The Role of Human Marketers in AI-Driven Marketing Decisions and the Impact of Collaboration Between Humans and AI on Outcomes
Abstract
The research’s aim is to identify the part that human marketers play in making advertising choices in AI-driven processes and to evaluate the way cooperation between AI and humans affects results. Chapter 1 emphasises the goal, objectives, and research questions used to identify the study’s research areas as well as the problem statement, the rationale, and its importance.
Chapter 2 examines a variety of literary sources to present ideas about AI-driven marketing, the function of human marketers, the results of this, as well as theoretical applications and knowledge gaps. However, several difficulties, including employee opposition and ethical issues, have accompanied the use of AI in marketing decisions. The chapter has highlighted several suggestions and theoretical applications to address new problems with AI integration.
A variety of processes that are systematically incorporated into research are informed by methodology. Chapter 3 provides background information on the methodological approaches used in this study to explore the function of human marketers in AI-driven marketing and their influence on the results. In order to give detailed information about the research process, it emphasised the selected philosophy, approach, design, and ethical considerations for the research.
Chapter 4 explores the secondary materials that were gathered and look for different themes in the data sets that were gathered. The chapter includes a discussion along with the thematic analysis, which creates a connection between the findings and the literature review.
Chapter 1: Introduction
1.1. Introduction
“Artificial Intelligence or AI” has revolutionised the marketing processes creating opportunities for the marketing teams of businesses. The application of AI combines technology, consumer information, and the brand’ prior experience to deliver precise insight into marketing trends and consumer journeys enhancing marketing efforts. Various AI technologies like “Machine Learning or ML”, “natural language processing or NLP”, “sentiment analysis”, and decision-making processes help business firms gain a competitive edge over their rivals. This chapter highlights the aim, objectives, and research questions to determine the research areas along with the problem statement, rationale and significance of the research.
1.2. Background
Human marketers play a crucial role in deciding marketing strategies based on the analysed data of AI, which increases the effectiveness of marketing strategies. The marketing data including purchasing history, website surfing, product like on the website, and product wish list are a significantly vast amount of database (Popkova and Gulzat, 2020). Therefore, AI plays a crucial role in analysing this type of enormous database, which saves the time of humans and allows them to invest time in creativity and innovation in other marketing aspects (Mgiba, 2020). The implementation and utilisation of AI products are increasing in the marketing field, which is positively affecting overall marketing effectiveness.
The well-known product ChatGPT is being widely used in the marketing aspect with its vast amount of database, which not only helps companies to analyse the market but also generates important market strategies based on market research (Longoni and Cian, 2022). As ChatGPT is being used in generating personalised marketing content, it is helping companies to make content related to a specific customer base. The role of AI in understanding market situations and customer preferences is reflected in making tailored emails, product advertisements, and recommendations, which increases the marketing strategies’ efficiency (Hair Jr and Sarstedt, 2021). Based on the increasing uses of AI, it is expected that the market revenue of AI is about to increase from USD 241.8 billion in the year 2023 to USD 740 billion by the year 2023 (Statista, 2023). It not only shows the market capture capability of AI but also the competency of AI in analysing and understanding marketing data and strategies.
Figure 1.1: Market size and revenue of AI
(Source: Statista, 2023)
The forecasted 17.3% CAGR shows the market dominance of AI in industries such as finance, education, and the health sector (Statista, 2023). The collaboration of humans with AI makes it more effective due to the speed of AI and the understanding of the real-time market preference by humans.
1.3. Research aim and objectives
The aim of the research is to determine the role human marketers play in marketing decisions in AI-driven processes and to assess the impact of collaboration between AI and Humans on the outcomes.
- To determine the role AI plays in marketing
- To identify the effect of collaboration between AI and Human marketers on decision-making and marketing analytics
- To assess the advantages of human-AI collaboration in attaining consumer engagement and higher efficacy in marketing campaign
- To analyse the challenges of AI implementation in the marketing process and to highlight appropriate recommendations
1.4. Research questions
- What role does AI play in the marketing process?
- How does human-AI collaboration affect the marketing analytics and decision-making process?
- What are the benefits of human-AI collaboration to obtain higher efficiency in marketing campaigns and enhance consumer engagement?
- What are the challenges of AI implementation in marketing and how can they be resolved?
1.5. Problem Statement
The increasing competition in today’s market has made the requirement for AI prevalent, which has also developed various challenges in the context of collaboration between human marketers and AI (Youn and Jin, 2021). One of the primary challenges that have increased the concern for companies to share their marketing information with AI in terms of compromising data security. Developing an entire AI system is barely possible for small and medium businesses; therefore, these businesses are required to collaborate with AI companies to increase their marketing strategy’s efficiency (Paschen et al. 2020). The distribution of roles and responsibilities is essential in a collaborative environment, which can align humans and AI towards the same goals. Human marketers can provide critical insights about the market’s situation, ethical suggestions, and decision-making.
On the other hand, AI excels at data analysis and automation, an undefined role and responsibility can negatively influence human credibility and decrease collaboration’s effectiveness. Another crucial issue that affects the collaboration is most of the AI is trained on old data and barely has direct access to current market data, which increases the chance of wrong prediction of market analysis (Xu et al. 2020). The marketing strategies often require quick responses and changes based on the market’s situation, which increases the difficulties for AI as the system has barely real-time access. The dynamic market has various reasons for changing customer preferences, which require real-time analysis.
The outdated AI training data can produce the wrong result of analysis, which can affect both the human and AI effectiveness. Error in AI analysis of the historical market situation and outcome of new products can create various challenges, which can severely affect the overall company’s marketing operations (Youn and Jin, 2021). Moreover, the effectiveness and credibility of human-AI collaboration depend on these factors, which increases challenges.
