An Evaluation of Customer Purchase Prediction Models in FMCG Companies: A Case Study of Consumer Goods Firms in Katsina State
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- NGN 5000
Background of the Study
In the fast-moving consumer goods (FMCG) industry, predicting customer purchase behavior is crucial for developing effective marketing strategies and managing supply chains. By leveraging data analytics and machine learning models, FMCG companies can forecast consumer demand, optimize inventory levels, and tailor marketing campaigns (Elumelu & Akinyemi, 2024). In Katsina State, which is home to a growing number of FMCG firms, there is a need to understand how these companies can apply predictive models to enhance their sales strategies.
Customer purchase prediction models help FMCG companies anticipate buying patterns based on past purchasing behavior, demographic data, and external factors such as economic conditions or seasonal trends. Despite the potential benefits, many firms in Katsina State have yet to fully adopt predictive models due to limited resources, lack of expertise, and inadequate data infrastructure. This study aims to evaluate the extent to which customer purchase prediction models are utilized by FMCG companies in Katsina State and assess their effectiveness in driving sales growth.
Statement of the Problem
FMCG companies in Katsina State face challenges in accurately predicting customer purchasing behavior, which affects their ability to manage inventory, forecast demand, and optimize sales strategies. Although many of these firms rely on basic historical data to predict customer purchases, they often lack the advanced predictive models that can offer deeper insights into consumer trends. As a result, businesses may experience stockouts or overstocking, leading to lost sales or increased costs. This study aims to evaluate the adoption and effectiveness of customer purchase prediction models in FMCG companies in Katsina State.
Objectives of the Study
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To assess the use of customer purchase prediction models in FMCG companies in Katsina State.
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To evaluate the impact of customer purchase prediction models on sales and inventory management in these firms.
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To identify the challenges FMCG companies in Katsina State face in implementing customer purchase prediction models.
Research Questions
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To what extent do FMCG companies in Katsina State use customer purchase prediction models?
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How do customer purchase prediction models impact sales and inventory management in FMCG companies in Katsina State?
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What challenges do FMCG companies in Katsina State face in adopting customer purchase prediction models?
Research Hypotheses
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FMCG companies in Katsina State have not significantly adopted customer purchase prediction models.
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Customer purchase prediction models do not significantly impact sales and inventory management in FMCG companies in Katsina State.
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Challenges significantly hinder the adoption of customer purchase prediction models by FMCG companies in Katsina State.
Scope and Limitations of the Study
The study will focus on FMCG companies in Katsina State that either use or plan to use customer purchase prediction models. The study's limitations include access to proprietary data on consumer purchasing behavior and the potential reluctance of businesses to share such information.
Definitions of Terms
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Customer Purchase Prediction Models: Statistical or machine learning models used to predict future customer purchases based on historical behavior and other factors.
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FMCG Companies: Companies involved in the production and distribution of fast-moving consumer goods, such as food, beverages, and personal care products.
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Sales and Inventory Management: The process of overseeing the sales of goods and managing stock levels to meet demand while minimizing costs.
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