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In recent years, artificial intelligence (AI) has become an emerging trend in different fields: science, business, medicine, automotive and education. AI has also reached marketing. The aim of the paper is to research how deeply AI is applied in marketing and what implications there are for marketing practitioners. Authors stated two research questions - which areas of AI are used in marketing and what implications AI delivers for marketing managers. To answer those questions, the authors conducted research on secondary data with AI examples used for marketing purpose. The analysis of gathered examples shows that AI is widely introduced into the marketing field, though the applications are at the operational level. This may be the effect of careful implementation of the new technology, still at the level of experimenting with it. The uncertainty of the outcome of AI implementation may affect the caution in putting these innovations into practice as well. Gathered examples proved that AI influences all aspects of marketing mix impacting both consumer value delivery as well as the marketing organization and management. The paper delivers implications for business, especially ideas about implementing AI into marketing, designing innovations and the ideas on how to incorporate new skills into marketing team required by the new technology.
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Volume 8 | Number 2 | 2019
DOI: 10.18267/j.cebr.213
Jarek, K., Mazurek, G.
Krystyna Jarek, Grzegorz Mazurek / Kozminski University, Poland.
In recent years, artificial intelligence (AI) has become an emerging trend in different fields:
science, business, medicine, automotive and education. AI has also reached marketing.
The aim of the paper is to research how deeply AI is applied in marketing and what
implications there are for marketing practitioners. Authors stated two research questions -
which areas of AI are used in marketing and what implications AI delivers for marketing
managers. To answer those questions, the authors conducted research on secondary data
with AI examples used for marketing purpose. The analysis of gathered examples shows
that AI is widely introduced into the marketing field, though the applications are at the
operational level. This may be the effect of careful implementation of the new technology,
still at the level of experimenting with it. The uncertainty of the outcome of AI
implementation may affect the caution in putting these innovations into practice as well.
Gathered examples proved that AI influences all aspects of marketing mix impacting both
consumer value delivery as well as the marketing organization and management. The
paper delivers implications for business, especially ideas about implementing AI into
marketing, designing innovations and the ideas on how to incorporate new skills into
marketing team required by the new technology.
Keywords: artificial intelligence, AI, marketing, AI application, AI implications, AI in
JEL Classification: M31, M15
Artificial intelligence (AI) has lately become a very popular subject in the area of
management and marketing sciences, although, quite paradoxically, the works on its
development in other fields of science have been proceeding continuously for over half a
century. Over the years, AI has been appearing in and disappearing from the spotlight
depending on the level of its advancement and the increase in its potential applicability. The
interest in and the extensive discussion on AI are caused by the first wide-scale commercial
applications of AI, which have shown the potential and the capabilities of this technology
also in the area of marketing. The rapid development of AI in recent years has been
possible thanks to the advancement of the cognitive mechanisms of AI and of capabilities of
machines to learn based on the obtained data (Lieto, Bhatt, Oltramari, & Vernon, 2017), as
well as thanks to the possibility to create previously non-existing information (Grawal, Gans,
& Goldfarb, 2017). The power of AI also lies in the spectrum of processing of various
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formats of data - apart from numerical data, artificial intelligence processes texts, images,
and sounds, providing them with significance and relevance for further analyses (Dhar,
AI has been so far drawing the attention of engineers, IT experts, and analysts, but is now
moving outside its traditional areas of occurrence, making an increasingly stronger mark in
the field of management and marketing. The ever-increasing amount of consumer data
available online, in big data systems or mobile devices, makes AI become an important ally
of marketing, as it is based on data analysis in almost every area of its application.
Marketing takes advantage of data to a large extent - from consumer needs research,
market analyses, customer insights, and competition intelligence through pursuing activities
in various communication or distribution channels to measuring the results and effects of
the adopted strategies. Marketing becomes a natural beneficiary of developing information
technology (Mazurek, 2011a, 2011b, 2014). The proximity of both domains makes it
possible to achieve a synergy effect. Therefore, it seems important to emphasise the
potential of artificial intelligence and of the available AI-based tools and to discuss the
commercial applications of AI in the area of marketing.
