Transforming life sciences with commercial analytics

Published: October 10, 2018

Every business player is cognizant of the value of analytics, 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. To drive performance, organizations must be able to discover new data sources, apply analytics and generate insights that can be quickly translated into action.

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, the research and development phase of every drug requires huge resources both in terms of time and money, and hence they need to start right.

While dealing with such investments, organizations must answer a few basic questions such as:

  • What is the risk of failure?
  • What is the ROI?
  • Will the drug pass all clinical phases?
  • How long will it take to develop the drug?

This list can be more exhaustive, and the questions can be more complex than those posed. Businesses need to work on answering them backed by facts, and this is exactly where commercial analytics is going to play a mammoth role.

Applications of commercial analytics

While implementing commercial analytics to drive customer engagement and identify cash cows, few areas must be focused upon. 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, companies can easily identify their future stars and matured cash cows.

Social media analytics

In a world where voicing opinions matters, organizations cannot turn a blind eye to social media. The sentiments of a population cohort can reveal the ground 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/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.

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Empowering commercial analytics with dark data

Unleashing the potential of unstructured data has become much easier in recent times, thanks to the advent of machine learning algorithms. Be it synthesizing or labeling data, ML algorithms have made life simple for the life sciences community. Know more about Dark Data Analytics

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 any more. Recently, a research conducted by students in Stanford Snorkel led to the development of a user-friendly tool for labeling unstructured data with minimum knowledge about the 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 the form of 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 companies compete in overlapping areas, but it is not possible to manually figure out the clinical trial development done across the world. To gather as much intelligence as possible, web crawlers run across science journals, research works, and social media to extract clinical notes with latest traction. Since the content contains 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 a market analysis in drug development offers valuable insights on various stages of drug development and competitor drugs across the world.

Almost every complex problem can be solved with the combination of dark data and commercial analytics, but organizations must continue to believe in its power to transform. As long as companies are invested in mining dark data, the future looks bright and promising.

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