Product brands employ shopper marketing (SM) strategies to convert shoppers along the path to purchase. Traditional marketing mix models (MMMs), which leverage regression techniques and historical data, can be used to predict the component of sales lift due to SM tactics. The resulting predictive model is a critical input to plan future SM strategies. The implementation of traditional MMMs, however, requires significant ad-hoc manual intervention due to their limited flexibility in (i) explicitly capturing the temporal link between decisions; (ii) accounting for the interaction between business rules and past (sales and decision) data during the attribution of lift to SM; and (iii) ensuring that future decisions adhere to business rules. These issues necessitate MMMs with tailored structures for specific products and retailers, each requiring significant hand-engineering to achieve satisfactory performance -- a major implementation challenge. We propose an SM Optimization and Inverse Learning Engine (SMOILE) that combines optimization and inverse reinforcement learning to streamline implementation. SMOILE learns a model of lift by viewing SM tactic choice as a sequential process, leverages inverse reinforcement learning to explicitly couple sales and decision data, and employs an optimization approach to handle a wide-array of business rules. Using a unique dataset containing sales and SM spend information across retailers and products, we illustrate how SMOILE standardizes the use of data to prescribe future SM decisions. We also track an industry benchmark to showcase the importance of encoding SM lift and decision structures to mitigate spurious results when uncovering the impact of SM decisions.