  Now-a-days, \emph{Online Social Networks} have been predominantly used by
commercial houses for viral marketing where the goal is to maximize profit. In
this paper, we study the problem of Profit Maximization in the two\mbox{-}phase
setting. The input to the problem is a \emph{social network} where the users
are associated with a cost and benefit value, and a fixed amount of budget
splitted into two parts. Here, the cost and the benefit associated with a node
signify its incentive demand and the amount of benefit that can be earned by
influencing that user, respectively. The goal of this problem is to find out
the optimal seed sets for both phases such that the aggregated profit at the
end of the diffusion process is maximized. First, we develop a mathematical
model based on the \emph{Independent Cascade Model} of diffusion that captures
the aggregated profit in an \emph{expected} sense. Subsequently, we show that
selecting an optimal seed set for the first phase even considering the optimal
seed set for the second phase can be selected efficiently, is an
$\textsf{NP}$-Hard Problem. Next, we propose two solution methodologies, namely
the \emph{single greedy} and the \emph{double greedy} approach for our problem
that works based on marginal gain computation. A detailed analysis of both
methodologies has been done to understand their time and space requirements. We
perform an extensive set of experiments to demonstrate the effectiveness and
efficiency of the proposed approaches with real-world datasets. From the
experiments, we observe that the proposed solution approaches lead to more
profit compared to the baseline methods and in particular, the double greedy
approach leads to up to $5 \%$ improvement compared to its single\mbox{-}phase
counterpart.
