Backtracking is a basic technique of search-based satisfiability (SAT) solvers. In order to backtrack, a SAT solver uses conflict analysis to compute a backtracking level and discards all the variable assignments made between the conflicting level and the backtracking level. We observed that, due to the branching heuristics, the solver may repeat lots of previous decisions and propagations later. In this paper, we present a new backtracking strategy, which we refer to as partial backtracking. We implemented this strategy in our solver Nigma. Using this strategy, Nigma amends the variable assignments instead of discarding them completely so that it does not backtrack as many levels as the classic strategy. Our experiments show that Nigma solves 5% more instances than the version without partial backtracking.