Application of Artificial Intelligence in Strategic Marketing Decision-Making Processes

Authors: Viktoria Mauz , Massimiliano Decarli, PhD , Gilberto Mendez, Michael N.

Abstract

This paper explores the perceptions of using artificial intelligence (AI) for strategic marketing decision-making processes. The qualitative research study uses a multiple case approach method. Data is collected through subject matter experts (SME) interviews and use cases to identify the main themes on how humans are using AI to make better strategic marketing decisions. The data analysis identified four main themes: efficiency, quality, trust issues and limitations, and big data analysis for prediction. Further, it has been examined whether these themes can be found in the use cases related to marketing. With the data triangulation, the result of the research suggests that AI is well accepted as a tool to support the strategic decision-making process in marketing and may support humans by making strategic decisions. For instance, by identifying patterns through big data. However, although AI would be technically capable to do so, humans seemingly do not want to let AI make decisions completely autonomously without the possibility of a human’s final judgment. The findings additionally suggest that AI is rather used to support the strategic decision-making process than make the decision itself. This implies that AI is less about replacing humans in practice but about enhancing the decision-making process. Further, the paper suggests that AI can support humans with making better decisions by also predicting future scenarios considering the effects of a potential decision.?

Keywords: Artificial intelligence, strategic decision-making, marketing decision, competitive intelligence.

1.?Introduction?

Drastic changes in technology over the past few years have triggered the adoption of digital systems that have changed our modus operandi. According to Microsoft and Accenture, 80% of marketing leaders believe AI will impact marketing in the next three years, with 84% of 80% expecting that AI will help enable better workflow (IMM Graduate School, 2022). Furthermore, global marketing automation spending will reach $25 billion by 2023 (IMM Graduate School, 2022). In an online survey done by the McKinsey Group, 1843 organizations were asked about their adoption of AI. Of those respondents, 1013 said their organizations had adopted AI in at least one function (McKinsey Group, 2021). Due to advancements in technology and the vast amount of consumers’ data available to firms, AI is becoming necessary for companies to remain competitive in the current landscape.?

The aim of this paper is to determine how AI can contribute to making better strategic decisions in relation to marketing.?

The research problem is that marketers often are unsure how to use AI on a strategic level to make better decisions and how AI can contribute to the decision-making process. Thus, this qualitative study aims to explore how AI is used in strategic decision-making processes to make better marketing decisions.?

With the aim of closing the identified gap in the literature of Borges, Laurindo, Spinola, Goncalves, Mattos (2021) in terms of how AI can contribute to a strategic decision-making process. Moreover, a lack of research about AI in marketing strategic decision-making was identified by Stone, et al., (2020).?

The introduction covers motivation and the necessity of why the research is required. The second chapter contains the conceptual framework and a literature review about AI in decision- making. The research methodology including the research method, sampling methods, the research question, data collection, and data analysis method will be described in chapter three. The results thereof will be presented in chapter four and discussed in chapter five by triangulating the data. The paper will conclude with chapter six, where the key findings and suggestions are illustrated.?

2. Conceptual Framework and Literature Review Framework

Conceptual Framework?

For this paper the conceptual framework is based on the Model by Colson (2019) and mentioned that AI-based decision-making processes require human involvement with non-digital data (Colson, 2019). This framework has been chosen to make full use of big data to achieve better business decisions by using AI technology (Sparrefors & Brodin, 2021). The following figure shows the Colson model, which illustrates the combination between AI and human judgment for making a business decision.?

Broken down the process is demonstrated as follows: Big data is supplied to the AI algorithm. Subsequently this algorithm, trimmed to the needs and wants of the user, runs the data through it, coming to a conclusion for a possible action. Once the possible action has been defined by the conglomerate which represents the machine it is moved to the next part. The human takes the possible action from the machine and judges them with its additional, non-digital information (Miyamoto et al., 2021). Based on the judgment of the possible action, the human will then undergo the last step which is interconnecting the non-digital information (Fernandes, Jardim and Lopes, 2021).?In this case the business decision based on AI can be made (Colson, 2019). This model has limitations as it does not consider deep learning factors as well as modifications to the AI algorithms, which oftentimes, cannot be done by individuals and may be tweaked (Lebovitz, Levina, & Lifshitz-Assaf,?2021). In the illustrated concept, AI is rather used to support humans in making business decisions instead of taking a decision by the machine itself (Colson, 2019).?

Literature Research AI in strategic decision-making?

