The Impact of Artificial Intelligence Analytics on Customer Segmentation: A Case Study of Nigerian Breweries, Kaduna State
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1.1 Background of the Study
Customer segmentation, a cornerstone of effective marketing strategy, has been revolutionized by the integration of Artificial Intelligence (AI). Traditional segmentation methods often relied on demographic and geographic data; however, AI analytics enables a more dynamic, data-driven approach. By leveraging machine learning algorithms and big data, businesses can analyze behavioral, psychographic, and transactional data to create precise customer segments.
Nigerian Breweries in Kaduna State operates in a highly competitive industry where understanding customer preferences and behavior is crucial. With AI tools, the company can identify distinct customer groups, predict purchasing patterns, and design targeted campaigns to maximize engagement and loyalty (Adeleke & Usman, 2024). This study investigates how Nigerian Breweries employs AI analytics for customer segmentation and assesses its impact on marketing efficiency and revenue generation.
1.2 Statement of the Problem
Despite the availability of AI tools, many businesses, including Nigerian Breweries, face challenges in leveraging these technologies for customer segmentation. Key issues include integrating AI with existing systems, interpreting AI-generated insights, and measuring the effectiveness of AI-driven segmentation on marketing outcomes.
1.3 Objectives of the Study
- To analyze the adoption of AI analytics for customer segmentation by Nigerian Breweries.
- To evaluate the impact of AI-driven segmentation on marketing efficiency and customer satisfaction.
- To identify barriers to the effective use of AI analytics in customer segmentation.
1.4 Research Questions
- How does Nigerian Breweries employ AI analytics for customer segmentation?
- What is the impact of AI-driven segmentation on marketing efficiency and customer satisfaction?
- What are the barriers to adopting AI analytics in customer segmentation?
1.5 Research Hypothesis
- AI analytics significantly improves the accuracy of customer segmentation.
- AI-driven segmentation enhances marketing efficiency and customer satisfaction.
- Lack of integration and expertise are major barriers to AI adoption in segmentation.
1.6 Significance of the Study
This study provides insights into the transformative role of AI analytics in customer segmentation, offering practical implications for businesses seeking to optimize marketing strategies.
1.7 Scope and Limitations of the Study
The study focuses on Nigerian Breweries in Kaduna State and its use of AI for customer segmentation. Limitations include potential resistance to sharing proprietary data and the evolving capabilities of AI technologies.
1.8 Operational Definition of Terms
- Customer Segmentation: The process of dividing customers into distinct groups based on shared characteristics.
- AI Analytics: The application of machine learning and data analysis techniques to extract actionable insights.
- Behavioral Data: Information about customers’ purchasing habits and online behavior.
- Psychographic Data: Insights into customers’ values, attitudes, and lifestyles.
- Marketing Efficiency: The ability to achieve marketing objectives with minimal resource expenditure.
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