How to Get Pharma Leaders to Wrap Their Minds—and Their Arms—Around Advanced Analytics

Posted by Shreyas Murthi on Mon, Mar 11, 2019

 

The business of pharma is becoming more data-driven—from R&D to commercial—yet many pharma companies still struggle to garner leaders’ buy-in to build and operate the analytics programs that they need to fuel their success. They face tough challenges, including overcoming skepticism, proving the program’s value and scalability, finding the right talent and ensuring organizational collaboration.    


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Pharma’s Commercial Resource Redesign: Managing Timelines and Expectations

Posted by Pratap Khedkar on Wed, Dec 05, 2018

This is the final post in a three-part series on commercial resource planning.

As pharma companies begin to think about reorganizing and deploying their commercial resources to better align with changing customers and an evolving healthcare landscape, their strategy for transforming commercial resource planning can’t be piecemeal, but their approach to implementing the change likely should be. It’s going to take time to develop the right customized approach to suit your company’s specific needs and challenges, and to get buy-in. Data and technology will carry some of the weight, as will coordinating internal processes and roles, and adopting an on-demand, customer-specific mindset.


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From BI to AI: Getting Started With Advanced Analytics

Posted by Niroop Singh on Thu, Nov 01, 2018

Pharmaceutical companies are making big investments in analytics ecosystems, but not without some disappointment in terms of ROI. Because investments are typically limited to people or technology in isolation, companies can’t deliver the kind of value that makes such programs worthwhile. At the same time, cost pressures mean that analytics and data management groups have to deliver more with less. Executives who sponsor such programs also need to be very clear on how to define the success of such initiatives. Creating hundreds of new reports does not equal success. To give your analysts the advanced tools that they need to truly succeed, you need the right combination of people, data processes and technology to get the most out of your advanced analytics investment.

What should my team look like? What kind of processes do I need to support an advanced analytics capability? What kind of technology? These are great questions to ask at the outset, and here are some answers:


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System Change: The Path to Customer Centricity

Posted by Pete Mehr on Wed, Aug 08, 2018

This post is the third in a three-part series on how pharma companies can achieve customer centricity.

In our second blog post, we highlighted the difference between point change—where a change program is focused on a single, new capability—and system change, where the change program is focused on redesigning a process of how several capabilities work together. We see companies purchasing new capabilities—cloud-based marketing automation tools, digital asset management tools, data science capabilities, etc.—but introducing these capabilities via point change. This reinforces the siloed structure that most companies have today and limits the value of these new capabilities. The better approach is to recognize that these tools and capabilities are designed to be integrated, and to redesign the marketing process to be more automated and integrated across functions. In other words, use the tools and capabilities to break the silos by deploying them via system change.


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Having Customer-Centric Capabilities Doesn’t Mean That You’re a Customer-Centric Organization

Posted by Pete Mehr on Mon, Jul 02, 2018

This post is the second in a three-part series on how pharma companies can achieve customer centricity.

In my previous blog post, we spoke about pharmaceutical companies moving from a tactic-based marketing process to an orchestrated marketing process. The motivation for making this change is to optimize the customer experience. To achieve this goal, pharmaceutical companies have started to leverage cloud-based computing capabilities, purchase marketing automation platforms, enhance their analytic capabilities using data science and machine learning, use social media platforms, and hire digital experts from other industries, just to name a few of the changes. Yet companies still struggle to realize the value from these investments. 


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