Research on Improving the Conversion Rate of Marketing Strategies Optimized by Big Data and Machine Learning

This study advocates a strategy to improve marketing tactics based on the random forest algorithm, with the goal of using massive data analysis to enhance the effectiveness of marketing initiatives. A huge data analysis framework covering multiple data inputs was conceived. This framework covered customer behavior indicators, consumption patterns, product tendencies and other dimensions. The random forest algorithm in the field of machine learning is used to perform classification and regression on the data to build a model, aiming to identify hidden consumer groups and estimate their conversion probability. Subsequently, this study fine-tuned the configuration of the random forest, involving parameters such as the number of decision trees, maximum hierarchical depth, and minimum sample division, to improve the prediction accuracy of the model. In the simulation experiment session, actual marketing data sets were used to implement model training and evaluation. The simulation results revealed that the marketing strategy optimization model using the random forest algorithm showed significant superiority in predicting conversion rates, with a conversion rate increase of 15%. Compared with traditional marketing techniques, this system can more accurately locate potential consumer groups, enhance the accuracy of marketing activities, and thus improving overall marketing effectiveness. This exploration provides an innovative perspective for marketing strategy innovation based on big data and machine learning, and provides technical support for enterprises to enhance market competitiveness.