Impact of Artificial Intelligence on Market Segmentation

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.

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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