LEVERAGING PREDICTIVE ANALYTICS TO OPTIMIZE SME MARKETING STRATEGIES IN THE US

Abideen Mayowa Abdul-Yekeen, Moshood Abiola Kolawole, Bunmi Iyanda, Hassan Ayomide Abdul-Yekeen
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Abstract

In the modern business landscape, data and analytics are playing an increasingly pivotal role in decision making across all sectors. Small and medium sized enterprises (SMEs) constitute a major portion of the economic fabric in the United States, yet many struggle with limited resources and an inability to leverage insights from customer and market data at their disposal. This study seeks to explore how SMEs operating in various industries across the US can optimize their marketing strategies through the application of predictive analytics techniques. By focusing on identifying patterns and trends in structured and unstructured data related to areas such as customer behavior, competitors, and industry shifts, SMEs stand to gain actionable recommendations for improving key metrics like sales, customer retention, and profitability. The findings of this research have implications for SME leadership teams seeking data-driven approaches to gain competitive advantage in a digital era defined by information abundance. 
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利用预测分析技术优化美国中小企业营销战略
在现代商业环境中,数据和分析在各行各业的决策中发挥着越来越关键的作用。中小型企业(SMEs)在美国经济结构中占有重要地位,但许多中小型企业却苦于资源有限,无法从所掌握的客户和市场数据中获得洞察力。本研究旨在探讨美国各行各业的中小企业如何通过应用预测分析技术来优化其营销战略。通过重点识别与客户行为、竞争对手和行业变化等领域相关的结构化和非结构化数据中的模式和趋势,中小企业可以获得改善销售额、客户保留率和盈利能力等关键指标的可行建议。这项研究的发现对中小企业领导团队在信息丰富的数字化时代寻求数据驱动方法以获得竞争优势具有重要意义。
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