Sequential Learning in Designing Marketing Campaigns for Market Entry

Developing marketing campaigns for a new product or a new target population is challenging because of the scarcity of relevant historical data. Building on dynamic Bayesian learning, a sequential optimization assists in creating new data points within a finite number of learning phases. This procedure identifies effective advertisement design elements as well as customer segments that maximize the expected outcome of the final marketing campaign. In this paper, the marketing campaign performance is modeled by a multiplicative advertising exposure model with Poisson arrivals. The intensity of the Poisson process is a function of the marketing campaign features. A forward-looking measurement policy is formulated to maximize the expected improvement in the value of information in each learning phase. A computationally efficient approach is proposed that consists of solving a sequence of mixed-integer linear optimization problems. The performance of the optimal learning policy over a set of benchmark policies is evaluated using examples inspired from the property and casualty insurance industry. Further extensions of the model are discussed.

This paper was accepted by Eric Anderson, marketing.