The article is divided into four parts. The first part includes the key definitions of the ideas
related to AI. The second discusses examples of AI solutions implemented in the area of
marketing. The third part, being an effect of an analysis of the collected examples, provides
a description of areas of AI’s impact on marketing. The final part of the article covers the
opportunities and risks underlying the application of AI in marketing activity.
1 Theoretical background
1.1 Artificial Intelligence overview
Artificial intelligence derives from information technology. It is often used interchangeably
with notions like automation or robotization. It also tends to be confused with machine
learning or algorithm application. According to Oxford Dictionary, AI is “the theory and
development of computer systems able to perform tasks normally requiring human
intelligence, such as visual perception, speech recognition, decision-making, and
translation between languages” ("artificial intelligence | Definition of artificial intelligence in
English by Oxford Dictionaries", 2019). The technology based on artificial intelligence is
able to imitate the cognitive functions that we attribute to the human mind, including the
ability to solve problems and learn (Syam, Sharma, 2018). The role and of AI is to process
and identify the acquired data and then to perform certain tasks. This is the definition of the
so-called Artificial Narrow Intelligence, which functions and carries out tasks in a defined
area (Shanahan, 2015). The second type of AI is Artificial General Intelligence, whose
scope of intellectual capacity is comparable to that of the human brain (Sterne, 2017). The
current potential of AI works in a narrow area, and tasks are performed thanks to the
advancement of three technologies: machine learning, deep learning, and natural language
Machine learning (ML) has taken AI to a higher level, one above the level of following a set
of predefined rules. Therefore, ML has changed the role of algorithms that have been used
so far with AI. ML has enabled computers to learn by themselves based on the available
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data by establishing links between individual pieces of data. Thanks to these capabilities,
ML makes it possible to draw conclusions and form generalisations on the basis of
performed analyses (McIlwraith, Marmanis, & Babenko, 2017). ML comes in many forms
and may be presented as pattern recognition, statistical modelling, data exploration,
knowledge discovery, predictive analytics, data analytics, adaptive systems, self-organising
systems, and many more (Domingos, 2016).
Deep learning (DL) is a higher level of ML because it is based on learning algorithms that
do not need to be managed manually. DL, taking advantage of big data and computing
power (of, e.g. server farms, CPU power, cloud computing), makes it possible to decipher
and provide the result for a new piece of information instantly (Alpaydin, 2016).
Natural language processing (NLP) is one of the applications of ML and DL, aiming at
speech recognition. Many years of research in this area have made it possible to work on
large amounts of data (text samples) that act as sources of the context, the vocabulary, the
syntax, and the semantic meaning (Alpaydin, 2016).
Advancements conducted in those technologies have enabled the development of AI in the
areas of voice, text, and image recognition, decision-making, and autonomous robots and
vehicles. Practical applications can be met for each of these areas. Voice recognition is
available, for example, in smartphones (e.g. Siri, Google Assistant). Text recognition
solutions are used as virtual assistants who deliver rapid answers (e.g. Deakin University
and IBM Watson). Image recognition is used for payment approval, thanks to comparison
face image the system can make payments (e.g. food chain KFC). Decision-making system
is available for educational purpose IBM Elements is dedicated for teachers to support
them in student assessment and to deliver creates recommended individual development
path for each student. Finally, autonomous robots and vehicles are used in the warehouses
to manage the stock (e.g. in Amazon Kiva system).
1.2 Marketing mix
In 2013 by the American Marketing Association approved a new version of marketing
definition. According to the association “marketing is the activity, set of institutions, and
processes for creating, communicating, delivering, and exchanging offerings that have
value for customers, clients, partners, and society at large ("What is Marketing? The
Definition of Marketing AMA", 2019).
