A successful campaign should be able to attract investment from the brand, and meanwhile manage the conflicting interests in the campaign cost between the brand and the influencers. As such, the agency between these two stakeholders plays a vital role. Motivated by the above, we stand in the agency's shoes to formulate an interesting yet practical problem, namely Profit Divergence Minimization in Investment-Persuasive Influencer Marketing Campaign (PDMIC). This problem aims to (i) minimize the divergence of the actual hiring prices from the asking prices of the influencers and meanwhile (ii) maintain the attractiveness of the pricing scheme for the influencers to the brand. We show that this problem is NP-hard. To mitigate the challenge of the extremely large searching space of the hiring prices of the influencers, we solve this problem by firstly considering a restrictive searching sub-space and then gradually expanding the searching sub-space to the whole space in the end (specifically, from binary price choices to a set of integer prices and then to any price in the feasible price range). We propose effective yet efficient approximate algorithms for solving the problem in each of these settings. Extensive experiments demonstrate the superiority of our methods.