Combinatorial auctions -- in which bidders can bid on combinations of goods -- have been shown to increase the economic efficiency of a trade when goods have complementarities. Until now, the computational complexity of determining winners coupled with the cognitive complexity of formulating combinatorial bids has prevented this mechanism from reaching the online marketplace. Recent theoretical developments have lessened the barrier posed by computational complexity, but the issue of cognitive complexity remains unexplored. Drawing from recent research in continuous combinatorial auctions, this study uses a data-driven approach to explore bidder behavior in such auctions using three experimental treatments that differ in the type of information feedback provided to participants. We examine bidder strategies in complex combinatorial bidding environments as a step toward understanding how bidders react to the complexity of continuous auctions and to the aid provided by different forms of feedback. To do so, we collect the bids placed by bidders and the clicks generated by bidders as they explore different bundling options. Using cluster analysis, we find three stable bidder strategies across the three treatments. We establish the robustness of the strategies using a separate set of experiments with a different setup. We also benchmark the continuous auctions against an iterative form of combinatorial auction used in the literature -- the Combinatorial Clock auction. The enumeration of the bidding strategies across different types of feedback, along with the analysis of their financial implications, is offered to help practitioners design better combinatorial auction environments.