The critical aspect of marketing is the value delivery to customers (Grönroos, 2006), while
the value may represent different product aspects such as goods, ideas, services,
information, or any type of solution that fulfil customer needs.
McCarthy proposed the idea of “marketing mix” as a conceptual framework translating
marketing planning into practice (Bennett, 1997). Though the marketing mix is not a
scientific theory, its tools can develop both long-term strategies and short-term tactical
marketing programmes (Palmer, 2004). McCarthy refined previous Borden's conception of
satisfying the target market. He regrouped Borden's 12 elements (product planning, pricing,
branding, channels of distribution, personal selling, advertising, promotions, packaging,
display, servicing, physical handling, fact-finding and analysis) into four elements, called
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4Ps: product, price, promotion, and place. There were further advancements made within
the marketing mix concept, such as adding another Ps - people, processes, physical
evidence (Booms, Bittner, 1980), though the idea of 4Ps is still widely used and accepted.
2 Methodology
Due to the emerging trends of AI application in the business area, the aim of this paper is to
review the AI implementation in the field of marketing. The goal of the research is to assess
the scope of AI application within marketing mix and find answers to the research
questions: (1) do all areas of AI (voice, text, image recognition, decision-making,
autonomous vehicles and robots) find application in marketing; (2) what kind of implications
does AI make in marketing practice. To answer these questions, the authors decided to
conduct secondary data research to gather examples of AI application. The procedure of
collecting AI application was made in two steps. The first step was a focus on review the
marketing portals (,, to gather
AI application in marketing. The second step was the validation of selected examples by
confirming gathered examples with the information on the company site or in the press
release of each example. There was no limitation connected with the sample location; the
goal of the secondary research was to collect any example of AI application. The presented
conclusions are based on the results of the authors' analysis of gathered examples.
3 Results
To answer the first research question, (do all areas of AI find application in marketing?)
authors compiled all validated examples grouped according to the five AI areas which are
presented in Table 1. For each mentioned AI area (text, voice, image recognition, decision
making, autonomous vehicles and robots) authors have found AI application in marketing.
While text recognition, image recognition, decision-making technologies are widely used,
the voice technology, as well as autonomous vehicles and robots, are not so popular. This
can be caused by the greater complexity of both technologies. Moreover, autonomous
vehicles and robots are more often perceived as a part of Industry 4.0 domain than
marketing. Thus it is seldom perceived as a way of creating and developing innovations in
sales channel management, merchandising optimization or delivering customer service
which are placed in a marketing mix program within the ‘place’ domain.
Table 1 | Examples of application of AI in marketing
AI areas
Examples of application in marketing
Voice processing
Voice purchase requests made through a device or the Amazon Alexa
Virtual assistants are supporting task execution (Siri, Google Home,
Text processing
Use of a virtual assistant as a guide to walk you through a shopping
centre (Alpine.AI).
A virtual assistant embedded in a mobile bank app, taking advantage
of NLP, handles client requests alone by responding to their inquiries.
A virtual assistant is presenting application features, options to make a
purchase of bank products by oneself, and providing information about
the location of bank branches and cash machines (ING Bank Śląski).
A GPS navigation system that apart from showing the route to the
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selected destination suggests attractions found nearby or on the way
to the destination, and shows similar objects to those related to the set
destination (Naver).
An analysis of statements made by clients of banks, insurance
companies, and telecoms performed in order to diagnose irritating
situations, which led to the elimination of negative events that might
occur in the customer journey, and to modification of the customer
service process (Touchpoint).
Development and launch of new beer recipes, and modification of the
existing products thanks to information gathered by a chatbot
(Intelligentx Brew).
Development of a marketing campaign to launch a new car model -
the Toyota Mirai. Using data provided by a selected target group,
computers performed an analysis of texts and videos on YouTube in
order to teach the machines the preferred style of the said target
group. Next, through multiple iterations, they developed the first
creative advertising campaign, and the final texts for the adverts were
approved by the supervising team. The result was almost a thousand
of advertising spots tailored to the profiles of the ad recipients on
Facebook (Toyota, Saatchi&Saatchi).
