Sales Analytics & Life Science
Life science sales analytics balance the trade-offs between cost and value. At the same time, such tools improve customer experience and optimize patients’ outcomes (Burke, 2013). Predictive analytics use digital tools to analyze huge datasets in order to predict clinical outcomes for individual patients, improve diagnoses, minimize hospitalization, and reduce drug prices. Interestingly, predictions in medicine employ statistical models, known as learning models, and artificial intelligence, which use a prediction algorithm based on past information. Consequently, a projection which can be applied to different niches can be created (Winters-Miner, 2014).
In a world where competition and advertising are inevitable, mastering the art of selling is challenging. Although life sciences and health technology are sensitive areas of work, just like with any industry, sales analytics play a crucial role in medical success. What’s more, due to the high impact of digital solutions and tech advancements, healthcare has gone through an essential shift in perspectives: patients are the leading participants in medical decision-making. As a result, life science companies, which provide drugs and treatments, need to involve patients in the sales process. In fact, any progressive life science sales analytics must bring patients, practitioners, sponsors, and leaders together. The transformations life sciences are undergoing demand new models and decision-making capabilities (Burke, 2013). Thus, companies need more than an ambitious sales representative to promote their products. For instance, more and more companies have started to reduce their sales teams and implement sales analytics methods. It’s not a secret that instead of trying to market numerous products, manufacturers now are trying to “do more with less,” cut the products they sell and expand their network.
But how can companies know which product to promote and where to invest? How can researchers know which product to test? How can patients know which product to choose? The answer can be found in life science sales analytics
Life Science Sales Analytics: Taxonomy
Life science and research are undergoing a tremendous change. Not surprisingly, technology has become a significant factor to consider and patients active participants in medical decision-making. As a result, the life science sales analytics and methods are also changing. Sales analytics are the key to success with two aspects to consider: the crucial role of personalized medicine and the abundance of medical data (Davenport & McNeill, 2013).
Companies in the field of life science and medicine need to embrace the changes that today’s digital era offers. Previously used only as a time-consuming method to report and track past effectiveness, sales analytics play a paramount role in today’s business marketing environment. It’s a fact that now life science companies rely on data and analytics, which is supported by digital solutions. Fragmented in the past, now data is highly integrated. Sales teams do not have to search through hospital data records, claims or physician notes anymore; they can simply access integrated databases and servers. As a result, sales teams can analyze a vast variety of data: social data, voice logs and even data generated by wearable devices. Thus, from online tools to big datasets, sales analytics can shape the future of life science and medicine.
Life Science Sales Analytics: Factors to Consider
decision-making. Tailored approach and individual feedback are two areas which benefit from technological solutions. As a result, existing treatments and drugs aim to help a particular patient, not a generic sample or the population in general. Thus, clinical and health outcomes analytics must ensure that patients are the focus of research. Such tools should combine comparative effectiveness, patient compliance, safety, and signal detection.
Research and development analytics: These methods are also crucial as they are related to the research and the development of new drugs. It’s not a secret that the market is very competitive. In fact, finding the right treatment (clinically and financially) is a long process, which involves clinical trials, digital recruiting, and errors. Any effective sales analytics in this area must combine supply optimization, enrollment, simulation, and targeted therapeutics.
Commercialization analytics: Commercialization analytics in the field of life science do not differ from any other field. They aim to maximize sales and customer relationships. Salesforce optimization, marketing strategy, portfolio optimization, social network analysis, customer lifetime value and drug repurposing are all vital aspects to consider.
Finance and fraud analytics: This is an essential aspect which is needed not only to ensure the financial stability of the operations but to protect patients’ data. Since healthcare and research are delicate fields, patients’ safety, confidentiality, and personal space must be protected. From fraud detection to claims excellence – finance and fraud analytics should be implemented by any medical body.
Business operations analytics: These tools are related to vital areas, such as driving productivity, profitability, and compliance across an institution and its business departments. Any company is like a living organism which can’t exist segmented: departments and managers should communicate and collaborate together. Staff utilization, call center management, and bending the cost curve are only a few of the fundamental steps to success.
