Evaluating Marketing Allocation and Pricing Rules by Monte-Carlo Simulation

The goal of this dissertation is to help practitioners make marketing decisions in situations of little information with high levels of uncertainty. We begin by constructing an allocation procedure and demonstrate that it outperforms all other methods it can be compared to. Next, we compare 8 pricing rules in a monopoly, and determine which rules are preferable, based on the situation. Both of these are evaluated with Monte-Carlo simulations. The largest novelty in the approach is the minimal information required to apply the rules (2 periods) and the robustness of the results independent of the shape and parameters of the response functions, and the size of the error terms. Finally, a test is developed for compairing methods in ANOVAs with all higher interactions and an arbitrary number of variables, which is a yet untapped area of mathematical research

Heuristic pricing rules not requiring knowledge of the price response function

Heuristic rules are appropriate, if a decision maker wants to set the price of a new product or of a product, whose past price variation is low, and budget limitations prevent the use of marketing experiments or customer surveys. Whereas such rules are not guaranteed to provide the optimal price, generated profits should be as close as possible to their optimal values. We investigate eleven pricing rules that do not require that a decision maker knows the price response function and its parameters. We consider monopolistic market situations, in which sales depend on the price of the respective product only. A Monte Carlo simulation that is more comprehensive than extant attempts found in the literature, serves to evaluate these rules. The best performing rules either hold price changes between periods constant or make them dependent on the previous absolute price difference. These rules also outperform purely random price setting, which we use as benchmark. On the other hand, rules based on arc elasticities or on a loglinear approximation to sales and prices, turn out to be even worse than random price setting. In the conclusion, we discuss how heuristic pricing rules may be extended to deal with product line pricing, additional marketing variables (e.g., advertising, sales promotion, and sales force) and a duopolistic market situation

Resource Allocation Heuristics for Unknown Sales Response Functions with Additive Disturbances

We develop an exploration-exploitation algorithm which solves the allocation of a fixed resource (e.g., a budget, a sales force size, etc.) to several units (e.g., sales districts, customer groups, etc.) with the objective to attain maximum sales. This algorithm does not require knowledge of the form of the sales response function and is also able cope with additive random disturbances. The latter as a rule are a component of sales response functions estimated by econometric methods. We compare the algorithm to three rules of thumb which in practice are often used for this allocation problem. The comparison is based on a Monte Carlo simulation for five replications of 192 experimental constellations, which are obtained from four function types, four procedures (i.e., the three rules of thumb and the algorithm), similar/varied elasticities, similar/varied saturations, and three error levels. A statistical analysis of the simulation results shows that the algorithm performs better than the three rules of thumb if the objective consists in maximizing sales across several periods. We also mention several more general marketing decision problems which could be solved by appropriate modifications of the algorithm presented

Resource allocation procedures for unknown sales response functions with additive disturbances

We develop a modified exploration–exploitation algorithm which allocates a fixed resource (e.g., a fixed budget) to several units with the objective to attain maximum sales. This algorithm does not require knowledge of the form and the parameters of sales response functions and is able to cope with additive random disturbances. Note that additive random disturbances, as a rule, are a component of sales response functions estimated by econometric methods. We compare the developed algorithm to three rules of thumb which in practice are often used to solve this allocation problem. The comparison is based on a Monte Carlo simulation for 384 experimental constellations, which are obtained from four function types, four procedures (including our algorithm), similar/varied elasticities, similar/varied saturations, high/low budgets, and three disturbance levels. A statistical analysis of the simulation results shows that across a multi-period planning horizon the algorithm performs better than the rules of thumb considered with respect to two sales-related criteria

Delocalized, Asynchronous, Closed-Loop Discovery of Organic Laser Emitters

Contemporary materials discovery requires intricate sequences of synthesis, formulation and characterization that often span multiple locations with specialized expertise or instrumentation. To accelerate these workflows, we present a cloud-based strategy that enables delocalized and asynchronous design–make–test–analyze cycles. We showcase this approach through the exploration of molecular gain materials for organic solid-state lasers as a frontier application in molecular optoelectronics. Distributed robotic synthesis and in-line property characterization, orchestrated by a cloud-based AI experiment planner, resulted in the discovery of 21 new state-of-the-art materials. Automated gram-scale synthesis ultimately allowed for the verification of best-in-class stimulated emission in a thin-film device. Demonstrating the asynchronous integration of five laboratories across the globe, this workflow provides a blueprint for delocalizing – and democratizing – scientific discovery