Promotion of the Milonerzy TV show, the Polish edition of Who Wants
to Be a Millionaire?, on Facebook taking advantage of a
conversational chatbot. Maintaining the format and the style typical of
the show made it possible to offer new and unique experience (TVN).
recognition and
Face recognition as a way to make payments (KFC).
Recognising the condition of face skin, followed by an individual
selection of the type of face cream based on an analysis of one’s
photo and data, including information about the current weather
A photo as a medium to search for items online. Apart from search
results in the form of identical items, the search engine offers similar
or complementary items (eBay).
Using the client’s face image to select colour cosmetics individually
during online shopping (Estée Lauder).
Service-free bricks and mortar shop where video cameras analyse the
selected products and payments are made automatically (Amazon).
Electronic mirrors in a clothing shop that match the collection to the
client’s appearance, style, and taste (FashionAI).
Selection of the best Christmas gift by going through twelve best
suggestions. Based on the recognition of the buyer’s face and emotion
analysis, the programme suggested the best option to go for (eBay).
Identification of clients before the start of a video consultation by
comparing the video image with a photo provided earlier by the client
Embedded ML mechanisms make it possible to automatically frame
images according to the requirements of the brand and
communication channels (Adobe Sensei).
An image finder that makes it possible to select the best photos and
reject the less appealing ones (Everypixel).
Development of individual savings plan thanks to an analysis of the
funds available on one’s account, receipts, amount of expenses and
the way one spends their money. By comparing the financial
behaviour of a user and a given community, the application develops
a tailor-made savings plan to match the financial capabilities of a
given person (Plum).
Travel destinations matched individually based on the traveller’s
musical preferences. Apart from the city, the app chooses specific
districts and attractions to match the user’s profile (Spotify, Emirates).
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A chatbot is preparing a cocktail recipe using the ingredients the
consumer has at home and based on the consumer’s preferences.
The chatbot analyses 300 recipes and offers the best-matched
solution (Diageo Simi Bartender).
Based on the user’s mobile phone data (location, sun exposure time),
the app indicates the right level of UV protection filter (Monteloeder).
Dynamic matching of prices to the user based on their shopping
record visited websites, or the owned mobile phone (,
Matching adverts to user characteristics based on one’s online history
(ING Bank Śląski).
New product recommendations (Amazon, Netflix).
GO-I-PACE, an application is analysing one’s driving style, route
choices, and frequency of charging the car (electric car). Based on the
results, the app offers suggestions on how to drive the care in a more
efficient and effective manner (Jaguar I-PACE).
ZozoSuit helps customers order clothes fitted perfectly to their figure.
Thanks to in-built 150 sensors, ZozoSuit makes it possible to take
150,000 measurements (Start Today, StretchSense).
A platform to manage marketing campaigns online. In the first weeks,
AI learns the specificity of a given company, then, based on data
analysis, comes up with recommendations concerning the campaign
strategy (Albert AI, Harley Davidson).
Detecting faults and errors in product functioning and forecasting
malfunction occurrences. The synchronisation of the work performed
by the technical team responsible for device (lift) monitoring and repair
works (if necessary) (KONE, IBM Watson IoT, Salesforce Einstein).
Creation of a consolidated customer record regardless of the products
purchased and used, linking customer data from every company area
(Sales Cloud Einstein, U.S. Bank).
Synchronisation of customer data from all possible points of contact
with the brand (social media, website, e-mail, phone conversation). All
interactions are aggregated and presented in one place in order to
offer improved customer service (Salesforce, Adidas).
robots and
Service-free shops (Ford & Alibaba, Amazon Go, Zaitt Brasil).
A robot used to check the stock on shop shelves and the arrangement
of the products displayed. Information of shortages or incorrect
arrangement is sent to the service staff, who take their time to look
into the reported issues (Schnuck).
