Web platforms run many marketing campaigns at once, and for each one they must decide which products to promote and which users to target. The best users depend on the products, and the best products on the users. The campaigns also compete with one another, because each user and each item may belong to only one of them and each audience is capped by the campaign budget. Still, existing approaches fix one side and search the other, choosing an assortment and then looking for its audience, or ranking items for one user at a time. Either way they solve a restricted version of the full problem, implying that a jointly optimized assignment would be at least as good and possibly far better. What the restriction costs, and whether the joint problem is solvable at the scale campaigns are run, remain unknown. Here we formalize the joint task as auto-targeting: assigning users and items to multiple disjoint, capacity-bounded campaigns that maximize the affinity between them. We adapt constrained spectral biclustering, greedy local search with pairwise swaps, and a multi-armed bandit formulation to the task and compare them against the conventional decoupled baseline. On synthetic data, two Amazon Reviews benchmarks, and two proprietary commercial datasets, auto-targeting raises campaign lift over the best decoupled pipeline, from around 20\% on the two smaller, denser datasets to more than threefold on the two largest. Biclustering yields the highest campaign quality across datasets, while bandit-based methods run fastest on the largest ones.

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