1.6. Rationale
In recent years, AI has become an integral part of the marketing efforts of numerous global businesses. AI has assisted business firms in facilitating social media listening, which helps human marketers track and identify the perception of the audience regarding their brand (Puntoni et al. 2021). On the other hand, sentiment analysis assists in gaining a deeper understanding of the targeted audience and determining their buying motivation along with the impact of various environmental factors on their buying decision (Bharadiya, 2023). This research has provided an overall view of the benefits AI has provided in marketing processes and highlighted the importance of human involvement as well. Therefore, this study can determine the essentiality of human-AI collaboration in the marketing process to obtain positive results.
1.7. Significance of research
The research has aimed to analyse the role human marketers play in AI-driven marketing. It helps in determining the importance of humans in the marketing process to utilise AI-driven insights appropriately (Haenlein and Kaplan, 2019). As a result, it can highlight the proper utilisation process of insights and decisions provided by AI technologies to improve marketing efforts by involving human marketers. On the other hand, it also informs the essentiality of humans in the marketing process despite the overwhelming capacity of AI (Enholm et al. 2022). In this way, the research is highly significant in comprehending the marketing efficiency AI provides without replacing human marketers.
1.8. Structure of dissertation
Figure 1.2: Structure of dissertation
(Source: Self-created)
1.9. Summary
The chapter has shed light on the research areas by informing the research aim and objectives. The research has set out to examine the role of human marketers in AI-driven marketing and determine its impact. Due to its analytic capabilities, AI has become one of the most common elements in popular business organisations, especially in their marketing segment. It has enabled them to analyse a wide range of data regarding consumers, their purchasing habits, and their experience. AI technologies have assisted in personalised marketing and providing precise insight regarding marketing trends. Additionally, human-AI collaboration has been able to deliver more suitable results regarding the marketing processes.
Chapter 2: Literature Review
2.1. Introduction
In recent years, the integration of AI has become one of the most common trends in marketing, which has facilitated collaboration with human marketers. “Artificial intelligence or AI” has been able to enhance creativity and provide appropriate solutions to assist in the decision-making process as well. The collaboration between humans and AI is known as “Hybrid Intelligence”, which combines the advantages of both sides to improve personalisation and consumer experience. The literature review analyses various literary sources to provide various concepts related to AI-driven marketing and the role of human marketers and its outcome along with theoretical application and literature gap.
2.2. Concepts
2.2.1. The role of AI marketing
AI significantly influences the ground of marketing through its development and the capability to analyse and generate vast marketing information. AI plays a crucial role in marketing analysis by providing insights about the market and customer preferences, which helps marketers, take action according to the needs of the market. As per the understanding of Verma et al. (2021), AI is the technology, which can do work that requires multiple humans and resources to accomplish. In terms of assisting, AI helps marketers analyse and get information regarding the market situation very quickly. Vlačić et al. (2021) stated that AI also plays a crucial role in tracking real-time activities and market demands, which allows companies to determine their next move and focus on the ground that needs development. Additionally, AI also helps in reducing personal bias in the decision-making process along with “24*7” assistance for consumers. Through the application of this technology, marketers can enhance their risk-taking capability by assessing market data and social trends.
Figure 2.1: Advantages of AI in marketing
(Source: Verma et al. 2021)
According to Huang and Rust (2021), AI has significant quality in innovation and controls on data-driven markets, which helps firms to get new ideas and innovation to influence customer buying behaviour. The quality of reading an enormous amount of data makes AI an incredible asset in various industries and their marketing departments. Vlačić et al. (2021) stated that analysing customer purchasing behaviour and their surfing history allows AI to assume customers’ current and future needs. Additionally, AI can handle customers’ general queries based on information, which allows to access the server and database with AI chatbots. It has reduced the scope of human errors in repetitive tasks, which has enhanced the efficiency of marketers and provided real-time data about the consumers improving the endorsement strategies.
2.2.2. The impact of human-AI collaboration in marketing analytics and decision-making process
The capability of AI is crucial in the large data-driven market, as the technology allows marketers to get valuable insights in a short time. According to De Bruyn et al. (2020), AI is capable of handling vast amounts of data and repetitive tasks, while humans can contribute to customer handling in an empathetic manner. Market analysis requires data such as customer purchasing, surfing history, whitelisting, recommendations, and product liking, which makes the database significantly larger for a human to analyse. Therefore, AI can play a crucial role in market analysis by utilising its capability to read large data structures, which can help humans get market insights. According to Vlačić et al. (2021), despite these capabilities, AI in its current state is still incapable of comprehending all human thoughts and feelings. As a result, comprehending customer sentiments and feelings is of the utmost significance in the field of marketing.
Eriksson et al. (2020) stated that AI power systems also help to track real-time market behaviour and trends, which helps marketers, make decisions accordingly. The collaboration of AI and humans significantly increases the efficiency of market analysis among various industries. Mustak et al. (2021) suggested that AI has aided human marketers to enhance their efforts, as the technology is capable of analysing enormous volumes of data in a relatively short period rather than becoming a substitute for human marketers. AI necessitates the use of humans to supply ideas and commands by comprehending people’s emotions and market needs in response to changing market conditions. In terms of comprehending client feelings, AI provides a systematic response because technology lacks the senses to comprehend human emotions.
The generated responses, according to Huang and Rust (2021), are also similar and repeated, which has a detrimental impact on the customer experience. As a result, the prospects of AI replacing humans in the near future are slim, making human-AI collaboration critical. AI also play a crucial role in enhancing product and service personalisation through market data analysis, which allows humans to make services accordingly. According to Kumar et al. (2019), the personalisation recommendation of products and services for customers influences companies’ decision-making process. The personalisation makes the service more customer-centric, which influences companies’ decision-making in customer approach. According to Statista (2023), approximately, 88% of companies across 35 countries use AI to influence customer journeys by personalisation. The collaboration shows the impact on market analysis and decision-making, which highlights the dependency of humans on AI. However, over-reliance on AI possibly resulted in a lack of understanding of individual unique and subtle aspects of customer relationships.
