Acquiring High Quality Customer Data with Low Cost

This work concerns optimal customer data acquisition and selection problem. We first propose using a divide-and-conquer technique to find empirical distribution of the customer data. We then formulate a data acquisition problem as an optimization problem that maximizes the quality of the acquired data while keeping the cost of acquisition as low as possible. We propose using generalized second-price (GSP) auction to acquire customer data and show that when the number of bidders is large, GSP is a truth-telling mechanism. We derive the analytical solution for the optimization problem, which finds a set of data that best represents the probability distribution of the target population relative to the acquisition cost. An experimental study is conducted to demonstrate the effectiveness of the proposed approach.