The emergence of big data, technological advancements and data science approaches has dramatically transformed industries like retail, finance, and travel.

The Life Sciences industry is no exception.

Companies in the Life Sciences industry have a high-volume of unorganized data sets they aren't aware of that contains hidden actionable insights.

The life sciences industry hasn't fully adapted to new technologies but they've gotten off to a convincing start.

Still, a huge number of enterprises haven't been able to decode the insights because of lack of expertise in pharma data analytics and inefficient pharma master data management.

It's an undeniable fact that by leveraging big data and data analytics, pharmaceutical companies can enhance their business processes, accelerate their product development and achieve set sales objectives.

It'll be explained in this article how companies can take advantage of data analytics to boost their pharma sales in today's market. 

Pharma Data Analytics

Over the years, pharma companies have relied on the traditional ways of managing their pharma sales operations. In the past, many pharmaceutical companies would send their representatives to visit doctors personally.

This exhausted the resources of the companies and lacked accuracy and actionable insights. With the traditional way of sales promotion, broad-scale production wasn't possible.

Thus, the ROI generated was comparatively lower.

Today, the market is still at a fairly nascent stage and there are significant challenges. Such as, poor data integration, lack of investment in talent and technology, and limited stakeholder alignment impacting this industry.

However, best practices are emerging to help overcome these challenges and push pharma and life sciences analytics further on the path of rapid growth.

According to a report by the McKinsey Global Institute, applying big-data strategies to make better-informed decisions could generate up to $100 billion in value annually across the US health-care system.

The report also suggests that the following can change the face of the pharma industry: 

  • Optimizing innovation.
  • Improving the efficiency of research and clinical trials. 
  • Building new tools for physicians, consumers, insurers, and regulators.

Why data analytics

The ever-growing competitive drug market requires pharmaceutical companies to be nimbler to deliver patient-centric solutions that collaborate with payers, physicians, and patients.

The dynamic drug market is urging pharmaceutical companies to realign their sales approaches to remain agile.

In a survey conducted by Accenture, Life in the New Normal: The Customer Engagement Revolution the following was found:

  • 25% of their pharma marketing is delivered over a digital platform. 
  • 87% intend to increase their use of analytics to target spending and drive improved ROI.
  • 77% of sales executives said they are already using third-party service providers to augment their sales and marketing activities. 
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By leveraging pharma CRM and pharma analytics, pharmaceutical companies can meet their cost reduction goals, master multichannel marketing, and improve their sales delivery models.

Data is growing exponentially and will continue to grow over the foreseeable future.

Designing your commercial business strategy with the use of big data and advanced analytics is a necessity. Patients, prescribers, and payers are all more informed and have a variety of information available to them.

To drive a winning message to grow your brand and bring real value to patient’s lives, build a culture of data-driven decision making. Adapting digital transformation is the key to protect and grow your business.

However, many pharmaceutical companies are suspicious about investing significantly in improving big-data analytical capabilities. In part, because there are few examples of peers creating a lot of value from it.

Yet, investment and value creation in the big data era will take a significant portion of the company budget and it will grow in coming years.

Different data types like patient survey, pharma sales program, call center, sales visit and other prescribers’ data has a deep relation to the effectiveness of companies on-going sales programs.

In this scenario, pharma data analytics offers early opportunities for the companies to up-sell and cross-sell. This will generate greater profits.

These analytics help organizations in quick decision-making, and eliminate delays in implementation because of poor decision-making.

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Applying big data strategy in the decision making process could generate up to $100 billion in value across the US healthcare system. Doing so, by building new tools for physicians, regulators, insurers and consumers.

Also, by improving the efficiency of clinical trials, and the efficiency of research to meet the promise of a more personalized approach. This was estimated by the McKinsey Global Institute.

A wide range of pharma data analytics solutions are being deployed in the pharma sector to help companies identify new potentials in the industry.

This happens, while predictive and prescriptive pharma analytics act as a powerful tool that gives insight to the potential business.

Reporting:

It has become imperative for pharmaceutical companies to deploy reporting analytics as it focuses on building data repositories. These enhance the operational efficiency of a company and assists in better understanding the sales cycles.

Reporting also helps in regulatory compliance with respect to the Sunshine Act. An example of reporting analysis is adverse event reporting.

Predictive analytics:

Predictive analysis helps pharmaceutical companies understand the behavioral pattern of payers and prescribers. By leveraging technology-driven analysis, informed decisions can be made. Thus, reducing risks and chances of product failure.

Examples of this include revenue forecasting based on health outcomes and customer lifetime value analysis.

Prescriptive analytics:

With the help of predictive analytics, companies get an early advantage. They have actionable insights that will help them achieve their specific business goals.

Under prescriptive analytics, action items will be required. Items that are based on the insights of predictive analytics.

For example, marketing strategy planning can be done based on the insights derived from revenue forecasting.

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The way in which sales representatives harness high-volume data requires having the right data at the right time for them to use in the right manner. The use of pharma analytics solutions gives an early response to physicians and sales reps of real-time insights.

Therefore, it helps them customize their offering for different physicians based on the analytics. This leads to greater conversions.

Having data that's consistent, reliable, and linked are big challenges for the life sciences companies. Implementing end-to-end data integration requires a number of capabilities.

Such as, trusted sources of data and documents, the ability to establish cross-linkages, quality assurance, workflow management, and role-based access to ensure that specific data elements are visible only to those who are authorized to see it.

Most of the companies in the pharma industry perceive that unless there's a great value for money, there's little value to investing in improving big-data analytical capabilities. 

Millions of gigabyts of datasets are created each day, that makes the above statement nothing but a myth. 

Also, companies are afraid that they can be in the front row of adopting the change which is synced with adequate investments.

Those companies can witness other businesses who've seen a major growth in business with the implementation of big data in their organization.

Want to know more about how you can boost your pharma sales operations? Get in touch today to book your FREE hour of consulting to understand how it can help your company.

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