Market structure maps spatially represent competitive relationships among brands and products. In these maps, each product is typically visualized in a single map location. Such visualization, however, does not adequately accommodate multimarket membership products (MMPs), which are products that compete in multiple submarkets that are each characterized by distinct competitors and consumer preferences. The author proposes a novel framework that accommodates MMPs by visualizing products in the local context of each submarket in which they compete. This framework is then used to study market structure in the digital camera market. Competitive relationships are inferred from consumers’ online searches using bootstrapped neural product embedding. The research shows that 8% of cameras are MMPs and that 50% of all submarkets are affected by MMPs. The map generated with the proposed framework uncovers mismatches between manufacturer positioning and consumer perception of several cameras. An extensive comparison of the proposed framework with alternative mapping methods reveals that (1) commonly used mapping methods misrepresent MMPs’ competitive positions in their maps and (2) ignoring MMPs can lead to distorted visualizations of the underlying market structure. Both problems compromise firms’ abilities to accurately assess products’ competitive positions and evaluate the effectiveness of their positioning strategies.