An autonomous shop is offering basic and fresh products and
magazines, able to travel independently to the warehouse in order to
replenish the stock. The shop was tested in Shanghai (Moby Mart).
Source: authors
4 Findings and Discussion
4.1 Implications for marketing
To answer the second research question (what kind of implications does AI make in
marketing practice?) authors conducted an analysis of gathered examples and made a
synthesis of how the examples reflect the marketing mix. Conclusions are presented in
Table 2. Each validated examples shows that AI impact each area of marketing mix
program. This fining is especially important for practitioners who are responsible for
developing innovations as AI influence the whole spectrum of marketing activity. It is worth
mentioning that the area of ‘place’ requires cooperation with Industry 4.0 specialist, as
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autonomous vehicles and robots play a crucial role in creating new sales channels and new
customer service. Additionally, AI applications that extend the core product requires
innovation design approach to find the insights that match ideas going beyond the product
or even category. This Is important for implementing AI within ‘product’ and ‘promotion’
areas in the marketing mix program.
Table 2 | Areas of the impact of AI on marketing mix
Promotion (Brand)
Place (Sales &
New product
additional value
solutions beyond
product category
management and
dynamic price
matching to
customer profile
Creating a unique
Creating the wow
factor and offering
Elimination of the
process of learning
product categories
Positive impact on
the customer
Convenient shopping
The faster and
simpler sales process
24/7 customer service
Purchase automation
Service-free shops
customer support
New distribution
Source: authors
The analysis of the collected cases shows that AI activities have a two-way impact on
marketing. On the one hand, the beneficiary of changes is the consumer, but on the other,
new solutions affect the entirety of the pursued marketing activities.
4.2 The impact of AI on consumers
Just as the Internet has brought about many advantages from the consumer’s point of view,
such as automatic recommendations and relevant product suggestions (Grewal,
Roggeveena, & Nordfältba, 2017), shorter shopping time (Moncrief, 2017), or customer
service personalisation (Jordan, & Mitchell, 2015), AI goes one step further and offers new
opportunities in marketing activity. The analysis of the collected examples of the application
of AI in marketing shows a whole spectrum of advantages that AI offers to consumers:
More convenient and quicker shopping time thanks to improved processes (e.g.
automatic payments, the better quality of search engines, 24/7 customer service).
New consumer experience via mass-scale hyper-personalisation, after-sales
service that creates additional value going beyond the basic product.
A new dimension of the consumer-brand relationship delivered by building
surprise and delight minimised post-purchase dissonance thanks to the possibility
to test the considered product virtually, elimination of the process of category
learning, and finally taking advantage of benchmarking against other users.
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4.3 The impact of AI on marketing management
When it comes to marketing management, AI has a significant impact on contemporary
practices, and will surely require a new approach to tasks fulfilled in marketing teams:
Elimination of laborious and time-consuming activities. AI automates routine
and repeatable tasks (e.g. data collection and analysis, image search and
Bigger significance of creative and strategic activities. Precise analyses
performed by AI increases the role of creative and strategic activities to build
competitive advantage.
Design innovations. AI redefine the way the value is delivered to the customer
and increase the role of finding new solutions through design.
Developing new competences in the marketing team. AI requires incorporating
data scientist skills as well as an understanding of the new technology possibilities
in the marketing team.
A new marketing ecosystem. The complexity of AI increases the role of
companies producing AI solutions. Due to the current level of AI advancement (the
level of Artificial Narrow Intelligence), there is a need to develop a new model of
cooperation with AI entities offering data engineering or ML tools.
The analysis confirmed that AI is applied in many areas of marketing. The commercial
solutions based on it take advantage of all five AI areas: image recognition, text recognition,
decision-making, voice recognition and autonomous robots and vehicles. While the first
three are applied quite extensively in marketing, the instances practical application of voice
recognition are rare and developed by the biggest tech companies such as Amazon,
Google, Apple, or Microsoft on a large scale. Similarly, the autonomous vehicles and robots
are not so frequent solution, as this area is much more connected with Industry 4.0, than
innovation design within the marketing mix.
