Just attended a whole day training about Pharmaceutical sales data by Kosta Tzavaras, the renowned author of books like ‘Pharmaceutical Sales Data 101’ and ‘Patient Data 101’. The information was quite insightful and definitely helped me better understand how different sets of data available from different sources are analyzed to serve the Pharmaceutical sales force.
Just attended a whole day training about Pharmaceutical sales data by Kosta Tzavaras, the renowned author of books like ‘Pharmaceutical Sales Data 101’ and ‘Patient Data 101’. The information was quite insightful and definitely helped me better understand how different sets of data available from different sources are analyzed to serve the Pharmaceutical sales force.
• IMS DDD – Sales of drugs by the wholesalers to non-retail (hospitals, institutes, etc.) and retail (pharmacies. mail order, etc.). Retail data is available at the zip-code level whereas the non-retail sales data is available at an outlet/account level and is not the actual sales made through prescriptions. DDD data is the ‘sell in’ data and hence considered to be at an aggregated level.
More Read
• IMS Xponent – Actual sales made through prescriptions in the retail channel. Data is available at a prescription and prescriber level. Xponent data is the ‘sell out’ data and hence more granular.
• IMD Xponent PlanTrack – This set of data provides more details over the Xponent data by breaking it down by payer and plan. This data mainly helps in providing valuable insights into managed care markets.
The sales force and managed care teams can derive insights from this data and analyze to access market opportunities, plan sales strategies and help targeting, segmentation and compensation. But all this is done by analyzing the past sales/prescription data. This is still a ‘backward looking’ scenario or as they say ‘rear-view mirror analysis’ based on lagging indicators. Actions are taken based on historical data and hence one can only predict the future assuming the current trends. But what could be those leading indicators that will help with a ‘forward looking’ analysis? Can we look beyond the traditional Pharma sales data to be more proactive?
Leading indicators such as macro-economic and political trends; population dynamics and climatic changes definitely play a major role, but the ones that can be easily correlated to are those that other industries like retail are embracing in the web 2.0 age. Internet and social media has grown significantly in the past couple of years and are valuable sources of information that serve as leading indicators. Sentiment analysis through social media/networking sites for physicians like Sermo (sermo.com) and WedMD (webmd.com); for consumers/patients like Medpedia (medpedia.com), Patientslikeme.com and healia.com can be leveraged to make better decisions. Unstructured data from these and other sources like youtube, twitter, facebook and other online community forums can be analyzed side by side with the structured data provided by IMS/WKH to understand the consumers, competitor products and the market much better.
Though the traditional Pharma sales data has played a major role in driving the Pharma sales and marketing teams, we can now look beyond and embrace the Web 2.0 technologies to formulate better strategies for the future.
