A growing number of the Pharmaceutical products launched these days are specialty products. At the same time, spend is growing exponentially on specialty drugs. A CVS Caremark report stated that spend on specialty drugs is projected to be as high as $402 billion by 2020, but this is not going to be straightforward to achieve.
There are a few things that players in specialist markets must get right:
Most recently, specialty drugs cannot merely provide an incremental benefit on the existing therapies. They must really be breakthrough brands. Clinical trials are critical in proving this. Just having good trial data will not be enough anymore. You will need to set up the clinical trial design to ensure that it uncovers how the drug meets unmet clinical needs effectively, and most importantly, how it benefits patients.
Every marketer knows that clear differentiation of their product is part of their key to success. However, what we see with our analytics many times is that the aspects of the drug that marketers differentiate on are not actually the aspects driving that market. How many more times will we see a drug erroneously differentiated simply on mode of action rather than on what is really driving the market, which is around value and outcomes to patients, payers and healthcare professionals. And it is not just about differentiating on brand attributes. Payer and reimbursement differentiators are a critical part of the differentiation strategy for a success launch.
You need a strong multifunctional team to implement your sales and marketing. Without a strong team and a high performing culture, it will be much harder to achieve your goals, no matter how good everything else is.
Companies will have to learn to enhance their sales and marketing analytics to allow far more precision in the guidance marketers are given, and to include all of the channel stakeholders in the new process. In addition, critical analytics areas focus on market access and pricing analytics to ensure avoiding high profile style failures like those of Provenge (Dendreon), Zaltrap (Sanofi-Aventis), Benlysta (Human Genome Sciences) and others. Addressing these challenges effectively in the new Pharma environment will not be possible with the older analytics approaches. It is now clear that older linear based approaches have had their day. All leading teams have moved to non-linear based artificial intelligence approaches, such as machine learning. By utilizing these kinds of approaches, far more granularities in the insights will be possible as well as a stronger understanding of the synergistic impact of different components.
So many companies have silos between teams. It is so crucial to break down these silos and have increased cross-functional team collaboration.The strategy and tactics used must be far more precise, making maximum use of all resources. One way of achieving this is through artificial intelligence, and specifically machine learning analytics applications which are ideal for complex environments as faced by many specialty products (such as drugs for Oncology, Diabetes, HIV, Hep C, Multiple Sclerosis and Rheumatoid arthritis as well as drugs for Respiratory, Cardiovascular, and many others).
Machine learning can examine hundreds of millions of options and find the exact combination of what is needed to achieve the desired goal with guidance on how to achieve this. It can be used to identify exact pockets of opportunity in a vast array of segments. The other powerful thing that can be done using this is to identify the exact value proposition needed by segment, and what levers are needed to be pulled, to maximize value to each segment.
When you have fewer patients, fewer physicians and higher costs, you really have no margin for error if you want to succeed. Understanding the finer points of specialty drug marketing is important. Each condition, regimen, drug, indication, types of specialty HCPs – and their patients and families – have different needs and requirements, and these must be considered when planning any strategy and tactics. Machine learning is a valuable way to understand the complexities inherent in these markets.
Conclusion
Given Pharma markets used to be heavily clinically driven, and now they are more economically driven, using artificial intelligence – specifically machine learning – can really help marketers see the exact changes needed in their commercial strategy to address more audiences with a clear cost benefit value proposition as well as other value propositions as identified by segment.
For more information on artificial intelligence in Pharma analytics, and machine learning in Pharma analytics, please contact the author – Dr Andree Bates at Eularis: www.eularis.com.
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To learn more about how Eularis can help you find the best solutions to the challenges faced by healthcare teams, please drop us a note or email the author at abates@eularis.com.