The below presented literature review contemplates three main research streams: AI in strategic decision-making, AI in marketing, as well as risk and trust issues of using AI. Upon review of these papers, it has been criticized that the coexistence of AI and humans is vital to making better strategic decisions (Giuggioli & Pellegrini, 2022; Kaplan, 2021). AI has the ability to?reveal a pattern within a big amount of data more efficiently and accurately than humans would be capable of (Eriksson, Bigi, and Bonera, 2020; Kraus, Feuerriegel & Oztekin, 2020). Furthermore, the paper of Shrestha, Ben-Menahem, and von Krogh (2019) states that merging the benefits of AI and humans optimally results in better organizational decision-making. The paper provides a foundation for understanding how people and algorithms can be effectively combined to make a decision by exploiting the gains to enable better decisions (Shrestha, et al., 2019). To introduce AI-based decisions to an organization, it is mentioned that it becomes relatively effective since a higher level of transparency can be achieved (Shrestha, et al., 2019). Jarrahi (2018) examines the complementarity of humans and AI, as each can have its own strength in organizational decision-making processes. Hence, AI in a strategic context might rather be seen as decision support by engaging customers or employees in the decision-making process (Borges, et al., 2021; Giuggioli & Pellegrini, 2022). Duan, Edwards, and Dwicedi (2019) states that AI is accepted as a decision supporter and can help users make a better decision within its supporter role, such as AI detecting patterns in data sets and interpreting their meaning and correlation (Giuggioli & Pellegrini, 2022).?

AI in Marketing Strategies.?

In recent years the focus of AI in business started to shift from an operation point to strategic- decision making as technology continues to evolve (Davenport, Guha, Grewal, & Bressgott, 2020). With the use of technologies like Machine Learning, Deep Learning, and Natural Language Processing machines are trained to handle big data for the generation of market intelligence (Davenport, et al, 2020). This allows AI to automate business processes, learn insights from past data, and generate consumer and market insights through the program-based algorithm (Davenport, et al 2020; Eriksson, et al, 2020).?

Particularly relevant research is the one of Huang & Rust, 2021, which conceptualizes AI technologies in three areas of intelligence for use in marketing strategies. First, ‘Mechanical AI’ is invented to speed up repetitive jobs and can help automate marketing tasks such as grouping techniques and identifying patterns to group customers and segment them (Huang & Rust, 2021). Second, ‘Thinking AI’ is designed to sort out and analyze data to arrive at new conclusions (Huang & Rust, 2021). Technologies utilized for Thinking AI include machine learning, neural networks, and deep learning (Lida, 2020). Thinking AI, for example, can assist marketing teams to choose the right target market and to make recommendations to marketing managers (Huang & Rust, 2021; see also Lida, 2020). Third, Feeling AI, designed for two-way interactions involving people, and/or for analyzing customer feelings and emotions (Huang &?Rust, 2021; Paschen, Kietzmann & Kietzmann, 2019). These three AI technologies can improve marketing strategies such as the 4Ps of marketing (product, price, promotion, and place), and key strategic decisions segmentation, targeting, and positioning (Huang & Rust, 2021).?

Trust issues with AI?

AI being the core topic of what sits in the middle of the fourth industrial revolution has brought aspects with the topic which tend to be controversial (Schwab, 2017). Humans tend to build trust based on physical appearance, which is difficult given the fact that AI is based on different sorts of algorithms and programs (Cho & Hu, 2009; Duarte, Siegel, & Young, 2012). When looking at trust it is inevitable to see and understand that at the end of the day trust means letting oneself be vulnerable towards another party, just that AI does not take a physical face (Mayer, Davis, & Schoorman, 1995). Throughout the past couple of years, AI has also gathered the reputation of being a human replacement technology (G. F. Davis, 2019). Interestingly, trust tends to build in a steep trend. There tends to be a direct correlation between the more information and know-how the robot or algorithm is fed, the more accurate the decisions will be taken (Glikson, Woolley, & Grahamm, 2020). A study performed by?Gombolay, et al. (2015) turned out that?two groups of individuals doing a project, one being directed by a robot and one by a human, interestingly more trust was given to the robot in scheduling tasks and so-called “logistical” undertakings. Hence the degree of scheduling and or programming needed for a task may correlate with the trust in individuals (Gombolay, et al. 2015).?

3. Research Methodology?

The qualitative research study is based on a multiple case study approach to explore the perceptions of SME about how AI is used to make strategic decisions in marketing (Yin, 2018). A qualitative study has been chosen to collect data and information to develop an explanation and answer for the research question (Halkias & Neubert, 2020). A multiple case study with SME interviews and use cases are analyszed to understand the similarities and differences between each case (Yin, 2018). Furthermore, the evidence and reliability of a multiple-case study can clarify and validate the theoretical perspective (Halkias & Neubert, 2020). Meaning that current SMEs point of view and perceptions will be used to analyze how AI can enhance competitive intelligence for strategic marketing decision making.?