Figure 2.2: Use of AI in marketing
(Source: Statista, 2023)
The personalisation designed by AI is based on real-time consumer interaction, which recommends products and services to humans to make further decisions. As the understanding of Saura et al. (2021), AI can alert about sudden changes in the market, which humans use to change strategies. Developed countries such as the US are utilising hybrid intelligence, which is expected to make the US the largest AI market by 2023 with a value of around USD 87.18 billion (Statista, 2023). The AI-designed personalisation requires humans to ensure that these recommendations are aligned with the company’s ethics and values, which affect company decision-making. Buntak et al. (2021) state that human marketers can disapprove of AI suggestions in case they are found not appropriate for a particular audience, which also influences a company’s decision-making. Moreover, working with AI allows humans to understand its capabilities and influential qualities in market analysis and strategy making.
2.2.3. Benefits of human-AI collaboration in consumer engagement and efficiency of marketing campaign
The benefits of hybrid intelligence play a significant role in enhancing customer engagement and market efficiency. Soni et al. (2020) stated that AI helps to design highly “personalised marketing messages” and recommendations by analysing vast amounts of data, which significantly helps to enhance customer engagement. On the other hand, humans use AI-generated data to assist individuals in the betterment of customer experience. AI-power chatbots can assist customers 24/7, while humans to increase customer satisfaction handle complicated queries. Puntoni et al. (2021) stated that the engagement of hybrid intelligence plays a significant role in increasing customer engagement with companies. AI also help to reduce the flow of customer general queries, which reduces human error in customer general responses. Therefore, maintaining trust and customer engagement crucially depends on hybrid intelligence.
Ai is also capable of discovering undetectable behaviour of customer approach, which helps marketers to understand customer’s requirements and required strategies to be changed. According to Saura et al. (2021), AI helps to identify the effective time and medium to reach the customer, which helps marketers deliver messages and notifications with the highest possibility of getting it seen. Delivering the required information at the right time increases customer engagement. However, Kopalle et al. (2022) claimed that the heavy reliance on hybrid intelligence for customer engagement could increase the chance of a robotic approach style. AI is capable of providing recommendations based on data, but it is possible to miss the understanding required regarding the customer’s emotional attachments.
Figure 2.3: Incorporation of AI and Human Intelligence
(Source: Statista, 2023)
The AI-human collaboration also helps companies to increase their marketing campaign efficiency, which affects overall organisation productivity. Rafieian and Yoganarasimhan (2023) stated that AI is capable of completing repetitive tasks and reporting, which frees up time for employees to concentrate on more complex tasks. AI can process enormous amounts of data to get valuable insights about the market, which helps firms make more effective market campaign decisions. According to Statista (2023), approximately 87% of leaders in various organisations have confirmed that the interaction mode of hybrid intelligence, where AI generates recommendations and humans decide is the most effective way. However, De Bruyn et al. (2020) argued that the overuse of AI might lead to over-automation in marketing campaigns, which can negatively affect the companies’ creativity and innovation. In addition, a heavily data-centric approach can miss uniqueness and quality in the marketing approach, which highlights the importance of human marketers to maintain the balance.
2.2.4. Challenges of implementation of AI in the marketing processes
The implementation of AI in the marketing process poses various challenges, which force companies to avoid the implementation process of AI. As per the understanding of Chan et al. (2022), one of the primary challenges that companies face in the implementation of AI is the limited skills and expertise, which prevent companies from achieving effective marketing processes. Approximately 34% of organisations are facing the same issue during the implementation of AI in their marketing process (Statista, 2023). In addition, companies also find it difficult to recruit individuals who are enough trained in AI and marketing. Shah et al. (2020) stated that due to the lack of knowledge and expertise before the implementation of AI companies face challenges in understanding the aspect of AI and the field it would control. Therefore, such companies are likely to avoid the usage of AI in their marketing process and use traditional processes.
Figure 2.4: Challenges of AI implementation
(Source: Statista, 2023)
Devang et al. (2019) opined that AI technology requires advanced software, systems, and server infrastructure, which creates another challenge for companies regarding expenses. The initial investment of purchasing tools, platforms, and required hardware is expensive; small businesses find it difficult to incorporate AI into their marketing process. As per Statista (2023), around 29% of organisations face a high price issue when making an initial investment in their marketing process. The expense also becomes a major factor, when recruiting AI experts and technicians, which makes companies, use traditional methods and old employees in the marketing process. Stone et al. (2020) opined that data infrastructure and regular maintenance also influence the company’s budget and prevent the implementation of AI. The technology requires robust infrastructure for storing data and regular basis monitoring and measuring information, which also increases the issue related to expense.
Data complexity is another challenge in incorporating AI in marketing processes that relate to data. AI technology requires a large amount of data to process and analyse, which allows the technology to understand the market trend and context. As per the understanding of Basri (2020), diverse data sources such, as customer purchasing, social media, website visits, and market trends are primary the source of marketing data. Managing this diverse data can be a complex task for an AI system, which requires expertise to develop and maintain AI algorithms. According to Statista (2023), in terms of managing complex data around 24% of companies face challenges in the implementation of AI in the marketing process. Furthermore, data volume, quality, “data privacy and compliance”, also poses additional challenges in AI implementation.
