PREDICTIVE ANALYTICS MODELS FOR SMES TO FORECAST MARKET TRENDS, CUSTOMER BEHAVIOR, AND POTENTIAL BUSINESS RISKS
DOI:
https://doi.org/10.60087/jklst.v3.n3.p355-381Abstract
Abstract
Introduction: Small and Medium-sized Enterprises (SMEs) face numerous challenges in today's rapidly evolving business landscape. Predictive analytics models offer a promising solution for SMEs to gain insights into market trends, customer behavior, and potential risks. These models apply analytical techniques for predicting future performances to help SMEs make the right decisions and survive successfully in their industries. Predictive analytics has received quite a lot of consideration from big organizations but is not much common among SMEs because of certain challenges that hinder its implementation therein.
Materials and Methods: The research methodology employed in review involves a comprehensive analysis of existing literature on predictive analytics models for SMEs. A systematic review of peer-reviewed articles, industry reports, and case studies was conducted to gather relevant information. The review focuses on three key areas: market trend predication, customer behavior predication and even business risk analysis. Besides, the paper aims at identifying the obstacles that SMEs experienced in the early adoption of predictive analytics models and whether it is possible to find a way around those challenges.
Results: The study demonstrates that advances in predictive analytics models have the potential to significantly improve decision-makers’ decision-making and SMEs’ performance. Forecasting models specific to the market trends help the SMEs to plan in advance, on any change in the consumer behavior and the market trends that exist in the business environment. Customer behaviour forecasting models aid the SMEs in delivering targeted products to clients and increasing customer loyalty. Risk assessment models help SMEs to determine and manage risks that can threaten their functioning. Nonetheless the take-up of predictive analytics in SMEs is still low as compared to large organizations, which are mainly attributed to resource constraints including a lack of knowledge about the capabilities of such systems.
Discussion: The review highlights the potential benefits of predictive analytics models for SMEs, including improved operational efficiency, enhanced customer satisfaction, and increased competitiveness. However, there are challenges that limit the widespread use of BI some of which include; Data quality problems, lack of monetary capital, and skills. This discussion also outlines multiple ways to solve such issues: creating easy-to-use analytics tools, engaging with universities, and launching governmental programs that would help SMEs transition to digital business.
Conclusion: Predictive analytics models offer significant opportunities for SMEs to enhance their decision-making capabilities and drive business growth. Despite the barriers which have been presented there are advantages that will lead to achievement of the needed outcomes of implementing the System. This is particularly so as technology remains a ubiquitous tool that bends with the strengths of SMEs that can harness predictive analytics in an ever-increasingly commoditized business world. Further work should be devoted to identification of new efficient, affordable and easily implementable solutions facilitating the SMEs growth in the variety of sectors.
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