Introduction
Artificial Intelligence (AI) influences marketing mechanisms and procedures by providing insight into consumer behavior and preferences. AI is a system’s capacity to analyze data correctly, learn from the analysis, and use it to achieve specific goals. This technology uses machine-learning algorithms to customize content and products to satisfy target markets and influences consumer choices by offering suggestions based on social perceptions (Haenlein & Kaplan, 2019). This technological tool can help organizations maximize their competitive advantage and market dominance. Nevertheless, the involvement of AI comes along with doubt and disbelief due to different biases. Therefore, organizations must isolate and neutralize biases to realize the benefits of AI in inducing consumer preference.
History of Artificial Intelligence
Experts have exerted themselves constantly to improve technology throughout the years. The inception of AI occurred in the 1940s when Isaac Asimov, an American Science Fiction writer, developed three laws of robotics through his short story ‘Runaround’ (Haenlein & Kaplan, 2019). The first law prevents a robot from harming a human being or allowing them to experience harm. Secondly, a robot must comply with every order except when they contradict the first law. Lastly, a robot must ensure its survival without jeopardizing the first and second laws (Haenlein & Kaplan, 2019). Asimov’s story inspired many scientists to venture into the field of AI, computer science, and robotics.
At approximately the same period, Alan Turing, an English mathematician, explored the mathematical possibility of AI. Turing argued that if humans make decisions and tackle problems using available information and reason, machines could do the same thing (Anyoha, 2017). As a result, Turing developed ‘The Bombe,’ a code-breaking machine, to decipher the Enigma code that the Germans used during World War II. The success of this development incited Turing to publish the article ‘Computing Machinery and Intelligence’ that explained how to create and test intelligent machines (Haenlein & Kaplan, 2019). This article suggests that a machine qualifies as intelligent when a human cannot distinguish it from another human. Turing’s test currently serves as a benchmark to identify an artificial system’s intelligence.
Despite Turing’s strong desire to explore the field of AI, he faced significant challenges that slowed down his pursuit of advanced technology. Turing had to prove his AI concept and seek support from high-profile people to convince funding sources to invest in machine intelligence. Fortunately, advancements in technology over the years facilitated the improvement of AI as computers became more efficient, more accessible, and cheaper (Anyoha, 2017). The knowledge about machine learning algorithms also increased, and people understood better which algorithms to apply to specific problems. Although algorithms may not improve much, the increased availability of information allows AI to learn forcefully. The marketing industry provides a clear insight into the effects and impact of AI.
Impact of Artificial Intelligence on Market Segmentation
The application of technology in business facilitates improvements in processes and encourages innovation. Many organizations have integrated AI into marketing, guaranteeing them a competitive advantage. Studies infer that AI facilitates decision-making based on consumer behavior, future business processes, and market trends (Nanayakkara, 2020). However, marketers must systematically allocate the available resources to minimize costs and increase investment returns by dividing customers into different categories according to their preferences and behavior (Eslamijam, 2020). Market segmentation can help minimize waste in campaigns and facilitate other marketing tasks, such as pricing and product recommendations.
Previously, market segmentation was a tedious and time-consuming exercise that required manual analysis of customer data to determine ways of grouping customers in distinct categories. However, the development of AI algorithms has made this process much more manageable. Machine learning prototypes can analyze customer data and identify recurring patterns in consumer behavior. AI algorithms can assist market analysts in determining customer segments that would be challenging to identify using intuition and manual data examination (Eslamijam, 2020). Successful market segmentation requires a combination of human intuition and AI.
Marketers can now acquire huge amounts of information about consumers’ buying behavior, consumption patterns, product preferences, and buying cycle. AI-powered tools can transform this data into useful information, providing marketers with the capacity to collect and analyze consumer information to develop actionable marketing strategies (Nanayakkara, 2020). For instance, the K-means clustering algorithm is convenient for market segmentation. This tool is an unsupervised machine learning algorithm that arranges data into similar clusters based on characteristics, such as customer’s age, expenditure, income, and many more (Eslamijam, 2020). Although the K-means clustering algorithm is fast and efficient, the marketer must define the relevant features of their marketing campaign to realize positive outcomes. Machine learning may not replace human intuition and judgment but can augment human efforts to higher levels.
