This paper investigates how a retailer's store brand supply source impacts vertical pricing and supply channel profitability. Using chain-level retail scanner data from major supermarkets in Boston prior to the leading retailer's divestiture of its store brand milk processing to a major brand manufacturer I estimate a random coefficients logit demand model employing a Bayesian estimation approach. Bayesian decision theory is applied to select from a set of pricing games the one most likely for the data sample analyzed. Results from this analysis indicate that the empirically valid model has the pre-divested retailer integrated into the processing of its own milk and takes as given the wholesale price of brand milks while competing retailers have nonlinear pricing contracts with brand manufacturers who produce their store brands. This model is matched against a series of counterfactual simulations as a baseline. The counterfactual simulations consider the eventual divestiture of store brand milk processing by the leading retailer to a major brand manufacturer as well as two fictional markets where store brands are no longer offered and optimal nonlinear pricing breaks down making way for a double marginalization outcome. Simulation results indicate that the divestiture likely improved profitability and reduced retail prices by eliminating double marginalization.