AI in marketing tends to be currently implemented at the operational level, usually as one-
off initiatives or activities. This may result from the fact that we are dealing with the first
instances of the practical application of AI, and companies are careful with implementing
this new technology, experimenting with it. The costs related to the development of new
concepts and the uncertainty of the outcome of their implementation may affect the caution
in putting these innovations into practice as well.
When analysing product popularity, i.e. of Salesforce Einstein and Albert AI, it seems that
the first implementations inspire trust to AI solutions and companies are more willing to take
advantage of them if they see positive results of their application.
The analysis of the collected examples shows that AI offers a new quality to a consumer’s
life. 24/7 customer service, hyper-personalised solutions, more convenient shopping, or the
possibility to avoid making the wrong choice all contribute to a new dimension in the area of
the marketing organisation.
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These changes have an unquestionable impact on the functioning of marketing
departments and organisations. Most of all, it necessitates introducing new functions and
skills to marketing teams, i.e. people with the right knowledge about AI, data science and
qualifications in design and implementation of new solutions. It is also about managing a
new model of cooperation with the entities offering advanced AI solutions and reaching a
synergy effect with regard to AI and other functions.
The research proved that AI applications are incorporated in all areas od marketing mix as
well as five different AI technologies are used within marketing practice. As the authors
found that the first AI applications are made as a single implementation, often as an
experiment, there is a need for further research to assess the impact of AI on marketing,
especially the business effect.
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This study considers the use of digitalization technologies in the management of marketing of a commercial company. As a result, the problem of bringing empirical knowledge about the importance of digitalization technologies in the company's marketing management to a theoretical model has been solved. Digitalization technologies for implementation in marketing activities were selected and the decomposition of marketing management in a commercial company was performed. On this basis, a theoretical model for assessing the impact of digitalization technologies on the marketing management of a commercial company has been developed. The results of an empirical study based on the developed theoretical model proved the truth of the hypothesis about the positive impact of the spread of the use of digitalization technologies on the marketing management components of commercial companies. The choice of specific digitalization technologies is explained by the essence of marketing as a management activity. Structural decomposition of the marketing management process is explained by traditional management functions and clarifies the criteria for choosing technologies. The results of calculations according to the formed theoretical model are explained by the development of the platform economy and ecosystems, which involve the studied big data technologies, cloud computing and social media, as well as blockchain. The methodical approach to substantiating the role of digitalization technologies proposed in this study is characterized by the use of fundamental provisions on cybernetic systems, management theory, and modern marketing concepts. The obtained results make it possible to find out the presence and direction of the effects of the spread of the use of digitalization technologies in the company's marketing management and make informed decisions about the business digitalization strategy
... Artificial intelligence (AI) is the subject of theoretical and applied research in many areas. For a relatively short time, it has also undergone research in the area of marketing [4,5], which is closely related to popular digital marketing. Since 2020, significantly growing interest has been observed in AI in marketing [6,7,8]. ...
Purpose : The study aims to demonstrate the importance of artificial intelligence (AI) and examples of tools based on it in the process of enhancing (building, measuring, and managing) customer engagement (CE) in social media in the higher education industry. CE is one of the current essential non-financial indicators of company performance in Digital Marketing strategy. The article presents a decision support system (DSS) based on social media engagement management with the use of AI-based tools in a higher education industry case study. Methodology : The study was based on an analysis of the literature on AI in conjunction with CE, the results of research – 2022 Social Media Industry Benchmark – prepared by Rival IQ, and qualitative research (in-depth interviews with experts) at selected universities in Poland. At a later stage, the interviews were transcribed, thematically analyzed, and open coding with NVivo was performed. Findings : The conducted study was of an introductory and exploratory nature. It recognises the significant role of AI in enhancing CE in social media. At the same time, examples of AI-based tools that can be used for this have been indicated. It was unequivocally stated that by implementing AI in marketing, universities can act more effectively and consequently enhance their non-financial performance. For them, it is a system that assists decision-making in the field of social media engagement management. Research limitations : Due to its preliminary nature, the study used secondary sources (Rival IQ Report 2022) and individual in-depth interviews with three managers of promotion/marketing departments, which does not give a complete picture of the situation under analysis. However, it is the first step in research on this subject that is to be continued. The theoretical contribution : The conducted research demonstrated the role of AI in enhancing customer engagement in social media in higher education while at the same time showing its auxiliary role in the decision-making process. Practical implications : Specific tools such as Sprout Social or Rival IQ were identified that, when applied in universities, can measure the engagement rate and effective CE management in social media used by the university.