Artificial Intelligence and Consumers
AI enables organizations to assist customers in substantial ways. Some benefits that customers may enjoy include wearable devices to monitor health, assistance with AI-powered household appliances, and convenient virtual assistance (Puntoni et al., 2020). However, deploying AI may subject consumers to social and individual challenges. Consumers’ experience from AI depends on its capabilities – listening, forecasting, producing, and communicating. The consumer-AI experience refers to the interactions between the customer and the organization during their journey to purchasing a product or service and involves emotional, social, behavioral, cognitive, and sensorial dimensions (Puntoni et al., 2020). Multiple sources have identified four distinct consumer-AI experiences: data capture, classification, delegation, and social experiences (Puntoni et al., 2020). Without a doubt, each experience has benefits and drawbacks to consumers.
The data capture experience arises from the multiple ways consumers transfer data to the AI. Consumers can provide data intentionally despite their understanding of the process. AI also obtains information from digital footprints that consumers leave behind during their daily activities (Puntoni et al., 2020). This experience benefits consumers by making them appreciate that AI maximizes their interests. On the other hand, consumers may develop a negative perception that the AI is exploiting their data, mainly due to their limited understanding of the AI’s operating criteria (Puntoni et al., 2020). Organizations leverage AI’s predicting capacity to provide customized suggestions and maximize relevance, engagement, and satisfaction. AI classification involves analyzing various information, including preferences of past and present consumers (Puntoni et al., 2020). This algorithm may provide consumers with relevant suggestions, resulting in a satisfying experience. However, consumers with limited knowledge concerning algorithmic mechanisms may perceive these recommendations as defining the type of person they are due to the natural tendency for categorical thinking (Puntoni et al., 2020). Therefore, there is an urgent need to create more awareness of how machine learning works.
Consumers also derive a social experience when relating to AI. Beneficial social experiences arise when consumers can identify a platform for exchanging information that links them to the organization naturally. Some benefits include increased efficiency and a highly fascinating interaction. However, the AI’s large data storage capacity increases the likelihood that various types of AI may outsmart consumers and intervene in their decisions (Pelau et al., 2021). The continuous use of AI also presents a high risk of consumer manipulation and overdependence on intelligent technologies. Consequently, the consumer-AI interaction could decrease the consumers’ cognitive abilities and affect their personality, thinking, and social relationships (Pelau et al., 2021). The social circle dictates the relationship between fascination and efficiency and the sensitivity of conserving human abilities.
Regardless of the benefits that AI can give consumers, this technological tool would only flourish if consumers accepted to use it. Consumers’ willingness to use AI may vary due to several factors. Nagy and Hajdu (2021) identified trust and perceived usefulness as the main factors that determine consumers’ acceptance of the use of AI. Some consumers do not trust AI since they perceive it as a financial threat, thinking that it might expose sensitive information or manipulate it to harm them. In some instances, consumers may view AI as a useless initiative that prevents them from attaining the support they need by minimizing and deflecting customer complaints (Tiwari, 2020). Marketers must act urgently to increase consumers’ acceptance of the use of AI. They must assure customers that AI is not a barrier to receiving wholesome customer service but a gateway to a seamless end-to-end experience.
Several best practices exist that marketers could use to maximize consumer acceptance of using AI. Consumers’ confidence in AI-powered platforms positively affects their willingness to use these provisions. A consumer who trusts a particular online shop is highly likely to go through the purchasing process (Nagy & Hajdu, 2021). Marketers can build customers’ trust in AI by providing informative content that educates them about how the organization is applying AI to support their customers goals (Nagy & Hajdu, 2021). Organizations can use AI to provide customer support in numerous ways. For instance, AI can provide valuable information that collections agencies can use to negotiate ideal payment plans for customers relative to their unique financial situations (Tiwari, 2020). Marketers should clarify why AI is the ultimate solution for achieving their customers’ specific goals.
Many people view AI in business as the automation of recurring tasks. As much AI helps organizations manage numerous tasks efficiently, this technology has evolved past mere automation. Marketers must correct this viewpoint by emphasizing sophistication rather than automation (Tiwari, 2020). They must show customers that AI exists to deepen customer-brand relationships without eliminating the personal touch. For instance, improvements in AI technologies, such as deep neural networks (DNN) and natural language processing (NLP), enable organizations to provide more individualized customer support using data-driven discernments (Tiwari, 2020). The level of sophistication would determine whether consumers perceive AI as a productive tool or a barrier.
Artificial Intelligence and Consumer Bias
The large-scale digitization of data and the use of AI is disrupting major economic sectors, such as advertising, retail, transportation, and energy. AI also affects government functions as they rely on automated systems to improve accuracy and maximize objectivity in decision-making (Lee et at., 2019). Since AI can treat similar people and objects differently, some algorithms could replicate and amplify consumer biases. For instance, U.S. judges use computerized risk assessments to determine sentencing durations and bail. However, these automated assessments could generate inaccurate conclusions, resulting in unfair judgments to a particular group of people, such as extended prison sentences or unreasonably high bails (Lee et al., 2019). Algorithmic bias mostly occurs due to incomplete or flawed training data replicating historical inequalities.