Data was collected through a purposeful sampling with thirteen participants (p) (Merriam & Tisdell, 2016) to collect in depth rich data from different perspectives.?After thirteen SME interviews, data saturation was achieved. The inclusion criteria for the SMEs to the research results are a) having a master's degree or higher and b) having knowledge about the application of AI in decision-making processes.?

Additionally, nine use cases in relation to the topic will be studied to discuss and verify the themes. The nine case studies selected for this study were of companies around the world with the focus of AI in marketing decision making.?

The following use cases are reviewed and considered as practical source of evidence:?

To answer the research questions, analysis of the SME interviews has been completed (Merriam & Tisdell, 2016). In order to point out the themes present in the data collected, a first manual inductive mixed coding cycle was used (Saldana, 2013).??In the second step, a cross-case analysis is done to identify the themes in different case studies and to support the themes with practical evidence (Yin, 2018). The method of data triangulation is used to validate data with different sources of evidence (Stake, 2006). The themes found in the answers of the SMEs are confronted with the above-listed use cases to verify the collected data and themes regarding their transferability into practice and contribution to the conceptual framework.?

In the next chapter, the results and findings of the data are presented.

4.????Results and Findings?

This chapter will answer the main research question as well as the subsequent research question with the below illustrated themes.?

Main research question: How is AI being used to make better strategic marketing decisions??

Theme 1: AI increases efficiency in decision-making?

AI can help increase efficiency in decision-making by either supporting the decision-making process by recommending (p 9) adequate action proposals or scenarios or by directly making the decision itself. Using AI makes the decision-making process faster (p 1; p 5) and unstructured big data can be processed (p?7). Additionally, strategic decisions can be supported by providing alternatives or visualizing options as an output of AI (p?4). Furthermore, AI can save time (p6) and make decisions more efficiently (p?7) due to the fact that a huge amount of data can be processed simultaneously. Hence, it is proven that AI can assist humans (p?5) to make better decisions. In conjunction with complex decisions, “AI is less about replacement of humans but rather complementing human abilities” (p?4). Moreover, the potential of analyzing big data will be a relevant factor for firms to stay competitive (p?7).

Currently, firms are outsourcing AI services to automate their data analysis processes. This allows firms to save time and money by leveraging manual tasks to the AI tools. A case of particular interest comes from the company?Red Balloon, an online marketplace for gifts and experiences.?By implementing AI to their marketing strategy they were able to make their ad campaigns more efficient, saving 25% on marketing spending and improving their results by 30%, (Kaput, 2018). Furthermore thanks to AI the company has been able to find completely new potential customers interested in buying (Kaput, 2018). Naomi Simson, co-founder of Red Balloon explains how her company found “markets in the US and UK of people traveling to Australia that she didn’t even know she had” (Kaput, 2018). Another relevant use case is that of Impossible Foods. Their branding, positioning and market entry strategies have become more efficient thanks to AI and their ability to track social media trends and buzz in “real time” (Sokolowski, 2018). Ashley Geo, PR & Communications Specialist at Impossible Foods adds that by adapting AI to their marketing strategy,??the company has been expanding quickly every month and they are able to “differentiate their content , and to determine what type of launch events would resonate best with each audience” (Sokolowski, 2018).

Theme 2: AI increases the quality of the decision-making process?

AI increases the quality of the decision-making process because a bigger amount of data may be considered (p?1) whereas humans by themselves can only decide based on “a consolidated amount of data” (p?8). Increasing the amount of considered information reduces the risk of making a wrong decision (p?8) as more variables have been evaluated and considered for the decision. The possibility to identify risks by AI helps to make better-qualified and accurate decisions (p?9). AI can unveil an option, which humans might have overseen within the big data (p?5). Yet “the quality of the results depends on the provided data” (p?1). Due to AI, human errors can be significantly decreased (p?8). For example, lots of human workforces are focused on mechanical and repetitive tasks nowadays (p?7), which can be done errorless and more efficiently by AI technology.

