In large markets comprising hundreds of products, comprehensive visualization of competitive market structures can be cumbersome and complex. Yet, as we show empirically, reduction of the analysis to smaller representative product sets can obscure important information. Herein we use big search data from a product- and price-comparison site to derive consideration sets of consumers that reflect competition between products. We integrate these data into a new modeling and two-dimensional mapping approach that enables the user to visualize asymmetric competition in large markets (>1,000 products) and to identify distinct submarkets. An empirical application to the LED-TV market, comprising 1,124 products and 56 brands, leads to valid and useful insights and shows that our method outperforms traditional models such as multidimensional scaling. Likewise, we demonstrate that big search data from product- and price-comparison sites provide higher external validity than search data from Google and Amazon. Data, as supplemental material, are available at http://dx.doi.org/10.1287/mksc.2015.0950 .
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