In the fast-paced world of e-commerce, product recommendations are key to driving engagement and sales. But while we know the impact of individual recommendation types—such as suggesting substitutes or complementary products—their combined effect remains a mystery. Our research explores how mixing different types of recommendations influences consumer behavior at various stages of the purchase journey. Drawing on insights from consumer psychology and decision-making theories, we will test three hypotheses through a large-scale online field experiment. Our findings will help e-commerce businesses refine their recommendation strategies to maximize both product views and conversions. For marketing professionals, this analysis will produce practical, data-driven insights on how to finetune recommendation engines for greater impact, allowing them to stay ahead of the competition by understanding how to guide customers more effectively from browsing to checkout.