2.2.5. Recommendations to overcome problems related to the AI-driven marketing process
The incorporation of AI into the marketing process has remarkable qualities to enhance market efficiency. However, due to some challenges, the incorporation of AI has become difficult, which can be addressed through various steps. As per Wang et al. (2020), minimising the challenge of a lack of skilled workforce and expertise in AI required specific training programs for the existing workforce and IT teams. It can significantly upskill the capability of the employees, which is expected to increase their understanding of AI. The training program requires to be tailored in such a way that can bridge the gap of knowledge that employees specifically need. According to Devang et al. (2019), the issue can also be addressed by collaborating with universities and organisations specialising in AI programs. Furthermore, companies can provide online courses for remote workers that are connected with their marketing process, which can help them to educate about the operation of AI.
Figure 2.5: Cost-benefit analysis
(Source: Basri, 2020)
Addressing the high-cost issue in initial investment can be minimised through the cost-benefit analysis, which can provide an overview to the firm about the long-term value of AI marketing. Basri (2020) stated that the cost-benefit could allow firms to choose carefully the tool, platform, and servers, which can minimise high expense issues in AI’s initial implementation stage. Additionally, companies are required to invest in data management to address the issue of handling complex data. De Bruyn et al. (2020) stated that companies could also work on data cleaning and structuring processes, which can help AI algorithms extract exact insights. Furthermore, incorporating visualisation tools can help employees to understand complex data.
2.3. Theoretical application
Kotter’s 8-step Model of Change
Kotter’s 8-step model of change depicts various stages through which change can be effectively integrated into existing work processes (Laig and Abocejo, 2021). The resistance of the employees toward change can hinder the implementation of AI in the marketing process. Kotter’s change model highlights the importance of creating urgency by determining challenges and creating a vision to communicate with the employees to highlight the importance of change in the existing framework (Harrison et al. 2021). Providing employees with training and rewards and creating short-term goals can also help with the integration of AI into the marketing processes.
Figure 2.6. Kotter’s 8-step Model of Change
(Source: Laig and Abocejo, 2021)
The 5Ps of Marketing AI
The 5Ps of marketing AI inform five separate stages; planning, production, personalisation, promotion, and performance. In the first stage, buyers’ personas and goals are planned to produce data-driven content for various marketing channels to improve personalisation through appropriate content, offers, and product recommendations (Peyravi et al. 2020). In the “promotion” stage, advertising expenses are broken down into various channels and performance is monitored to evaluate the results (Palanivelu and Vasanthi, 2020). The application of this theory can help human marketers strategically incorporate AI in their marketing efforts to acquire positive outcomes.
Figure 2.7: The 5Ps of Marketing AI
(Source: Palanivelu and Vasanthi, 2020)
2.4. Literature gap
The study has effectively analysed various concepts, benefits, and drawbacks of AI-driven marketing. However, the change in the skill requirement of marketing professionals and ethical challenges has not been analysed in depth. The lack of skill and ethical concerns have negatively affected the implementation of AI (De Bruyn et al. 2020). It has created a gap in the study, which can be addressed by investigating the impact of AI-driven marketing on the skill requirements.
2.5. Summary
The chapter has shed light on a few concepts regarding the role of human marketers in AI-driven marketing decisions and their impact. AI has emerged as one of the most significant trends in the marketing industry as it has been able to analyse consumer data and their buying behaviour to predict market trends and upcoming changes. Along with that, AI-human collaboration has resulted in improved personalised services to consumers. However, various challenges such as employee resistance and lack of skills have been associated with the implementation of AI in marketing decisions. The chapter has highlighted various recommendations and applications of theory to resolve emerging challenges of AI integration.
Chapter 3: Methodology
3.1. Introduction
Methodology informs various processes, which are systematically integrated into research. It offers a reliable and valid approach to address the objectives, questions, and aim of the research, which also includes the process of data collection along with the analysis process. This chapter informs the incorporated methodological techniques in this research to examine the role of human marketers in AI-driven marketing and its impact on the outcome. It has highlighted the chosen philosophy, approach, design, and ethical considerations for the study to provide comprehensive information regarding the research process.
Figure 3.1: Research onion
(Source: Mardiana, 2020)
3.2. Research philosophy
Research philosophy is a belief regarding the appropriate procedure of data collection regarding a phenomenon. It is generally connected to the acquirement of knowledge and the identification of suitable sources, which correlate with the nature of the study (Al-Ababneh, 2020). Research philosophy generally ensures the implementation of appropriate collection and analysis processes for data regarding the research. It is generally divided into three main categories; Positivism, realism, and interpretivism. Positivism has higher reliability on factual knowledge to obtain specific insight regarding a certain phenomenon (Newman and Gough, 2020). The application of positivism philosophy in research requires a large set of data and assists in gaining objective knowledge. On the contrary, realism relies on scientific assumptions to acquire knowledge; however, it ignores human perspectives. On the other hand, interpretivism helps in analysing an event relying on the societal value system and offers an in-depth understanding.
In this study, interpretivism has been selected as the research philosophy to examine the role of AI in marketing and the requirement of human marketers in the AI-driven endorsement process. Interpretivism generally needs a small size of samples and offers comprehensive knowledge regarding the event. As a result, interpretivism has assisted in gaining in-depth insight regarding the application of AI in marketing strategies. Furthermore, interpretivism has helped in comprehending human behaviour, which has assisted in analysing the role human marketers play in AI-driven marketing approaches and their effect on the outcome (Newman and Gough, 2020). Hence, Interpretivism has assisted the research in analysing the combined effort of human marketers and AI in marketing strategies and their outcomes.
3.3. Research approach
The research approach is a common way of conducting a study and it has a significant contribution to collecting and analysing research data. The research approach can be segmented into three different parts such as inductive, deductive, and abductive. The inductive approach is known for its usefulness in achieving goals, objectives, and research questions from the study (Greening, 2019). The deductive approach plays a crucial role in resting the reliability of the research’s hypotheses or assumptions. The approach involves a general idea or theory, which helps to judge the credibility of the research. In contrast, the abductive approach helps to analyse and examine phenomena, which is not yet understood or explained.
