Sterling Simulation consulting company was chosen to provide an agent-based marketing model for a pharmaceutical firm. The 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 model was supposed to answer the question, “When should the company stop DTC marketing for the newer drug to maximize total prescription sales?” on DTC marketing investment. The provided answer, if followed, could save the pharmaceutical company tens of millions of dollars.
Traditionally, marketing analytics have decided on different spend scenarios by using a marketing-mix model to determine the impact of marketing spends. However, this approach does not provide articulated understanding of why the spend changes impact the results. To obtain a better understanding of the mechanics behind marketing-mix models and expand their functionality, one alternative is to use agent-based modeling (ABM). It provides the secondary benefit of, which allows for eliminating real-world risks.
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 agent-based modeling (ABM). It provides the secondary benefit of removing assumptions from the model, which allows for a more complete understanding.
Once the approach of ABM was selected, AnyLogic was chosen as the software platform for model building. These were a few of the reasons:
- The consulting company was familiar with the software and its capabilities.
- AnyLogic allows for the greatest flexibility in comparison to other modeling frameworks.
- AnyLogic has the best visualization possibilities for presenting the model to the executives.
The model framework differed substantially from a traditional marketing-mix model that the company used to apply. Specifically, the model considered the entire patient/doctor interaction in order to determine the impact of market spends. Additionally, the impacts of the new drug’s introduction to the market was integrated to obtain correct market share information.
The model consisted of the following elements, presented as agents:
- sales representatives;
The patients in the model were all diagnosed with the specific illness and prescribed with a list of similar drugs to choose from. The drugs in the model were the two company drugs, another specific non-generic drug, and generics as a group. The disease involved was not life threatening, so this drug category was elective.
The behavior of the patient included:
- Meeting with their physician every three months.
- Determining whether they desired a specific drug (the result showed primary DTC market impact).
- Awareness of different drugs based on advertising and the ability to request it from doctors.
- Whether they filled their prescription (depended primarily on drug’s price).
- Whether they stayed on a drug (on average, the first month and second/third month loss was calculated to be around 40% and 20% or less, respectively).
The doctors in the model had different specializations related to the illness, and had differing numbers of patients, depending on specialization. The behavior of the doctor included:
- Handling patient appointments.
- Determining what drug to prescribe to a patient (theoretical preference was based on clinical drug performance, practical preference was based on patient reaction on drugs).
- Whether to provide samples or a script to new patients.
- Interacting with sales representatives.
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 on the statechart.
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 model was calibrated primarily to each drug’s (or drug family’s, in the case of the generics) market share in terms of both patients and prescriptions per month. Once calibrated, the 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 established by noticing that there was no difference in the calibrated metrics over the same period. The given answer was to stop DTC marketing in the near future.
Another interesting insight was connected with sales rep marketing. It was clarified, that over time, the doctors’ preferences overwhelmed patients’ preferences to the drugs. That is why the money invested in sales rep visits, in contrast to DTC marketing, always showed steady returns and influenced market share.
Concerning the budget issue, it was safe to say, that should the pharmaceutical company follow the results of the model and eliminate DTC marketing, they would save at least $10M a year.