An Assessment of Dynamic Pricing Algorithms in Northern Nigeria's Retail Industry: A Case Study of Sahad Stores, Abuja, FCT
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1.1 Background of the Study
Dynamic pricing is an AI-powered pricing strategy that adjusts prices of goods or services in real-time based on market demand, competition, customer preferences, and other external factors (Sanni et al., 2024). In the retail industry, dynamic pricing algorithms enable retailers to maximize profits by charging different prices for the same product based on the time, customer, or market conditions. This is particularly important in competitive markets like Northern Nigeria, where retailers like Sahad Stores in Abuja face challenges related to pricing strategy, customer demand fluctuations, and market competition.
Dynamic pricing relies on advanced AI techniques such as machine learning, which helps to forecast demand patterns and set optimal prices for products (Ogundele & Adeyemo, 2025). Sahad Stores, a leading retail chain in Abuja, has incorporated dynamic pricing algorithms to adjust the prices of products in response to real-time data, aiming to improve profit margins and stay competitive. This study investigates the use and impact of dynamic pricing algorithms in Sahad Stores, assessing how the technology improves pricing strategies and enhances revenue generation in the context of Northern Nigeria’s retail industry.
1.2 Statement of the Problem
While dynamic pricing has been widely adopted in global retail markets, there is limited research on the application and effectiveness of dynamic pricing algorithms in Northern Nigeria’s retail sector. Sahad Stores in Abuja has implemented dynamic pricing but lacks comprehensive studies on how well these algorithms work in the Nigerian context. The problem is the absence of empirical evidence on the efficiency of dynamic pricing algorithms in enhancing revenue, customer satisfaction, and market competitiveness for Nigerian retailers.
1.3 Objectives of the Study
1. To evaluate the effectiveness of dynamic pricing algorithms at Sahad Stores in Abuja, FCT.
2. To assess the impact of dynamic pricing on revenue generation and customer satisfaction in the retail sector.
3. To identify challenges and barriers to the successful implementation of dynamic pricing algorithms in Northern Nigeria’s retail industry.
1.4 Research Questions
1. How effective are dynamic pricing algorithms in optimizing pricing strategies at Sahad Stores in Abuja?
2. What impact do dynamic pricing algorithms have on revenue generation and customer satisfaction at Sahad Stores?
3. What are the challenges faced by Sahad Stores in implementing dynamic pricing algorithms, and how can these challenges be addressed?
1.5 Research Hypothesis
1. Dynamic pricing algorithms significantly improve pricing strategies and revenue generation at Sahad Stores in Abuja.
2. The implementation of dynamic pricing algorithms leads to higher customer satisfaction and retention at Sahad Stores.
3. Sahad Stores faces challenges such as data privacy concerns, technical expertise limitations, and consumer resistance to dynamic pricing algorithms.
1.6 Significance of the Study
This study is significant because it sheds light on the role of dynamic pricing algorithms in improving pricing strategies, profitability, and customer satisfaction in the Nigerian retail industry. By focusing on Sahad Stores, the study provides valuable insights into the practical application of dynamic pricing in a developing economy. The findings will inform retailers in Northern Nigeria and beyond about the potential benefits and challenges of adopting AI-powered pricing strategies.
1.7 Scope and Limitations of the Study
This study focuses on the use of dynamic pricing algorithms at Sahad Stores in Abuja, FCT. It does not extend to other retail businesses or consider other AI applications in pricing strategies. Limitations include data privacy concerns, difficulty in obtaining proprietary information from the company, and the specific contextual focus on Northern Nigeria, which may limit the generalizability of the findings.
1.8 Operational Definition of Terms
1. Dynamic Pricing: A pricing strategy that adjusts product prices in real-time based on market conditions and demand.
2. Artificial Intelligence (AI): The use of machine learning and data-driven algorithms to automate decision-making processes.
3. Revenue Generation: The process by which a company earns income from the sale of goods or services.
4. Machine Learning Algorithms: Computational models that enable systems to learn from data and improve their performance over time.
5. Customer Satisfaction: A measure of how well a company’s products and services meet or exceed customer expectations.
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