Figure 3.2: Comparison of inductive and deductive approach
(Source: Greening, 2019)
The deductive approach allows collected data to test the hypotheses based on existing theories and ideas. On the other hand, the inductive approach promotes collecting data first and identifying themes and patterns in the proceeding steps (Budianto, 2020). This research has used an inductive approach to examine or analyse the role of human marketers and AI-driven market collaboration. The approach has also helped in understanding the importance of human marketers in an AI-driven market. The utilisation of the inductive approach has shed light on the research’s outcomes and complications regarding the AI-driven marketing approach.
3.4. Research design
The research design is considered a conceptual framework for scientific studies, as it allows for identifying techniques and ideas appropriate for the research. The research design also plays a crucial role in planning and selecting the way that the research is expected to follow further (Dzwigol, 2022). Furthermore, research design has various benefits such as providing a clear research objective and an understanding regarding the outcome of the research. It also helps to increase the validity and reliability of research by minimising the bias and irrelevant variables in research. Increasing the credibility of collected data is another crucial benefit of research design, as it ensures that the data is collected in a systematic way (Babii, 2020). The classification of research design can be differentiated into five stages “descriptive”, “experimental”, “correlational”, “diagnostic”, and “explanatory”.
The “descriptive design” refers to a specific situation or case that is being studied. Researchers use this method to concentrate on collecting and analysing data, which can provide a clear understanding of the subject they are researching (Dzwigol, 2022). The “experimental design” focuses on exploring the cause and effect of the elements related to the research. It helps to understand how a single change of an element can affect other elements. “Correlational research” design analyses the relationship of two different variables that are closely related. The research technique involves analysing two different sets of data and barely considering any assumption to reach a conclusion (Sileyew, 2019). The “diagnostic research” focuses on the root cause of a specific phenomenon and uncovers the exact reason for the problem. It involves three steps such as “inception of the Issue”, “diagnosis of the issue”, and “solution for the issue.“Explanatory research” focuses on exploring and providing explanations for certain aspects of a subject. It analyses a situation by focusing on questions related to what, how, and why. This research is conducted under the selection of descriptive design, which is expected to shed light on the role of human marketers and AI in the context of making marketing decisions. The outcome of utilising the descriptive research method can provide plans for development and making informed judgments regarding the role and impact of human marketers and AI in the decision-making process. This design plays a crucial role in providing an understanding of the problem statement, which is not clear. The method’s framework also helps others to understand the overall research’s crucial points such as the relation between various variables, challenges, opportunities, and solutions.
3.4. Research strategies
Research strategies are the plans or schemes, which carry out the activity of searching and analysing information related to a certain event. It generally influences the data collection method of the study, which is selected based on the research objectives, questions, and available time (Snyder, 2019). Research strategies are divided into seven different categories; experiment, survey, case study, action research, grounded theory, ethnography, and archival research. Surveys are able to generate a large amount of data and experiment is a process of detailed study, which utilises empirical methods. In the experiment strategy, phenomena are tested within a rigorous and controlled environment to analyse the impact of various factors on each other.
On the other hand, action research is a process of systematic research, which aims to solve general challenges through identifying effective solutions. It includes the complex dynamics of society and aims to solve the problems, which occur in specific situations (Rumsey et al. 2022). In addition to that, case studies have emerged as one of the most popular research processes regarding industrial marketing. Grounded theory is another research strategy, which relies on qualitative methods and helps in developing a theory based on systematic data collection and data analysis (Urcia, 2021). On the contrary, ethnography favours intricate, complex, and qualitative social research through different techniques such as “observations”, “note-taking”, “interviews”, and others. Lastly, archival research analyses historical archives, journals, and articles to identify gaps in research.
In this research, a case study has been chosen as the research strategy as it is one of the common methods for marketing contexts. The case study method has helped in investigating the role of AI in marketing in the context of the real world. It has assisted in collecting a wide range of information regarding human marketers and AI related to industrial marketing from various sources. It has also helped in performing a broad investigation related to the challenges of AI applications in marketing.
3.5. Research choices
Research choices are of three types, mono, mixed, and multi-method, which inform the process of conducting research. It generally depends on the type of data such as qualitative and quantitative, required to be used in the research. The Mono method generally includes only one type of data such as qualitative or quantitative in a specific research. On the other hand, the mixed method utilises both quantitative and qualitative information to gain a comprehensive view of an event (Vebrianto et al. 2020). On the contrary, multi-method utilises more than two types of processes to collect relevant information for the research. For example, the combined use of content analysis and thematic analysis along with quantitative processes such as surveys can be a form of multi-method.
This research has incorporated the mono method as it is one of the simplest methods and it uses only one type of data. The Mono method has assisted in reducing the time of data collection on the role of AI in marketing. It has assisted in gathering a specific type of data related to the research, which has simplified the analysis process as well.
3.6. Time Horizon
The time horizon is essentially the time frame of the research, which includes the data collection process. It generally determines the various points of time data has been collected during research. Time horizons are of two types; cross-sectional and longitudinal. Cross-sectional studies generally require the collection of relevant data at a certain point in time (Ryder et al. 2020). On the other hand, longitudinal studies require the collection of information at various points in time as they aim to analyse the changes over time. As a result, the data collection process for longitudinal studies requires more time compared to cross-sectional research. In this research, a cross-sectional process has been used to accumulate relevant information regarding the role of human marketers in AI-driven marketing. It has helped in collecting suitable data in a swift procedure as it only needs to collect information at a specific point in time (Zawacki-Richter et al. 2020). Therefore, the cross-sectional approach has aided in reducing the time required for the data collection process for this research.
