 Senior Vice President Digital and Data Automotive
2 Marketing Project Manager Automotive
3 Head of Digital Marketing and Sales Retail
4 Executive Creative Director Digital Consultancy
5 Marketing Analyst Retail
6 Head of Digital Marketing and Sales Banking
7 Product Marketing Specialist Mechanical Engineering
8 Executive Creative Director Digital Retail
9 Senior Media Consultant Consultancy
Table 1. Overview of Interviewees.
4 Results
As this research is merely intended as the first step towards a better understanding of the use cases and
organizational benefits of generative
AI
in digital marketing, the results are regarded as preliminary and
are to be revisited and likewise extended in the future. The insights gathered from our
IP
s are arranged in
line with the two-part research question in two blocks—opportunities and limitations. In addition, we
structure the results in categories identified within our analysis.
4.1 Opportunities
Medium-independence. "In a classical marketing setting, this technology can for example be used at the
point of sale for advertisement banners. [...] Regarding virtual channels digital banners or chatbots can
be created with generative
AI
"(IP3). Accordingly, we conclude that the technologies’ application is not
restricted by the mode of conveying the marketing message and can therefore be considered regardless of
the medium of choice—analogous or digital.
Cross-border. Furthermore, generative
AI
is capable to "[...] blur the boundaries of physical limitations"
(IP5). Hence, it enables to use testimonials without restrictions such as time or geographical location.
"Aging is no problem. The flexible and dynamic use in multiple campaigns in parallel is possible" (IP4).
"This applies even if a person has already died" (IP7). Moreover, it allows marketers to "[...] create
entirely new personas as desired" (IP1).
Distinctiveness. Next, the practitioners recognize the content created with generative AI to be markedly
different if desired and thereby able to attract specific attention among customers. "In particular, the
representation of prominent people in the digital world can be expected to create interest" (IP2). "The
entertainment potential is enormous (IP8). Given the possibilities of generative
AI
, organizations are even
able to land new viral hits. This argument is supported by IP8: "You can stand out in digital marketing
and set special trends through such technologies". However, IP6 notes, "[...] interest in content produced
by generative AI will initially be disproportionately high, as many aspects are new and exciting. Curiosity
prevails in such early phases and generates attention, but will flatten out in later phases".
Personalization. "Market research has found that people value being noticed by a brand. If only in-
dividuals are addressed in a personalized way and feel special, this can have a very positive effect
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on the business" (IP7). Hence, the content produced with generated
AI
and targeted towards specific
individuals can be highly effective for the purpose of marketing. In that vein, "synthetic technologies can
act as an interesting approach to mass customization" (IP1). Furthermore, "a personal connection with
AI-generated content can create an emotional connection and conversation" (IP5).
Control. "If I want to achieve a purchase intention among my target group, I need to reach them with
a message that speaks to them. Relevance and added value are important keywords here. If I don’t do
that, then I risk losing my target group" (IP6). This can be done in a controlled manner with generative
AI
."You can place content for specific target groups" (IP6). This argument is supported by IP4 who
recons, "[...] a major advantage of generative AI is the localization of content in the context of market
and segment-specific communication". To this end, the customer segmentation approach as described
previously can be utilized for automatic content creation. However, in order to maintain control of the
intended effect of
AI
generated media, it is noteworthy "[...] that segments are not rigid and people switch
back and forth between different segments" (IP8).
Automation. "The benefits of synthetic media may lie particularly in its scalability through automation
[...] the use of generative
AI
can shorten many real processes in marketing" (IP5). An example is given
by IP7: "With generative
AI
, A/B testing for marketing campaigns can be highly automated to detect
opportunities and create impact".
Resource-saving. The use of generative
AI
can also result in cost savings. "It is possible to optimize costs
by creating media via algorithms and thus enabling more efficient work" (IP3). This argument is explicitly
backed by six of the other
IP
s. But it may also result in other types of resource savings as described by
IP
1 in the following. "Many customers order numerous items in e-commerce and only select a few of
the products. This results in a high average return rate of 70%, which could be reduced with a virtual,
realistic fitting room or more realistic product designs and personalized product presentation. This would
be a great relief both economically and ecologically".
4.2 Limitations
Misinformation. The use of generative
AI
could cause distrust on the customer’s side. "I see a huge
problem with text or content creation. It is already very difficult to distinguish what is real and what is not.
Trust could suffer as a result" (IP9). Furthermore, the technology might be abused to pose severe threats.
This is acknowledged by IP6. "Deepfakes carry a high risk of discredit. Political opponents, minorities,
dissidents, and individuals can be affected".
Polarization. The application of generative
AI
carries the risk of social polarization. "Social media
combined with the misuse of such technologies fuels the fragmentation of our society" (IP6). "[...] you only
see the things that suit you. Marketing messages can be conveyed more precisely. This may be good from
a corporate perspective, but it is questionable from a societal perspective. Many small niches are created
that ultimately lead to polarization. Data collection and the use of machine learning could strengthen
such a development" (IP1). These two statements clearly explicate and warn against this risk.
Bias. "There could be a bias in
AI
generated content which is considered extremely questionable from an
ethical point of view. Neural networks are prone to be systemically prejudiced due to training on biased
data sets (IP5). Such bias might also remain undetected until the damage is caused leading to adverse
effects such as high costs or reputational damage.
