Gaining customer insights in big data for SMEs market segmentation decisions in emerging markets - Oluwayomi Omotayo Olota; Ebenezer Oluwadamilare Balogun; Opeyemi Emmanuel Babawale (Economic Forum, 2025)
Small and medium enterprises around the world, and especially in emerging markets, face challenges when it comes to market segmentation. They have limited knowledge of the importance of big data customer insights for making concrete market segmentation decisions. The purpose of this study was to assess gaining customer insights in big data for market segmentation decisions of SMEs in emerging markets. The results of the study indicated that customer behaviour analysis strongly affects market segmentation decisions of among small and medium scale enterprises. A beta-value of 0.344 was shown to indicate that a unit change in customer behaviour analysis will lead to a unit change in market segmentation decisions among small and medium scale enterprises with the t-statistics of 4.608 and p-value of 0.001. It was specified that customer preference analysis strongly affected market segmentation decisions of among small and medium scale enterprises. The results showed that the beta-value was 0.379, indicating that a unit change in customer preference analysis will lead to a unit change in market segmentation decisions of among small and medium scale enterprises with the t-statistics of 6.654 and p-value of 0.001. It was revealed that customer feedback analysis strongly affected market segmentation decisions of among small and medium scale enterprises, the results showed that the beta-value was 0.215 also indicated that a unit change in customer feedback analysis will lead to a unit change in market segmentation decisions of among small and medium scale enterprises with the t-statistics of 3.155 and p-value of 0.002. It was concluded that gaining customer insight in big data was essential for small and medium enterprises in emerging economy to make effective market segmentation decisions.
Keywords: 330; complex data; customer behaviour analysis; customer feedback analysis; customer perspective; customer preference analysis