Neslin, Scott A., Thomas P. Novak, Kenneth R. Baker, and Donna L. Hoffman, “An Optimal Contact Model for Maximizing Online Panel Response Rates,” (August 2006).
Abstract. We develop and test an optimization model for maximizing response rates for online marketing research survey panels. The model consists of: (1) a decision tree predictive model that classifies panelists into “states” and forecasts the response rate for panelists in each state, and (2) a linear program that derives a plan specifying how many panelists should be solicited from each state in order to maximize response rates. The linear program is forward looking in that it optimizes over a finite horizon during which S studies are to be fielded. It takes into account the desired number of responses for each study, the likely migration pattern of panelists between states as they are invited and respond or don’t respond, as well as demographic requirements. The model is implemented using a rolling horizon, whereby the optimal solution for S successive studies is derived and implemented for the first study; then, as results are observed, an optimal solution is derived for the next S studies, and the solution is implemented for the first of these studies, etc. The procedure is field tested and shown to increase response rates significantly, compared to random selection and the heuristic currently being used by panel management. Implications are discussed for further model development, implementation issues for online panel managers, and for the broader area of optimal contact models in customer relationship management. Download pdf.