All of the cases studied?show an?improvement in the quality of the decision-making process. For instance, PrettyLittleThing, a known fashion and lifestyle brand is using AI to make the customer experience as personal as possible. By using AI they identify potential customers and predict their product preferences. With this approach, they ensure that they are only targeting potential customers with their advertisements. Due to AI, PrettyLittleThing could consequently increase the quality of the customized advertisement (Taylor, 2021). An additional case of interest comes from the company Impossible Foods. The firm has taken advantage of AI to improve their content differentiation and to make strategic decisions about the type of launch events that would resonate best with each target customers” (Sokolowski, 2018). Further, the company Deckers Brands, which owns the footwear brands Teva and Hoka uses AI to improve the product creation and go to market process. Here, AI is collecting data from target customers as input for making product development decisions. AI is helping to find opportunities and determining issues and complications (Taylor, n.d.). Citrix uses AI to support humas by predicting which marketing effort is promising to generate better quality deals. Therefore, they are using data like psychographic and behaviors from customers to get insights (Kaput, 2021).??

Theme 3: User must trust and rely on AI and its decision, despite limitations in its autonomous decision-making?

“The AI system needs to be transparent to understand the results of its analysis” (p?1). For users it is important to understand how the AI comes to a decision to build trust in the algorithm and to reply to the decision made or to the action proposed. Hence, the transparency of the AI system is key for its reliability (p?1). The success of AI in decision-making is dependent on the human’s culture in terms of how much humans rely on and dedicate the decision-making process to AI and the accountability of the decision (p?4). Tendency shows that younger generations are more bias-free and are used to using a computer and relying on its decision (p?2). Nonetheless, lack of trust comes from concerns like data privacy and data security (p?9). Users have “to trust the effectiveness of the algorithm” (p?6) and rely on suggestions or actions (p?7). Participant 1 states that it?“is important to not delegate the decision-making responsibility to the AI system […] because it is far from being perfect and every decision still requires human judgment” (p?1). The algorithm only processes the data in the way it is taught or what it is programmed to do (p?6). Any unforeseen catastrophes like a pandemic might not be included in the analysis. In case ill-prepared data is part of an AI training, the algorithm may have been taught a bug. If the data fed to AI is incomplete or lacks knowledge and experience, the system underperforms (p?7).?

All of the cases analyzed relied on a hybrid system of AI and human marketing teams for their decision making. However, only?one of the reviewed cases?showed issues of trust or limitation?in using?AI.?The company BlueYonder provides software for supply chain management and uses customer data to personalize emails or offers and to predict customer behavior. The founder of BlueYonder mentioned that the decisions often can be influenced by too many variables which are not predictable. Therefore, the remaining uncertainty is limiting the use of AI for decision-making (Tjepkema, 2018). Also, Red Balloon is describing trust issues and limitations of using AI in terms of finding new customers. Simson, co-founder of RedBalloon, says that AI is about trust and people tend to control everything by themselves. On top, Simson mentioned that AI only works, if you spend enough money on the technology. Hence, AI is not a fit for startups to start the business as it requires huge set up cost (Kaput, 2018).???

Subsequent research question: What are the trends and solutions for the use of AI in strategic marketing decision-making according to SMEs??

Theme 4: AI uses big data analysis to predict and recommend?

AI can contribute to making a more accurate decision based on a predicted event or development (p?2),?proven is that big data is used to make a demand analysis. For instance Amazon has been using big data to understand their customers in detail (Li & Zang, 2021). The trend which can be observed is predicting scenarios more accurately by using AI (p?4). In particular, the technology of AI allows companies to better understand customer needs and make brand predictions (p?7).?This was also the case in?an in-depth analysis where social media was analysed in how different factors have been improving with the use of big data and machine learning. Few of those factors are: analysis of real time interaction, target your customers specifically and predict future outcomes (Chaudhary et al, 2021). Participant 1 states that AI can support supply chain management to facilitate just in time deliveries by predicting the demand for production and delivery, this?has also, at least from the emergence of AI, been reflected in the Chinese property market. With the use of big data and AI, Chinese banks and property builders arranged to implement such big data and AI solutions to understand where the market was heading, hence putting a price tag on front street venues higher than actual properties(Li, et al., 2016). Customer behavior and preferences can also be predicted as well as which sales channels customer prefers (p?1). A reliable decision based on certain facts is only possible if a significant amount of data related to this subject have been processed. Therefore, the algorithm demands a huge amount of big data to make trustworthy decisions (p?6).?

The research question with its subsequent research question can be answered by the theme. Better strategic decisions can be made by using AI as the decision process is more efficient and the quality of the decision increases as well. Nonetheless, using AI to support humans in making a decision requires a certain level of trust in the technology. The trend shows that big data is analyzed by AI to make predictions.??

