La regresión lineal en el proceso de ventas y su influencia en la competitividad organizacional en entornos Big Data

In the competitive fashion retail sector, sales optimization through the use of Big Data has become an essential element for sustaining competitiveness. This study applied linear regression models to analyze the influence of key variables on the performance of clothing stores, using simulated data processed in Apache Spark. Through a backward elimination process, the initial model was refined, and it was identified that conversion rate and price satisfaction are positively correlated with competitiveness, while competitive pricing shows a negative relationship. These findings reveal that beyond price reduction, factors related to the conversion of interest into purchase and the perception of satisfaction are decisive in shaping the value proposition of organizations. Consequently, linear regression emerges as a valuable analytical tool to guide strategic decisions that strengthen competitiveness in fashion retail within a Big Data–driven environment.