This paper proposes an approach that leverages data on customer purchasing sensitivity to quoted order-to-delivery times (ODTs) when designing middle-mile consolidation networks to maximize the profit of e-commerce retailers. Our approach integrates quoted ODT-dependent sales volume predictions into a new mixed-integer program (MIP) that simultaneously determines ODT quotes and a consolidation plan, characterized by the frequency of load dispatches on each transportation lane. The objective of the MIP is to maximize sales revenue net fulfillment cost while ensuring that quoted ODTs are met with a high probability as set by the retailer. We linearize the ODT chance constraints by approximating the waiting delay incurred between load dispatches using convex piecewise-linear functions. To find high-quality solutions for large, practically sized instances, we build an adaptive IP-based local search heuristic that improves an incumbent solution by iteratively optimizing over a smartly selected subset of commodity ODT and/or route options, which is randomized and adjusted based on solver performance. Results from a U.S.-based e-commerce partner show that our approach leads to a profit increase of 10\% when simply allowing a marginal change of one day to the current ODT quotes. In general, we observe that integrating ODT-dependent customer purchasing estimation into a decision model for joint ODT quotation and consolidation network design achieves an optimal trade-off between revenue and fulfillment cost.