In the next chapter, the above illustrated results are discussed

5.????Discussion

By triangulating the data of this exploratory research the following themes could be identified: efficiency, quality, trust issue, and big data analysis. These themes are reflected in the perception of the SMEs with regards to the use of AI in decision-making processes. Furthermore, these perceptions can be transferred to the marketing related cases. Lastly, there is a correlation between the SMEs perception, the use cases, and the perspective of the authors from the literature review. The SMEs and the cases reflect an improvement in the efficiency and quality of the decision-making process by using AI. This was also explored by Huang, et al.?(2021) where AI can assist marketing teams in choosing the right market to enter and the correct strategy for a marketing campaign to succeed. Moreover, the SMEs mentioned that AI can?uncover?an option which might have been overseen by humans.?Based on Glikson (2020), trust in AI technology is directly correlated with the amount of information fed to the algorithm. According to SME’s statements users will trust the effectiveness of an algorithm given they have the transparency of the decision generated by the data inserted. While using?machine?learning,?deep learning or?natural?language?processing algorithms are trained to handle big data which delivers market intelligence (Davenport, et al. 2020), which in turn connects to the SMEs statements that behaviors and tendencies of customers can be predicted.??

Finally, SME’s responses and use cases?contributed to the illustrated views from the literature review.?For humans it is important to consider a big amount of data to reflect and evaluate several different options and opportunities. Here, AI is supporting humans with data mining to make a more profound and data-based strategic decision. Additionally, a trend can be observed towards using big data to predict the situation with better accuracy. The paper of Shrestha, et al. (2019) suggests that merging the benefits of AI and?humans?result in a better organizational decision-making process. Yet, the paper is not?illustrating how?both benefits can be merged optimally. Likewise, the Colson model is ending with the possible action proposal but is not making use of AI to make predictions about the future scenario or developments in case this decision will be taken by the human. Furthermore the Colson?model lacks a loop of machine learning and processing new data.?One trend for the future use of AI is suggested by the theme that AI can contribute to predicting future events. This has been identified as an important factor for decision-makers in the SME interviews and in the use cases.?As a result, the model in Figure 2 is developed as a new version of the conceptual framework to consider two loops of data processing: first, fully automated action proposal by the machine and second, where humans make the final decision assisted by AI to augment or support the decision.?

It seems essential that a marketer making a strategic decision, which?leads to?consequences on the business, would like to get a better understanding of the risk and would like to get more information on how this decision might influence the future of the firm. This leads to an enhancement of Colson’s model, to contribute?in?solving this limitation. Here, AI can evaluate the potential decision and predict a potential scenario.?

As shown in figure 2, Colson’s model, presented as a conceptual framework in chapter two, gets enhanced after the human judgment of the potential action. The business decision will not be taken right after the human judgment. This enhanced model makes another loop by taking the potential decision back to the AI system. The machine then will evaluate the potential decision by also considering the additional data from the human and illustrate the possible scenario?that?will happen if this decision is taken. Furthermore, the?machine will learn and adapt the algorithm by the data coming (Lebovitz, et al., 2021)?from the possible action and potential scenario.?By illustrating the outcome of the potential scenario, the decision-maker can get an even better vision of its decision effect. This loop can contribute to reducing the bias of using AI for strategic decisions because the user gets an impression of the future scenario when taking the potential decision. Therefore, with the illustrated enhancement, the decision-maker can gain more confidence in its decision. Still, the human judgment of the machine output is indispensable for making better business decisions. Furthermore, the algorithm will learn from the final business decision to increase the database for future decision-making processes.?

The conclusion of this paper will be presented in the next chapter.?

6.????Conclusion

The purpose of this qualitative research is to explore how AI is used in strategic marketing decision-making processes to make better decisions. A finding of the research is that AI is used to make decisions more efficiently in terms of reducing the time or the cost for the decision-making process. Furthermore, AI facilitates humans to make use of the available big data to consider their decision as the system can process a high amount of data. Additionally, a complex pattern can be easily and accurately identified by using AI, which increases the quality of the decision and can contribute to reducing wrong decisions. A further finding of the research is that humans must trust and rely on the AI system to implement the technology in the decision-making process. By processing a lot of different data and identifying patterns, the machine can propose possible actions to the human. The human will subsequently judge the proposed action with its own, non-digital information like experience and subjective opinion to find a potential decision. This research suggests that such potential decisions can then be further evaluated by AI and the machine predicts some possible and future scenarios based on the desired decision. Thus, decision-makers can be more confident in their final decision because AI can contribute to predicting the future outcome of their decision.?

The sampling strategy and the sampling size are defined to explore the phenomenon. However, the research is limited in generalization due to the lack of a representative sample. To generalize this paper’s results, there is a need to examine the topic also with a quantitative research methodology in order to verify the identified themes. Finally, there is a demand for further research to verify and confirm the developed enhancement of Colson’s model for supporting humans to make better strategic decisions using AI.?

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