This webinar discussed the relevance of different types of data for a retailer’s ability to predict sales of new products before their launch. Their approach combined four information sources: (1) in-house observable market data such as price and promotion level, (2) customer attitudes based on a representative survey, (3) incentivized purchasing decisions, and (4) functional magnetic resonance imaging (fMRI) data from a relatively small sample of individuals collected in a laboratory. The researchers used a large German retailer’s weekly sales data to define an estimation data set containing 34 packaged foods and drinks. This estimation data set was used to estimate the parameters of the model. The research then used the parameter estimates to predict sales of 17different products before they were launched. Results indicate that using fMRI data to forecast sales of new products significantly increased forecasting accuracy: It led to a 28.6% better forecast than a naïve model that considered historic sales data only, while the model combining all data led to an improvement of 38.6%. Using this approach, managers can quantify the benefits of collecting different types of data beyond observable market data—including neuroscientific data—to predict the market success of new products.