Strategic new product media planning under emergent channel substitution and synergy

New product and service introductions require careful joint planning of production and marketing campaigns. Consequently, they typically utilize multiple information channels to stimulate customer awareness and resultant word-of-mouth (WOM), availing of standard budget allocation tools. By contrast, when enacting strategic allocation decisions—which must align with other management imperatives—dividing expenditures across channels is far more complex. To this end, we formulate a multichannel demand model for new products (or services), amenable to analysis of inter- and intrachannel interaction patterns and with the WOM process, without building such interactions directly into the modeling framework. To address the notorious complexity of media planning over time, we propose a novel decomposition of the multichannel dynamic programming problem into two distinct “tiers”: the strategic tier addresses how to allocate total expenditure across channels, while the tactical tier studies how to allocate the channel-specific budgets (determined in the strategic tier) over time periods. This decomposition enables optimal media strategies to sidestep the curse of dimensionality and renders the model pragmatically estimable. Strategic tier analysis suggests a variety of novel insights, primarily that funds should not be allocated based on (relative) channel effectiveness alone but also systematically aligned with WOM generation. Specifically, each channel can face a “chasm-crossing” threshold, abruptly transitioning the adoption process from lead-users to mass-market penetration. Moreover, the model provides actionable managerial insights into when, and which, channel interactions are synergistic versus substitutive. Specifically, a channel's interactions are governed primarily by its own “leverage” (potential demand impact) and the WOM-based demand “momentum” (market penetration) it can generate, affording a novel basis for channel typography and firm action. The modeling framework is illustrated by examining camera sales for two media channels (free-standing inserts and radio) and their effects over 28 months. We use Bayesian machinery to estimate a highly flexible diffusion-based model, along with forecasts, media plans, and both theoretical and empirically-based qualitative insights.