LEVERAGING PREDICTIVE ANALYTICS TO OPTIMIZE SME MARKETING STRATEGIES IN THE US
DOI:
https://doi.org/10.60087/jklst.vol3.n3.p73-102Keywords:
Predictive analytics, customer retention, personalization, data-driven, customer acquisition, lead scoring, churn prediction, customer lifetime value, multivariate testing, next best action, engagement, loyalty programs, proactive customer service, omnichannel support, product development, feature prioritization, predictive maintenance, concept testing, Machine LearningAbstract
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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