  Computational marketing has become increasingly important in today's digital
world, facing challenges such as massive heterogeneous data, multi-channel
customer journeys, and limited marketing budgets. In this paper, we propose a
general framework for marketing AI systems, the Neural Optimization with
Adaptive Heuristics (NOAH) framework. NOAH is the first general framework for
marketing optimization that considers both to-business (2B) and to-consumer
(2C) products, as well as both owned and paid channels. We describe key modules
of the NOAH framework, including prediction, optimization, and adaptive
heuristics, providing examples for bidding and content optimization. We then
detail the successful application of NOAH to LinkedIn's email marketing system,
showcasing significant wins over the legacy ranking system. Additionally, we
share details and insights that are broadly useful, particularly on: (i)
addressing delayed feedback with lifetime value, (ii) performing large-scale
linear programming with randomization, (iii) improving retrieval with audience
expansion, (iv) reducing signal dilution in targeting tests, and (v) handling
zero-inflated heavy-tail metrics in statistical testing.
