Thursday, November 9, 2017

Applications of Monte Carlo Simulation in Marketing Analytics
Summary and Critique by Oddinigwe Onyemenem

At the inception, Monte Carlo Simulation (MCS) was developed for use in designing nuclear weapons. MCS is an effective way to simulate processes which involve chance and uncertainty. It can be widely utilized in diverse areas such as market sizing, customer lifetime value measurement, customer service management, marketing, Customer Relationship Management(CRM), traffic flow simulation, deep coal mining logistics, post office queuing protocols, hazard analysis of chemical plants, and risk analysis in global finance.

The paper emphasizes the importance of MCS to marketing analytics lies in its ability to integrate several components to create a complete model. Some of these components may have been developed by other methods. The author acknowledges that the process of integration may create complications. Three case studies are used to illustrate the application of MCS in various fields. First, using MCS to integrate models of customer behavior. Second, utilizing MCS to help answer many important business questions about call center resourcing and the impact this has on customer service. Third, testing the performance of analysis software by using synthetic customer data generated using MCS.

The paper identifies three main issues of MCS:
·       Verification – ensuring that the implemented simulation follows the specification designed for it.
·       Validation – ensuring the simulation acceptably characterizes the real world that it is intended to model, so the pertinent business questions can still be answered. There are two elements to validation — validating individual components of the model and validating the overall system.
·       Computational burden – ensuring that adequate provisions are made for computational requirements from the beginning because of the amount of times simulations need to be run repeatedly.

The figure below shows specialized MCS software tools which can significantly reduce the time needed to implement other useful models.

Monte Carlo simulation can be useful in forecasting especially in understanding risk impact and uncertainty in quantitative analysis. The paper provided an in-depth look into the application of MCS in marketing analytics and other fields and how it can enhance the use of other models. Adhering to the three key issues stated in the paper will be instrumental in appropriately conducting MCS.

Furness, P. J Direct Data Digit Mark Pract (2011) 13: 132.


  1. I agree Odi, Monte Carlo simulations is a great forecasting tool. It seems that this method, like other methods we mention also takes into account future risk, however, the Monte Carlo simulation provides less uncertainty to its outcome. This is due to the method being very flexible, in which it allows us to vary risk assumptions under all parameters and thus model a range of possible outcomes. Although the advantage of Monte Carlo is its ability to factor in a range of values for various inputs, this is also its greatest disadvantage in the sense that assumptions need to be fair because the output is only as good as the inputs. In the end, Monte Carlo simulation is a technique used to understand the impact of risk and uncertainty by measuring how likely the resulting outcomes are.

  2. The article is spot-on in identifying three main issues of Monte Carlo Simulation. Verification and validation are key because the address the common computer science concept of garbage in, garbage out as it relates to the inputs and outputs. Additionally, computers with faster processors and larger memories are essential for repeating the simulation.

  3. I agree that the main issues of MCS include verification, validation, and computational burden. It is a great technique for mitigating risk and uncertainty and seems to have gained traction in the business world.