Influence maximization is the task of finding k seed nodes in a social network such that the expected number of activated nodes in the network (under certain influence propagation model), referred to as the influence spread, is maximized. Lattice influence maximization (LIM) generalizes influence maximization such that, instead of selecting k seed nodes, one selects a vector x = (x_1, ..., x_d) from a discrete space X called a lattice, where x_j corresponds to the j-th marketing strategy and x represents a marketing strategy mix. Each strategy mix x has probability h_u(x) to activate a node u as a seed.LIM is the task of finding a strategy mix under the constraint x_1+...+x_d <= k such that its influence spread is maximized. We adapt the reverse influence sampling (RIS) approach and design scalable algorithms for LIM. We first design the IMM-PRR algorithm based on partial reverse-reachable sets as a general solution for LIM, and improve IMM-PRR for a large family of models where each strategy independently activates seed nodes. We then propose an alternative algorithm IMM-VSN based on virtual strategy nodes, for the family of models with independent strategy activations. We prove that both IMM-PRR and IMM-VSN guarantees 1-e-εapproximation for small ε> 0. Empirically, through extensive tests we demonstrate that IMM-VSN runs faster than IMM-PRR and much faster than other baseline algorithms while providing the same level of influence spread. We conclude that IMM-VSN is the best one for models with independent strategy activations, while IMM-PRR works for general modes without this assumption. Finally, we extend LIM to the partitioned budget case where strategies are partitioned into groups, each of which has a separate budget, and show that a minor variation of our algorithms would achieve 1/2 -εapproximation ratio with the same time complexity.