3.7. Sampling method
Sampling techniques are known as the process of selecting specific samples to gather data. It has two groups known as “probability sampling” and ‘non-probability sampling”. Probability sampling provides strong assumptions by allowing random samples of data sources, which makes it easier to analyse and explain, while, non-probability sampling prefers specific and relevant data sources (Patel and Patel, 2019). Additionally, probability sampling has been classified into four different stages such as simple random, stratified, systematic, and cluster sampling. Simple random sampling allows equal opportunities for various data sources chosen for the research, while systematic sampling allows a process of choosing data based on random at regular intervals criteria (Acampora et al. 2022). On the other hand, Stratified sampling divides data into subcategories such as age, gender, and location, which later allows one to randomly choose from each of these subcategories. Cluster sampling encourages creating groups or clusters of data sources, which represent the characteristics of the entire data.
In this research, simple random sampling has been involved to collect informative information on the collaboration of humans and AI in marketing decisions. It is the easiest process of choosing samples, which also reduces personal bias. Using this technique has helped researchers to collect information on the role of AI-human collaboration.
3.8. Data collection
Data collection refers to the way of accumulating information regarding a certain event to proceed with research. Based on the process, data collection is divided into two categories; primary and secondary. Primary data collection helps in collecting information directly from the participants through the use of surveys, interviews, polls, and others. On the other hand, secondary data collection gathers information from existing journals, articles, and other scholarly sources (Wohlin and Runeson, 2021). Primary data collection generally requires more time compared to the secondary process. In this research, secondary data collection has been integrated to acquire information from existing sources to analyse the role of human marketers in AI-driven marketing.
Figure 3.3: Secondary data collection sources
(Source: Wohlin and Runeson, 2021)
Additionally, the data collection method can be further divided into two types; qualitative and quantitative. The quantitative data-gathering method helps in collecting statistical information; whereas, the qualitative method assists in collecting descriptive information (Djafar et al. 2021). In this research, qualitative data has been chosen for conducting a secondary research process. The research has integrated a secondary qualitative method to gather relevant information regarding the hybrid marketing method, which highlights the cooperation between AI and human marketers from existing articles, news reports, journals, government publications, and online sources.
3.9. Data analysis
Data analysis is the process of analysing, understanding and later converting the raw data into understandable insights. The process of analysing includes various tools, methods, and approaches, which allows an understanding of ongoing trends. In this research, thematic analysis has been used to assess secondary qualitative information regarding the role of collaboration between human and AI-driven markets. The benefit of thematic analysis is its flexibility, which allows the researcher to identify patterns in collected data (Chivanga and Monyai, 2021). Thematic analysis also helps to analyse large data by dividing the large data into small data sets, which decreases the distraction for the researcher.
3.10. Inclusion and Exclusion Criteria
The inclusion-exclusion criteria provide few guidelines for the collection of resources. The inclusion criteria for gathering secondary resources are English language, sources published from 2019 to 2023, and related to AI applications in marketing. On the other hand, exclusion criteria are sources which are in languages other than English and older resources than 2019. The inclusion and exclusion criteria have assisted in collecting relevant and recent information in the research.
3.11. Ethical considerations
Ethical Considerations are the principles and guidelines, which ensure that the research is conducted in an ethical and responsible manner. Protecting the confidentiality of collected data requires putting the data in a “password-protective folder”. Providing a fully authentic outcome of the research is ensured by strictly avoiding plagiarism (Willmott, 2020). The literary sources that have been incorporated in this research as data sources are only to build a foundation for the research. Furthermore, the research has been adhering to the data protection law by ensuring it complies with legal standards for handling and safeguarding data (Tayebi Abolhasani, 2019). The collected data of the research has been stored in a “password-protected folder” to ensure proper data security.
3.12. Conclusion
The chapter has shed light on the various processes of constructing the research and chosen strategies for conducting the research on the role of human marketers in AI-driven marketing. A secondary qualitative method has been chosen as the data collection process and thematic analysis has been used to assess the collected data to identify themes and patterns within the collected data set. The ethical considerations provide an overview of research guidelines which has helped to maintain the authenticity and credibility of the research. Additionally, the justification for chosen strategies is elaborated in this chapter.
Chapter 4: Findings and Discussion
4.1. Introduction
The application of AI has assisted businesses in gaining in-depth market insight in a short period with a higher capability of processing data. On the other hand, human marketers have been able to make the proper decision based on the assessment provided by AI analytics and deliver targeted advertisement content to attract specific segments of consumers. In this chapter, the collected secondary resources have been examined to identify various themes within the collected data sets. In addition to the thematic analysis, the chapter also includes a discussion, which establishes a link between the literature review and findings.
4.2. Thematic analysis
4.2.1. Theme 1: The benefit of human-AI collaborations in marketing efficiency
Marketing efficiency depends on the collaboration of AI-human. Which allows companies to make effective strategies. AI has a significant quality to analyse a vast amount of data faster compared to a human (Longoni and Cian, 2022). It allows marketers to have various valuable insights regarding market trends and situations, which help to make operational decisions. This quality of AI also helps to understand market competitors and customer behaviour, however, making decisions requires human consciousness (Lin et al. 2019). Therefore, AI-human together play a crucial role in understanding market trends and implementing business strategies. AI is significantly capable of tailoring promotion offers, and marketing messages based on individual needs to influence customer’s purchasing behaviour (Taddeo et al. 2019). It allows AI to create high-quality personalisation choices and offers, which also increase customer engagement with the brand.