Purpose This study evaluates the current responsibilities, skills, and knowledge sets required by marketing managers. Furthermore, it seeks to make projections about the future of the marketing management profession. Research Method Three different studies were conducted to achieve our objectives. The first analyses 600 job descriptions, the second gathers insights from marketing managers using a survey, and the third explores the perspectives of the marketing managers regarding the future of their profession. Results The results analyze and systematize the key activities and responsibilities of marketing managers and highlight the role of future marketing managers. The study concludes that changes in technology and big data will have a significant impact on the marketing management profession. Research Value This paper makes three primary contributions. First, it identifies the qualifications and skillsets sought among marketing managerial positions. Second, it contributes to research within the marketing domain by examining the interface between marketing management function and automation by generating insights that highlight how technology affects the role of marketing managers. Finally, it presents a snapshot of guidelines to direct companies in guiding skill development and understanding future requirements for the marketing manager position.
Artificial intelligence is a transformational general purpose technology that is impacting marketing as a function and marketing managers' activities, capabilities, and performance. The job of a marketing manager will be evolving into understanding which kind of artificial intelligence can and should be applied to which kind of marketing actions for better performance. Marketing managers will have to go through a learning curve and acquire new skills. The aim of this chapter is to present the tertiary literature on AI and to discuss the future of AI's impact in the field of marketing. The literature on AI for marketing is growing steadily. This article starts by describing some key concepts of the AI literature. It then illustrates a few important consequences of AI on businesses in general. Finally, it unfolds some of the important ways on how AI is already impacting, and will continue to impact, marketing managers' activities, capabilities, and performance. The article ends by discussing the future implication for marketing managers.
Business organizations are implementing various advanced technologies viz. computing, software and storage for improving the understanding of their content to enable them to undertake effective business decisions. Business organizations in the emerging economies are rapidly searching for technologies with enhanced service capabilities that could be leveraged throughout organizations. Whether structured or unstructured, content organizations aim at extracting relevant information from wide range of content and the amalgamation of artificial intelligence, internet of things and machine learning technologies supports the companies to achieve this objective. Across the globe, it has been estimated that Asia-Pacific is creating ample opportunities for content intelligence technique to flourish and witness high growth rate. The paper attempts to explore the possibility of how content intelligence is transforming business processes in the emerging economies. Additionally, it aims at delivering detailed insight into the trends and driving forces towards the growth of content intelligence technique in emerging economies.
Der vorliegende Beitrag fokussiert die Bedeutung nachhaltigkeitsorientierter Unternehmenskultur für den Einsatz von künstlicher Intelligenz im Marketing. Spezifischer skizzieren wir die Einflussmöglichkeiten auf den Einsatz nachhaltiger künstlicher Intelligenz zur Förderung des Sustainable Development Goals 12 (Nachhaltige/r Produktion & Konsum). In einem diskursiven Ansatz führt dieser Beitrag Erkenntnisse aus der Literatur zur nachhaltigkeitsorientierten Unternehmenskultur, nachhaltigen künstlicher Intelligenz und dem Nachhaltigkeitsmarketing zusammen und bildet diese mithilfe des St. Galler Managementmodells ab. The paper highlights the importance of sustainability-oriented corporate culture for the use of artificial intelligence in marketing. More specifically, we outline the influence on the realization of Sustainable artificial intelligence for promoting Sustainable Development Goal 12. This article unites scientific insights from the literature on sustainable organisational culture, sustainable artificial intelligence and sustainability marketing and draws from the St. Gallen Management Model to illustrate this connection.