Patients analytics: Last but not the least, sales analytics must benefit patients. Patients are not passive participants in research anymore. Thus, patients and their families must have access to clear data in order to decide which provider, drug or treatment to choose.
Life Science Sales Analytics: A Step-by-step Guide
Since life science sales analytics represents today’s changing field of healthcare, successful business methods should embrace technology, tailored approaches, and marketing resources (Davenport & McNeill, 2013). Some of the challenging steps to develop a successful sales analytics plans are fundamental to progress:
Gain knowledge: Knowing the industry through experience is the only way for companies to gain skills and insights, which can help them become leaders in consulting and masters in revenue.
Data access: Information is the main tool for success. Data access and transparency can foster interoperability and improve communication. Multiple sources can help data access. IT support is needed along with other media (print, radio, etc.). Of course, direct-to-customer approach and advertising are also vital in gaining customers and monetizing investments.
Consider predictive analytics: The whole concept of sales analytics has changed. As described above, sales analytics in the field of life science is similar to any other business environment. From reports based on past sales, sales analytics have evolved to a fundamental indicator of effective marketing strategies. As a matter of fact, predictive analytics are among the sales analytics approaches that companies need to succeed.
Embrace complexity: There are many sources of information and statistical tools. However, commercial life sciences must consider government and ethical regulations. This makes life science sales analytics more complex. For instance, in physician targeting, new predictive analytics often aim to identify low-level prescribers with the potential to grow instead of top-decile physicians to increase revenue.
Visualize data: Access to data is not enough. Having good visualized information is what companies need to get a clearer picture of their sales, customers, products, and the organization as a unit. In fact, by visualizing data, experts can monitor pipeline. Graphic snapshots of the sales prospects and process, which is the sales pipeline, can give valuable insights and define roles and quotas. This, on the other hand, will help employees track their work and bring teams together.
Manage performance: Talking about teams and organization, life science sales analytics involve a whole system of active participants: sales representatives, IT specialists, sponsors, practitioners, and patients. Thus, managing performance is vital. The improvement of end-to-end services can optimize performance. Factors, such as customer lifecycle and lead generation, should be a focus of analysis. Of course, productive commercial back offices are vital. Most of all, good management practices involve not only expertise but enthusiasm.
Go-to-market strategy: Sales analytics can be utilized to improve go-to-go strategy. The tactics how any organization will put offerings, present services, and products, and reach revenue are the main goal of success. Note that, as explained above, direct selling has been slowly replaced by multichannel models and different types of promotion. Not surprisingly, digital services are on the rise. Let’s not forget about the power of social media. Media channels may improve customer experience, increase planning strategies and benefit multichannel marketing strategies. As a result, companies can deliver business value and revenue.
Outsourcing: Outsourcing practices can not only cut costs, but help experts gain knowledge about different medical and sales practices across the globe (including strategy launch, growth strategy, and service effectiveness). Outsourcing opportunities can promote global consistency and local collaboration at the same time. This can foster expansion to new markets and dynamic pricing.
Sales analytics is paramount in today’s healthcare ecosystem and landscape. Transformations are inevitable. Leading life science companies have realized that the main aim is to cover solutions that deliver positive patient outcomes. Thus, companies, payers, and patients must work together to gain the most from life science sales analytics.
Burke, J. (2013). Health Analytics: Gaining the Insights to Transform Health Care. Wiley.
Davenport, T., & McNeill, D. (2013). Analytics in Healthcare and the Life Sciences: Strategies, Implementation Methods, and Best Practices. Pearson.
Steiner, D. (2016, June 8). How Businesses Use Data Analytics to Improve Sales. Retrieved from https://www.salesforce.com/blog/2016/06/businesses-use-data-analytics-improve-sales.html
Winters-Miner, L. (2014, October 6). Seven ways predictive analytics can improve healthcare: Medical predictive analytics have the potential to revolutionize healthcare around the world. Elsevier.