Examples of bias in AI exist where organizations intentionally programmed their computer systems to discriminate against a particular group of people. These occurrences incited the Federal Trade Commission to issue a warning against the use of biased algorithms due to their potential to violate consumer protection laws (Landi, 2021). Therefore, organizations can cause consumer bias by developing discriminatory algorithms intentionally to disregard the interests of a particular group of people. In addition, incomplete training data also results in consumer bias. For instance, feeding AIs with information from news articles could result in bias against women, while training AIs on law enforcement data could cause bias against people of color.
One remedy to algorithmic bias is to improve AI-powered systems. Operators and relevant stakeholders must exercise diligence in addressing factors that encourage bias (Lee et al., 2019). A proactive response to algorithmic bias can help avoid harmful effects on users. Governments could also intervene by formulating public policies that encourage the ethical and unbiased use of machine learning technologies (Lee et al., 2019). For instance, governments could update nondiscrimination laws to govern digital practices. AI developers must consider consumer-focused strategies to ensure the technology addresses the interests of all social groups.
Influence of AI on Marketing Strategy Formation
AI capacities are altering the way organizations formulate and manage marketing strategies. Machine learning technologies help marketers provide personalized experiences to the constantly growing number of consumers. Rather than depending on intuitive heuristics, marketers use AI to identify consumer preferences, personalize value, and align messaging (Talbot, 2019). Likewise, marketers are increasingly using data and AI to inform and evaluate strategies. Algorithms ensure a marketing strategy is consumer-oriented rather than product-oriented. Instead of focusing on how the organization could reach its customers, AI learns how the customers reach the organization and then provides an enabling environment (Talbot, 2019). Eventually, this approach would provide clearer insight into consumers’ behaviors and preferences.
The optimization of marketing strategies relies on segmentation. AI can create distinct market segments to help marketers reach target consumers easily. Machine learning technologies are also resourceful in providing ideas for developing new products (Talbot, 2019). AI aids marketers in determining a significant role in product development. Deep listening allows AI to provide content that builds and sustains strong relationships. Through proper training, AI can accurately evaluate Customer Lifetime Value (CLV) and help marketers determine how to invest in each customer (Talbot, 2019). Lastly, AI enables marketers to test and certify assumptions about consumer behavior. This technology achieves this role by providing the possible causes of the current strategies’ success or failure. Besides formulating marketing strategies, AI influences market analysis and research processes in various ways.
Influence of AI on Market Research and Analysis
Market research and analysis involve obtaining information concerning consumers’ needs and preferences and evaluating it based on specific criteria. AI influences these marketing processes in various ways. Open-end coding is among the market research processes challenging to market researchers. This exercise encompasses reading numerous open-ended texts and allocating the most relevant code (Vlacic et al., 2021). However, data scientists have developed algorithms to facilitate text analysis. For instance, Google Cloud’s Natural Language API has attained the capacity to understand the syntactic structure, category of text, and text sentiment (Gell, 2019). Market researchers have incorporated automated sentiment analysis into online surveys to calculate the positivity and negativity of responses (Vlacic et al., 2021). As a result, data interpretation has become an easier and more time-efficient exercise for market research analysts.
AI also enables market researchers to extract new insights from random text data, facilitating complex decision-making processes. For instance, IBM’s Watson can assess social media posts from multiple users and identify complex characters or themes (Gell, 2019). Market researchers managing online panels also benefit from AI. Online panels comprise participants willing to engage in various market research studies suppose they qualify. However, most members are likely to disengage themselves if they do not qualify for a survey despite their active participation in pre-screening surveys. With the help of AI, panels can monitor the members’ behavior and predict when one is likely to lose interest in participation (Gell, 2019). Consequently, online market researchers can avoid member turnover and maximize the quality of research participants.
Conclusion
AI is a technological advancement that has revolutionized business and organizational processes worldwide. Marketing processes are not exceptional to this change. AI facilitates multiple marketing processes, including market segmentation, marketing strategy formation, and market research and analysis. As a result, this technology can maximize the organization’s competitive advantage and market share. However, marketers must acknowledge its potential and apply it appropriately. Misuse of AI could result in the violation of consumer protection laws. Therefore, business organizations and government institutes should embrace the potential of AI and use it to provide a better customer experience.
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