A Stochastic Model for Budget Distribution over Time in Search Advertisements

In search advertisements, advertisers have to seek for an effective allocation strategy to distribute the limited budget over a series of sequential temporal slots (e.g., days). However, advertisers usually have no sufficient knowledge to determine the optimal budget for each temporal slot, because there exist much uncertainty in search advertising markets. In this paper, we present a stochastic model for budget distribution over a series of sequential temporal slots under a finite time horizon, assuming that the best budget is a random variable. We study some properties and feasible solutions for our model, taking the best budget being characterized by either normal distribution or uniform distribution, respectively. Furthermore, we also make some experiments to evaluate our model and identify strategies with the real-world data collected from practical advertising campaigns. Experimental results show that a) our strategies outperform the baseline strategy that is commonly used in practice; b) the optimal budget is more likely to be normally distributed than uniformly distributed.