Privacy violations. A conflict may arise between the use of generative
AI
tools and personal privacy
rights. General data protection regulation (
GDPR
), for example, is a law imposed by the European Union
in 2016 to protect its citizens’ private data from unintended use. It forces organizations to adhere to
a strict data privacy policy. Now, generative
AI
can harm an individual’s privacy. "The application of
deepfake technology onto individuals is primarily a legal issue. Just because you can turn any person into
a deepfake video doesn’t mean you should. There must be consent for this" (IP9).
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5 Discussion
The results illustrate both—the opportunities and limitations associated with the use of generative
AI
in
digital marketing. In light of the insights from this first assessment, we derive the following essential
bottom lines.
•Opportunities:
◦
Medium-independence. Regardless of the means to convey the marketing message—analogous or
digital—generative AI can be leveraged.
◦Cross-border. Generative AI enables to cross physical boundaries such as geolocation and time.
◦
Distinctiveness.
AI
generated content can be novel and markedly different from current approaches
and thereby draw customer attention effectively.
◦
Personalization. The marketing approach can be highly customized to appeal directly to individuals,
especially on an emotional level.
◦Control. The output of generative AI can be managed such that it addresses customers according
to identified and targeted segment groups effectively.
◦
Automation. Generative
AI
can automate and thereby accelerate marketing processes including
evaluation.
◦Resource-saving. The use of generative AI holds the potential to reduce necessary resources.
•Limitations:
◦
Misinformation. The use of generative
AI
can lead to distrust on the addressees’ side or even
question the truthfulness of the marketing message transmitter—i.e., the brand.
◦
Polarization. The content produced with generative
AI
may polarize customers and thereby
intensify the development of media bubbles.
◦
Bias. Specific ethnic groups or characters may be negatively affected by biases in the generative
AI.
◦
Privacy violations. User profiling and individual tailoring of advertisements can violate personal
data privacy and thereby disobey laws or the individual’s comfort zone.
With the acquisition of new customers and the retention of existing ones at the heart of marketing activities,
leveraging the opportunities associated with generative
AI
enables novel and thereby distinct approaches to
shape the marketing mix. Marketers can unleash the potential associated with the deployment of generative
AI
by taking advantage of its opportunities while at the same time considering its limitations. For example,
by personalizing an ad to individuals’ characteristics or identified target groups, marketers can convey a
marketing message with a high degree of control. Besides, they can automate this hyperpersonalization
process with generative
AI
. However, in doing this they must comply with the law and privacy regulations.
Moreover, as implied by the possible limitations of the technology it is recommended to follow the ethical
code and obey the individual’s comfort zone.
Hence, the above implications help practitioners as initial guidelines to decide whether the use of
generative
AI
is feasible and fits the intended purpose while considering its opportunities and limitations.
Furthermore, it acts as a cornerstone to discuss the use of generative
AI
with decision makers and financial
providers, justify its use with stakeholders, and more specifically in coordination with the respective area
of application leverage the opportunities while at the same time mitigating the limitations.
6 Research Agenda
Based on the interviews and their implications, in the following, we outline a concise research agenda
with the overall aim to foster the use of generative
AI
in digital marketing while also addressing associated
limitations. The proposed agenda comprises three consecutive studies.
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Techniques. The first study concerns the comparison of the various techniques to generative
AI
to highlight
the foremost promising approaches for marketing. This can be done in two steps. By thoroughly analyzing
current literature on state-of-the-art approaches suitable for marketing an overview is created. Next,
by taking a data science perspective the approaches can further be explored regarding appropriateness
for advertisement content generation while especially including various data types (i.e., image-, audio-,
video-based, and textual data). Based on the expected findings, marketers can make an estimate on the
possibilities available and decide whether to try a specific generative AI technique or not.
Design. The second study is intended to assess the value of using generative
AI
techniques in marketing
economically. To this end, we propose to pursue a design-oriented approach. Hence, firstly a prototype is
designed with the generative
AI
component to then be demonstrated and evaluated regarding the actual
utility for marketers. Here, a single or even multiple application context(s) should be carefully chosen in
advance to allow for a meaningful value determination and thus reference for practitioners. The value can,
for example, reflect the human capital costs associated with marketing mix creation with or without the
use of generative
AI
. The design should be applicable regardless of the approach to generative
AI
, data
types involved, usage context, and the economic value to be calculated.
Adoption. The third proposed study is directed towards the identification of factors contributing to
the adoption of generative
AI
in marketing on a higher level. Therefore, we suggest to build on the
technology-organization-environment (
TOE
) framework as introduced by Tornatzky et al. (1990). This
helps to understand how the adoption and implementation of generative
AI
in marketing is influenced
by the various contexts. To this end, the questionnaire can be based on the application opportunities
and connected limitations identified within the present article. Accessing the crucial factors enabling
or likewise restricting the adoption of generative
AI
helps to build tools appropriately or carry out
countermeasures respectively.
We constitute this work to be a first assessment of the marketers’ view of generative
AI
, which does
not claim to be exhaustive. However, the results provide valuable implications for practitioners—both
software developers and marketers—and researchers alike to go beyond the frequent disregard of the
technology in the marketing discipline. Furthermore, the developed research opens up an avenue to anchor
generative AI as a commodity in marketing.
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