A Pharmaceutical Company Decides on a Marketing Strategy Using Agent-Based Modeling

A Pharmaceutical Company Decides on a Marketing Strategy Using Agent-Based Modeling

Problem:

Sterling Simulation consulting company was chosen to provide an agent-based pharmaceutical marketing model for a drug manufacturer. This company owned two competing non-generic drugs on the same market. One drug was well established and tended to be the industry leader, and the other one was recently introduced.

There were several concerns about how to obtain a useful market share for the newer drug, while maintaining or increasing the market share for the company’s drugs as a whole. The company used different forms of promotion, including direct-to-consumer (DTC) marketing strategy, which usually concludes in advertising through TV, pint, and other mass and social media. The pharmaceutical market simulation model was designed to answer the question, “When should the company stop DTC marketing for the new drug in order to maximize total prescription sales?”. A sufficiently accurate pharmaceutical market model could save the pharmaceutical company tens of millions of dollars.

Solution:

Pharamceutical Market Simulation

Pharamceutical Market Simulation

Traditionally, marketing analytics have determined different spending scenarios by using a marketing-mix model to calculate the impact of marketing. However, this approach does not provide a clear picture of how the changes in spending impact the results. Marketing-mix models can give you weights, but they cannot tell you why these weights exist. The company wanted a pharamceutical market simulation model that would be at least as good as a marketing-mix model, which meant the contractors had to find the percentage point of market share over time.

To obtain a better understanding of the mechanics behind marketing-mix models (for example, why does DTC marketing give diminishing returns v. sales rep visits?), one alternative is to use an agent-based modeling approach (ABM) to pharmaceutical market simulation. It provides the secondary benefit of removing assumptions from a pharmaceutical market simulation model, which allows for a more complete understanding.

One thing to note about ABM approaches in general, and this model in particular, is that the data requirements are different from those in marketing-mix models and, in general, higher. In this case, the pharmaceutical company had the data and was able to take full advantage of pharmaceutical market simulation using ABM approach. Agent-based models tend to be more able to deal with assumptions and provide insights, which are more profitable, in this case for pharmaceutical marketing modeling, than simple answers.

The ABM approach was selected, and, for this pharmaceutical marketing modeling project, AnyLogic software was chosen. These are a few of the reasons AnyLogic was used for the pharmaceutical marketing simulation model building:

The model framework differed substantially from traditional marketing-mix models. Specifically, this pharmaceutical market simulation model considered the entire patient/doctor interaction in order to determine the impact of marketing expenditure. Additionally, the impacts of the new drug’s introduction to the pharmaceutical market was integrated to obtain correct market share information.

The pharmaceutical market simulation model consists of the following elements:

The latter two elements were not described in the case because they had little behavior in the model.

The patients in the model were all diagnosed with the specific illness that the pharmaceutical market handles. The disease involved was not life threatening, so this drug category is elective. The behavior of the patient included:

Pharmaceutical Modeling — Simulation Model Logic

Pharmaceutical Modeling — Simulation Model Logic

The doctors in the pharmaceutical market simulation model had different specializations, related to the illnesses, and had differing numbers of patients, depending on specialization. The behavior of the doctors included:

You will find the preassigned model of a patient’s behavior during and after an appointment, as well as the description of a patient’s lifecycle below.

Sales representatives were assigned to a pool of doctors. They visited their doctors at a different rate, based on the patient pool of each doctor and historical information. During the visit, the representative tried to change the doctor’s attitude towards a certain drug by adding samples to the doctor’s supply.

The drugs in the pharmaceutical market simulation model were two of the company’s drugs, another specific non-generic drug, and generics as a group.

Outcome:

The pharmaceutical market simulation model took a while to calibrate properly. This was because the data was sparse in some areas. The model was calibrated primarily with each drug’s (or drug family’s, in the case of the generics) market share, for both patients and prescriptions per month. Once calibrated, the pharmaceutical market simulation model showed that the ideal time to stop DTC marketing for the new drug would have been six months before the current date. This was made clear by the calibrated model. Since this was unfeasible, the given answer was to stop DTC marketing as soon as feasible.

Another interesting insight was connected with sales rep marketing. It was clarified, that over time, the doctors’ preferences would overwhelma patients’ preferences for drugs. That is why the money invested in sales rep visits, in contrast to DTC marketing, did not show any diminishing returns and always influenced market share. This was not unexpected, given that the availability of samples was directly tied to the visits, and had a broad impact on the willingness of patients to try the drug.

Concerning the budget issue, it was clearly demonstrated, that should the company follow the results of the pharmaceutical market simulation model and eliminate DTC marketing, doing so would save at least $10M a year.

To learn more about this project and pharmaceutical market modeling watch the presentation by Scott Hebert, Vice President of Sterling Simulation
Scott Herbert presentation video screenshot

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