How Simulation Benefits the Pharmaceutical Industry
This post was originally featured in Vol. 1, Issue 3 of the Rx Data News.
Featured Interview: Greg Silverman, Founder and CEO of Concentric
Before Concentric, Greg was Global Managing Director, Analytics and Valuation, for Interbrand. Greg received his BBA in Marketing and Retail from the University of Georgia and his MS in Management from Georgia State University.
Rx Data News: Describe some of the ways advanced analytics are having an impact on the pharmaceutical industry.
Mr. Silverman: The marketing landscape is changing, and with that, the way pharmaceutical companies market themselves is changing, too. Across industries, teams are quickly trying to pinpoint today’s optimal marketing strategy, but for pharma, the stakes are higher – and not just because they invest billions of dollars in bringing their products to market. While many companies have the luxury of testing marketing campaigns through trial-and-error, pharmaceutical companies distributing life-saving medications – like drugs to take on the opioid crisis, for example – must tackle urgent problems now. There is no room for error.
That’s where advanced analytics comes in. Advanced analytics allows pharma companies to strategically launch a product, refine messaging and deploy optimal marketing strategies, fast. Once a product is live, it gives companies the ability to closely evaluate success factors like adherence to drug regimen and outcome attribution. For pharma, the window of opportunity in Marketing is narrowing. Television commercials offer limited ad time, and a significant portion of it is dedicated to the narration of side effects. Meanwhile, patients have more access to medical information than ever, and many arrive at the doctor with a list of symptoms and a self diagnosis. Marketing is fighting individuals with no medical training diagnosing themselves and a narrowing media window. Finding the right go-to-market strategy is hard.
Now, business leaders have the ability to conduct rapid what-if forecasting, quickly integrating massive and diverse datasets to test scenarios and better understand the impact of their decisions.Greg Silverman, CEO and founder, Concentric
Advanced analytics incorporates techniques like machine learning, artificial intelligence (AI) and simulation to forecast future outcomes, unlike basic business intelligence (BI) tools that evaluate historical information. Now, business leaders have the ability to conduct rapid what-if forecasting, quickly integrating massive and diverse datasets to test scenarios and better understand the impact of their decisions. At product launch, this means advanced analytics is able to take numerous datasets into consideration to optimize strategy, delivery and performance, ultimately mitigating risk of missing sales projections and risk of a doctor not having the drug they need to help a sick patient recover.
The great benefits that advanced analytics can provide to pharmaceutical companies are known and accepted – and not only in driving sales, but also in other areas of business, from research and development to manufacturing and supply chain. Today, the existence of advanced analytics and the opportunity it presents – impacts pharmaceutical companies by challenging them to reconsider how their organization operates. Becoming a data-driven company is about so much more than investing in IT infrastructure and hiring an analytics team. To take advantage of all that advanced analytics has to offer, pharma companies are tasked with bringing information across their organization, out of silos, and to work together to integrate data analytics throughout their entire process.
Rx Data News: What are some of the benefits that simulation forecasting can provide to pharmaceutical companies?
Mr. Silverman: The healthcare industry is constantly evolving, requiring flexibility that many advanced analytics solutions are ill-equipped to address. Most analytics tools are backwards-facing, deriving their analysis by extrapolating from past results, in the case of BI tools, or by building a predictive model from historical data in the case of machine learning or regression-based approaches. These methods are not adept at addressing a changing business environment. When regulations or macroeconomic conditions change (which happens much more frequently now), even good predictive models become stale and inefficient in predicting what will happen, especially in an industry as volatile as healthcare and pharma.
Simulation forecasting is unique among advanced analytics techniques in that it involves simulating an entire market, leveraging modern computing power, machine learning and principles of behavioral economics. The idea is to define the population of people a company aims to reach, and then to account for the range of factors that will influence their purchase decision, like price, quality convenience, alternative options, awareness and perception, and even human irrationality. Once the model has been trained to understand the rules of decision-making within the market, businesses can test different scenarios, pulling various levers to see how certain investments might influence a person to make a decision in their favor.
With simulation, different areas of expertise that exist inside a pharma company can work together, supported by AI, to get collaborative, accurate answers, faster.Greg Silverman
For pharmaceutical companies, simulation offers the ability to consider how simultaneous decisions impact each other. Instead of turning to one point solution that suggests strategic messaging, and another that suggests strategic marketing spend, simulation forecasting allows point solutions to speak to one another, to determine what will come of decisions made in tandem. With simulation, different areas of expertise that exist inside a pharma company can work together, supported by AI, to get collaborative, accurate answers, faster.
For example, we worked with a pharmaceutical company that was transitioning one of its products from prescription to OTC. The marketing team was experienced with Rx launches, but had never launched a product in consumer retail, and the category was heavily crowded. When they tested the marketing plan they built using traditional Rx tactics, our simulation software revealed that they would significantly miss their sales target unless they adjusted their marketing mix. The team then tested a different strategy, one more commonly used in CPG marketing, and found that shifting their investment from an influencer strategy to in-store support would enable them to meet their goal. The launch program resulted in the highest grossing prescription to OTC in company history. Through simulation forecasting, healthcare and pharmaceutical companies answer very complex questions much faster and with a high degree of confidence.
Rx Data News: How can collaborative analytics augment an organization’s intelligence?
Mr. Silverman: Healthcare data comes from a wide variety of sources, which is difficult for most advanced analytics platforms to handle. Most analytics tools use a single flat file for analysis, which requires data preparation, a time-consuming task. Some estimates place the amount of time spent on data wrangling at 80% of a data science or advanced analytics project. Collaborative analytics platforms provide connectors to multiple data sources and the intelligence to blend the data seamlessly for advanced modeling and simulation, allowing organizations to make strategic decisions while understanding how they will impact their entire business. That said, whether technology is involved or not, collaborative processes always lead to smarter decisions.
Rx Data News: What role do you see artificial intelligence playing in the pharmaceutical industry?
Mr. Silverman: Artificial intelligence or machine learning is a part of the solution, but it’s just one piece of the puzzle. Machine learning is adept at answering a single question, such as predicting patient readmissions to avoid ACA penalties, or using analysis to determine which patients are likely to develop infections to develop preemptive treatment plans. But for more complex business questions, like how a new drug with no historical data will perform in market, healthcare and pharmaceutical companies need new technologies that are powered by AI like simulation. Ultimately, simulation, which incorporates a number of advanced scientific disciplines, including machine learning and behavioral economics, will allow healthcare organizations to more easily react to changes by understanding how their actions will affect the market.
Rx Data News: What makes Concentric unique among analytics companies?
Mr. Silverman: Concentric’s unique in that it has a simulation platform – it’s the only simulation software of its kind on the market. The software integrates AI, behavioral science and diverse datasets to build a predictive model with human decision-making at the center. Now, companies can test how the market will respond to strategic initiatives, products and opinions by quickly running scenarios in a simulated environment – testing strategies weeks, months and even years before launch. It’s a scientific approach to decision making that takes behavioral economics, network science and market analytics into consideration, made possible by recent advancements in cloud computing.