AI

Introduction to the Aggregate Marketing System Simulator

Abstract

Advertising is becoming more and more complex, and there is a strong demand for measurement tools that are capable of keeping up. In tandem with new measurement problems and solutions, new capabilities for evaluating measurement methodologies are needed. Given the complex marketing environment and the multitude of analytical methods that are available, simulation has become an essential tool for evaluating and comparing analysis options. This paper describes the Aggregate Marketing System Simulator (AMASS), a sim- ulation tool capable of generating aggregate-level time series data related to marketing measurement (e.g., channel-level marketing spend, website visits, competitor spend, pricing, sales volume, etc.). It is flexible enough to model a wide variety of marketing situations that include different mixes of advertising spend, levels of ad effectiveness, types of ad targeting, sales seasonality, competitor activity, and much more. A key feature of AMASS is that it generates ground truth for marketing performance met- rics, including return on ad spend and marginal return on ad spend. The capabilities provided by AMASS create a foundation for evaluating and improving measurement methods, including media mix models (MMMs), campaign optimization (Scott, 2015), and geo experiments (Vaver and Koehler, 2011), across complex modeling scenarios.