Furthermore, proper implementation of collaboration of AI-human approach can help businesses to increase customer conversion rate. AI can analyse customer data, provide recommendations for products, and tailor messages, which can impress customers to change their purchasing decisions (Kumar et al. 2021). On the other hand, humans can use that AI-generated information to personally approach customers, which will not only increase customer engagement but also build a good relationship between customers and organisations (Shah and Murthi, 2021). In terms of email marketing, AI plays a crucial role in tailoring specific emails, which can impress or change customer-purchasing decisions. Mail drafts by AI can have various attractive information and it can help humans approach customers based on the AI recommendation. As AI is capable of analysing vast amounts of data, it allows the technology to predict future crises and demands.
Therefore, AI can forecast future market demands, which can help humans to maintain inventory accordingly. The predictive analysis can ensure the stock, fulfil customer demands and increase marketing efficiency (Luo et al. 2019). Moreover, getting knowledge about future demands provides a significant benefit to the companies in terms of saving costs. As the companies already have the products in their stock, it can reduce the expense caused by the uncertain increase in product price during market crises. Moreover, AI can also save costs in repetitive tasks and optimise marketing, which can allow marketers to plan and implement cost-effective strategies.
4.2.2. Theme 2: Emerging challenges of human-AI collaborations in marketing decision-making
The collaboration of AI-human in enhancing marketing efficiency has various challenges, which not only affect an organisation’s productivity but also its credibility. Data privacy and ethics are one of the primary challenges that AI implementation in marketing approaches faces (Krishen et al. 2021). Extracting outcomes based on AI-generated recommendations, messages and offers required analysing a vast amount of customer data. Protecting that data and ethical utilisation is the primary concern for the companies due to the hackers and bad elements in the organisations (Leone et al. 2021). The companies are also required to ensure customer regarding the ethical use of their data for enhancing the marketing facilities not for their gain. Until May 2023, approximately 1.67 billion euros worth of fines has been filed against companies for not following the “General Data Protection Regulation or GDPR” (Statista, 2023). Therefore, the primary challenge for AI-utilising companies is to ensure that the use of consumer data complies with GDPR.
Companies need to store a vast amount of customer data to analyse market trends by AI, which increases the threats of “cyber threats” and “security breaches”. Protecting the data from malicious activities and malware attacks by hackers is the primary concern for companies to maintain their credibility in terms of protecting customer trust (Prentice and Nguyen, 2020). An adversarial attack is one of those threats, which involves the manipulation of data that can cause misclassification for AI during data analysis. This manipulation is barely visible to human eyes, which makes it more severe by generating content that is not appropriate for the specific customer base.
Figure 4.1: Type of Cyberattacks
(Source: Statista, 2023)
There are also other various methods of attacking such as ransomware and network breaches, which can also affect and steal the stored customer data. In 2002 ransomware was the most common type of cyber attacking method with 68% reporting in all-over attacks, while network breaches were in second place with over 18% of the detected attacks (Statista, 2023). Furthermore, another common challenge in this context is the lack of expertise and skills, which increases the difficulties in operating and understanding AI-generated outcomes (Herhausen et al. 2020). Due to the newest technology in the market, there are very few experts available, which is also expensive for small businesses. Insufficient personnel with AI expertise emerging challenges in terms of human-AI collaborations in enhancing marketing.
4.2.3. Theme 3: Suggestions for resolving issues of human-AI collaboration in marketing
In terms of minimising challenges in human-AI collaboration in marketing approach, companies can use various techniques. Companies are required to develop strict regulations to comply with GDPR, which will protect firms from penalties. Regular audits and monitoring data handling can also secure companies from data protection challenges (Manser Payne et al. 2021). Firms need to build good communication with customers in terms of the usage of customer data, which will increase the companies’ transparency. Allowing customers to know about the way their information is going to be used will also help the firm to increase data handling transparency. Developing a strong code of conduct within the organisation can help to tackle the bad elements within the firms from misusing customer information.
Tackling security and cyber-attacks have different approaches, in terms of security; companies can introduce a 2-step authentication process for their employees to access the server room. It will ensure that only the authorised person has access to the server room and the person will be responsible for any wrong activities (Tong et al. 2020). Tackling cyber-attacks companies can build their firewall systems or collaborate with cyber security experts, which will ensure the protection of the firm from cyber-attacks. Furthermore, investing in robust cybersecurity and ransomware protection can also help companies enhance their data security (Leone et al. 2021). However, detecting adversarial attacks is quite different and requires an advanced adversarial detection mechanisms system in the AI, which will identify the manipulation and provide alerts to the humans.
Enhancing the workforce’s skills and knowledge requires investing in their personal development, which can help firms have talented people to handle customer data. Companies can introduce AI training and education to increase the workforce’s knowledge about the technology they are working with (Luo et al. 2019). It will not only ensure an increase in their effectiveness but also help the firm tackle any emergency by utilising its talented workforce. Companies can work on cross-function collaboration between marketing teams and AI experts, which can allow the companies a good utilisation of AI-generated outcomes. Furthermore, companies can outsource AI experts in terms of getting benefits of cost-effectiveness in terms of small businesses.
4.2.4. Theme 4: Impact of change in skill requirement in marketing professionals for AI-driven marketing
In the aspect of the changing marketing landscape, the impact of changes in skill requirements in marketing professionals is a crucial factor. AI has taken a pivotal role in shaping the marketing dynamics, which has affected the skill demand from marketing professionals. Traditional skills such as creativity, brand management, and strategic thinking are still vital for marketing professionals (Tong et al. 2020). However, in recent years, business firms have been focusing on acquiring marketing professionals, who have a thorough understanding and working knowledge of AI tools, the ability to interpret insights from AI, and adequate proficiency in data analytics (Pedersen, 2021). One of the major impacts, which has emerged due to the popularity of AI-human collaboration in marketing, is the essentiality of data literacy.
