Improving engineering design in the context of market systems requires a deep understanding of the decision-making processes of multiple interacting stakeholders and how they affect the success of new products. One key group of stakeholders in this system is consumers, who make purchase choices that directly influence each product’s market share and profits. Since real-world individual decisions are influenced by social communications, supporting product development efforts with social network analysis can enable producers to predict demand much more accurately.This article presents an agent-based modeling (ABM) framework for design for market systems analysis that incorporates social network word-of-mouth (WOM) recommendations. To investigate influences of homophily-driven WOM and network structures on consumer preferences and the prediction of market demand, the random and small-world networks are generated based on the concept of homophily to study the differences in the emergent system-level behaviors. We compare the output of the models against a similar model that excludes WOM influences, using a case study of the top-selling midsize sedans in the US automobile industry. The results show that the addition of WOM improves the ability to accurately forecast consumer demand in a statistically significant way. This suggests that producers who invest in supporting their product development efforts with design for market systems analyses that account for social networks may be able to better optimize their decision-making and increase their market success.