In this work, we propose the problem AdInvest, which models the decision-making process for allocating investment in digital marketing from the advertiser perspective. For the proposed problem, we define an algorithm called balGreedy, and we prove its guarantees in deterministic and stochastic instances of the AdInvest. The proven theorems assure to our algorithm worst-case results relatively close to OPT, in several types of instances raised during the work. In particular, we focus on the instances that model the audience saturation effect, which is present in the dynamics of online advertisements. As shown in the computational experiments, the balGreedy algorithm had been consistently efficient compared to the alternative policies adopted, both in the instances generated by simulation and in real instances built from the data of a certain Facebook Ads advertiser.