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The term "Cognitive Architectures" indicates both abstract models of cognition, in natural and artificial agents, and the software instantiations of such models which are then employed in the field of Artificial Intelligence (AI). The main role of Cognitive Architectures in AI is that one of enabling the realization of artificial systems able to exhibit intelligent behavior in a general setting through a detailed analogy with the constitutive and developmental functioning and mechanisms underlying human cognition. We provide a brief overview of the status quo and the potential role that Cognitive Architectures may serve in the fields of Computational Cognitive Science and Artificial Intelligence (AI) research.
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Retailers have embraced a variety of technologies to engage their customers. This article focuses on " The Future of Retailing " by highlighting five key areas that are moving the field forward: (1) technology and tools to facilitate decision making, (2) visual display and merchandise offer decisions, (3) consumption and engagement, (4) big data collection and usage, and (5) analytics and profitability. We also suggest numerous issues that are deserving of additional inquiry, as well as introduce important areas of emerging applicability: the internet of things, virtual reality, augmented reality, artificial intelligence, robots, drones, and driverless vehicles.
Experts have suggested that the next few decades will herald the fourth industrial revolution. The fourth industrial revolution will be powered by digitization, information and communications technology, machine learning, robotics and artificial intelligence; and will shift more decision-making from humans to machines. The ensuing societal changes will have a profound impact on both personal selling and sales management research and practices. In this article, we focus on machine learning and artificial intelligence (AI) and their impact on personal selling and sales management. We examine that impact on a small area of sales practice and research based on the seven steps of the selling process. Implications for theory and practice are derived.
The world of sales research continues to transform as we move more into the world of social media. This article briefly examines a historical examination of sales research by decade and then presents a model of sales research needs going forward based on how social media is being implemented by selling and buying organizations. The model assumes that the selling process is incorporated within a social media world and elements within the model include the salesperson and sales center, the buyer and buying center, the use of artificial intelligence, the teleselling unit, the interaction between marketing and sales departments, and the methods of selling. Discussion follows the model, focusing on how social media is being used among and between units in the selling process. The discussion unfolds on three key parts of the sales equation: (1) the sales organization, (2) the buying organization, and (3) the interaction between marketing and selling departments. Research questions follow each of the three discussion sections.
Machine learning addresses the question of how to build computers that improve automatically through experience. It is one of today’s most rapidly growing technical fields, lying at the intersection of computer science and statistics, and at the core of artificial intelligence and data science. Recent progress in machine learning has been driven both by the development of new learning algorithms and theory and by the ongoing explosion in the availability of online data and low-cost computation. The adoption of data-intensive machine-learning methods can be found throughout science, technology and commerce, leading to more evidence-based decision-making across many walks of life, including health care, manufacturing, education, financial modeling, policing, and marketing.
Advances the case for a new marketing paradigm: a paradigm that is driven by the needs of the market, rather than the organization. Examines the notion of targeting, from the perspective of the buyer, a concept labelled by the author as “buyer disposition” - the process undertaken by buyers when sourcing a product or service. Argues that the disposition of the buyer towards a product or service, or supplier, during the sourcing process can be represented by five criteria, termed the “five Vs”: value, viability, volume, variety and virtue. Proposes that the five Vs can be used in conjunction with the marketing mix to enable a supplier or provider to achieve a more detailed understanding of the buying process, and ultimately of product/service adoption. Argues that a fundamental understanding of buyers’ needs and wants, through the eyes of the buyer, will help to generate a stronger and more strategic focus on the achievement of marketing objectives.