Data literacy assists marketing professionals in handling and interpreting the huge amount of data that AI generates through the analysis process. It also highlights the importance of marketing professionals in understanding data quality, data sources, and appropriate use of consumer information in the AI-driven marketing context (Wirtz et al. 2020). As data-driven decision-making in the marketing process gradually becomes normal for business enterprises, marketing professionals, who lack data literacy, can find it hard to acquire employment. Another requirement for marketing professionals is cross-disciplinary skills to collaborate with IT specialists, AI developers, and data scientists to incorporate suitable AI solutions swiftly into marketing strategies (Prentice et al. 2020). The requirement for effective project management and communication ability is required to reduce the gap between technology and the marketing team.
Figure 4.2: Ethical concerns of AI-driven marketing
(Source: Wirtz et al. 2020)
Additionally, AI plays a significant role in facilitating personalised marketing, which requires marketing professionals to develop their ability to leverage AI-driven consumer information to plan highly personalised and targeted endorsement campaigns (Manser Payne et al. 2021). It highlights that marketing professionals need to have appropriate knowledge regarding consumer behaviour analysis, ethical utilisation of the personalisation process, and AI algorithms. On the other hand, transformation in skill requirements has a direct implication on training and education. Marketing professionals need to have access to upgraded and ongoing training programs to obtain the necessary skills for using AI in the marketing process (Prentice et al. 2020). Global business firms have also taken active initiative by providing marketing employees with adequate training sessions to improve their knowledge of AI tools and understanding of AI algorithms.
4.2.5. Theme 5: Assessment of ethical concerns related to AI-driven Marketing
Various ethical challenges have emerged as AI has been continuously integrated into marketing efforts, which demand effective mitigation strategies and careful consideration of human marketers. One of the significant ethical concerns of AI-driven marketing consent and data privacy. AI-driven marketing process highly relies on consumer data to provide personalised content and strategies to target a certain consumer segment (Pallathadka et al. 2023). It raises doubts about the process of collecting consumer information, storage, and utilisation as consumers often unknowingly share personal and confidential information. In this case, human marketers need to ensure consumers have been asked for their consent regarding sharing their information and informing consumers about the data practices.
Another ethical challenge for AI-driven marketing tools is the biased outcome in case the AI has been trained with inappropriate information. It can result in the integration of societal biases in the marketing efforts in cases not regulated by the marketers carefully. Biased results from AI-driven marketing tools can lead to discrimination and unfair targeting, which can damage the reputations of business firms in the global market (Maedche et al. 2019). Therefore, human marketers need to be careful to audit and curate their data sources to ensure fairness and inclusivity by reducing biases. Moreover, AI-driven marketing also depicts questions regarding human replacement in certain marketing tasks. AI has been able to automate several tasks, which has increased the redundancy of human efforts resulting in job loss. In this case, ethical concerns highlight the importance of supporting the displaced workers and providing them with a scope of upskilling and retaining their employment status.
4.3. Discussion
Although humans can contribute to client handling in an empathic way, AI is capable of processing massive volumes of data and repetitive jobs. The amount of data needed for market analysis, including browsing history, whitelisting, product preferences, and consumer purchases, makes the database much bigger for a human to evaluate (De Bruyn et al. 2020). The findings have also supported the effectiveness of human-AI collaboration as it has highlighted that despite the capability of AI to analyse vast amounts of data, human cognition is still needed for decision-making to understand market competition and consumer behaviour. Thus, the combination of AI and humans is essential for comprehending market trends and putting company plans into action.
Further research on the challenges regarding AI-driven marketing has highlighted significant issues regarding data privacy and ethics. The findings have highlighted that adversarial attack, which entails data tampering could lead to AI misclassifying data during data analysis. The fact that this manipulation is so subtle that it produces content unsuited for the target audience only serves to exacerbate its severity (Longoni and Cian, 2022). Additionally, findings have depicted that the application of “two-factor authentication” can provide higher security for consumers, which can reduce the scope of data theft. The literature gap has highlighted the need to provide an in-depth analysis Of the skills requirement of marketing professionals and an assessment of various ethical concerns in the context of AI-driven marketing.
The literature gap has been effectively addressed in the findings as it has provided a comprehensive analysis of the topics. Businesses have been concentrating on hiring marketing specialists with sufficient expertise in data analytics, a solid grasp of AI tools, and the capacity to decipher AI insights in recent years. The importance of data literacy is one of the main effects of AI-human collaboration in marketing that has become apparent. Marketing professionals can handle and analyse the massive amounts of data generated by AI analysis with the help of data literacy (Chan et al. 2022). Additionally, it emphasises the importance for marketing experts to comprehend data sources, data quality, and proper consumer information use in the context of AI-driven marketing.
As a result, it has highlighted the ethical concern regarding human marketers becoming replaceable in case they do not have proper data literacy. Cross-disciplinary abilities are another need for marketing professionals to work with IT experts, AI engineers, and data scientists to quickly integrate appropriate AI solutions into marketing plans. The marketing staff has to be more proficient in communication and project management. As a result, various business firms are providing marketing professionals with training sessions to help them develop the required competencies and Knowledge (Maedche et al. 2019). However, the findings have also highlighted that AI has replaced human efforts in many marketing tasks, which needs to be addressed by the business firm to provide the displaced staff with adequate opportunities to retain their employment and upskill.
4.4. Conclusion
The chapter has highlighted various themes by assessing secondary data collected on the role of human-AI collaboration in marketing. It has discussed the importance of human-AI collaboration in terms of marketing approach. The elaboration on AI-human collaboration challenges highlighted various influential factors. It has also discussed AI’s capabilities for tailoring offers and messages based on a customer database. The recommendations discussed provided various ways to tackle the challenges that lie in AI-driven marketing. The ethical challenge that human marketers can eventually be replaced in case they lack the necessary data literacy has also been brought to light by this research.
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