Every business player is cognizant of the value of analytics in life sciences, but it takes winners to tap into its commercial aspect.
Commercial analytics is all about running a business efficiently to convert insights into income and data into dollars. Organizations must discover new data sources, apply data analytics in life sciences, and generate insights that can be quickly translated into action and acknowledge the value of analytics in life sciences.
Commercial analytics plays a crucial role in the life sciences industry. The ROI cycle varies significantly for patients, physicians, providers, and pharma, and each stakeholder needs to identify its strength and analyze commercial outcomes before investing.
The cost incurred in the development of a single drug is close to $350 million per company. For pharmaceutical companies, every drug's research and development phase requires huge resources both in terms of time and money, and hence they need to start right.
While dealing with such investments, life sciences organizations must answer a few basic questions such as:
This list can be more exhaustive, and the questions can be more complex than those posed. Businesses need to answer them backed by facts, exactly where life sciences commercial analytics will play a mammoth role for real world application.
Applications of commercial analytics
While implementing commercial analytics to drive customer engagement and identify profitable business endeavors, a few areas require focus. These include:
Customer segmentation and targeting
For pharmaceutical companies, it is important to identify physicians whose prescription patterns suit their drug portfolio.
Every physician is bound to follow a pattern of prescriptions, but there needs to be right mapping to the physicians. Organizations need to determine the churn rate and physician lifetime for a given drug.
Patient data analysis
Data pertaining to drug efficacy can help pharmaceutical companies estimate the lifetime value of its product.
By analyzing terabytes of patient data and putting unstructured data into defined data sets, companies can easily identify their future stars and matured investments.
Social media analytics
In today's world, the life sciences industry cannot turn a blind eye to social media and the opinions that are shared on social media platforms.
The sentiments of a population can reveal the groundbreaking truths about a drug. Unbiased traction for a newly launched drug can be measured by analyzing social media, a direct medium for understanding customer voice.
Analysis of marketing channels
Every organization uses unique ways to market its products or services, and every marketing channel has its own pros and cons. In the age of fast-paced digital marketing, are the traditional channels keeping pace? Are they still relevant? If so, what is the cost to the business? Answering these questions enables investors to prepare channel-based go-to-market strategies.
Unleashing the potential of unstructured data has become much easier in recent times, thanks to the advent of machine learning algorithms. By synthesizing or labeling, machine learning algorithms have made life simple for the life sciences community. Learn more about Dark Data Analytics here.
Drug efficacy calculation by analyzing clinical notes
Each time a physician recommends a course of treatment to a patient, it involves a clinical note. However, labeling such a note is a challenging task.
A basic understanding of the way a clinical note is written will enable algorithms to learn heuristics and identify its true meaning. Once labels are created, dark data is not so dark anymore, even for large amounts of data.
Polypharmacy analysis with network graphs
It is easy to imagine a simple longitudinal table with ten rows and ten columns but add ten such tables to it, and it becomes difficult to figure out the 10X10X10 connections. A simple EMR of a county consists of millions of records, and each record can hold more than 500 features.
To analyze such complex data, networks need to be first plotted as graphs which results in the creation of dark data in unlabeled images. While it may appear redundant, the trick is to hold the metadata information beneath these connections. Images of graphs can then be rolled over with Convolutional Neural Networks to extract patterns from complex graphs.
Drug discovery with text analytics
Many health care and life sciences companies compete in overlapping areas, but it is impossible to manually figure out the clinical trial development done across the world. Web crawlers run across science journals, research works, and social media to extract clinical notes with the latest traction. Since the content contains a large amount of noise, algorithms are designed to classify noise from relevant information.
A series of text analytics algorithms and NLP solutions map clinical notes with disease conditions. Ranking all the mapped disease conditions to perform market analysis in drug development offers valuable insights into various drug development stages and competitor drugs worldwide.
Almost every complex problem can be solved with the real-time combination of dark data sets and commercial analytics, but organizations must continue to believe in its power to transform with analytics in life sciences. As long as companies are invested in mining dark data, the future looks bright and promising for life sciences commercial analytics.
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