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Blog, Comment & Insight

For Pharmaceutical Marketing Analytics

New Approaches to Create a Fail-Proof Launch

The odds are not in your favor. According to a McKinsey & Company report, two-thirds of drug brands today fail to meet sales expectations for their first year on the market. Even companies with a strong heritage of success aren’t immune. You’ll see drugs from Sanofi and AstraZeneca among the many brands that have suffered the humility of a place on top 10 launch disaster lists.
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Why Your Customer Data Could Be Giving You Poorer Sales Results

Around 6% of annual revenue is being lost through poor quality data, according to new research released recently. All sales and marketing teams are attempting to provide their customers with the right content, in the right channel at the right time for that individual, in a compliant way. However, at the same time, regulations are getting tighter, and physician access is reducing. It is possible to achieve nirvana with big data and AI, but in order to do this, good data is critical.
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Reinventing Customer Segmentation - How AI Is Changing the Game

In recent years, our target audience changed from just doctors and patients to also include payers, pharmacists, physician assistants, nurse prescribers, patient associations and more. Despite these differences,  pharma marketers are still segmenting customers the same way they have for decades.
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How to enhance customer experience using artificial intelligence – without being creepy

In previous articles I have discussed various ways AI is being used to track all types of customer behavior and serve up the next best content or channel for that individual as well as patient identification in rare diseases. We do this with many projects in the pharma and health spaces. One thing that is critical in all these kinds of activities is a combination of utilize the data in compliant personal ways, while avoiding an invasive creepy feeling to the recipient. When a company or brand integrates AI into the foundation of its core data and processes, the information it is able to access will be much more valuable to both the company and the customer. But doing this then poses questions about what is done with that data, whether it is ethical, and how marketers can retain trust.
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Take Your Sales Targeting + Segmentation to the Next Level

In a recent conference panel discussion, all senior industry executives in the panel confessed that they felt their company and themselves lacked sophisticated physician targeting. It is critical to understand your customers as individuals and not treat everyone the same way as people have different needs. Customers are unique and should no longer be chosen solely based on their type of specialist or a level of prescribing. 
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Are you making these data visualization mistakes?

I was asked to write a small piece about marketing to millennials versus baby boomers. In writing that, one thing that is clearly a difference in these groups is not only channel bias (Millennials prefer instagram), but also content consumption. Instagram got me thinking about data visualization - but is any visualization better than none?
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Seven Reasons Many Big Pharma are Failing in their Big Data and AI Projects

The headlines today still tell stories of challenges facing pharma. “Why Eli Lilly Shares Lost 8% in November” The Motley Fool, December 3, 2016 “Sanofi Points to Lower Profit for 2017” Financial Times, February 8, 2017 “Struggling Teva eyes thousands of job cuts” FiercePharma, March 23, 2017 How can these headlines become weighted on the whole to be positive for pharma rather than negative? The answer lies in becoming data-driven - intelligently. It is well known that Teva have been working with IBM Watson to change their fortunes for some time, and yet in 2017 the results of that partnership do not appear to be changing their financial situation.
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Becoming a Data Driven Pharmaceutical Company

The numbers for becoming data-driven from a recent McKinsey Institute survey are compelling: 23 times more likely to acquire new customers, 19 times more likely to be more profitable, and 6 times more likely to retain customers. These kind of results are driving many in senior management in pharma to aim at becoming data driven as an organization.
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AI Applications in Pharma Today

Imagine you are in the year 2037 and anything you want to do with AI is possible. How would you use that to really redefine how you interact with your customers? What would be your first projects on your ‘to do’ list? Interestingly, when I ask my pharma clients that question, some of the things they come up with actually are already not only possible, but we are already doing them with clients today. Do you know the breadth of areas and things that can be done now? I thought this may be an interesting blog to get people thinking about their own projects and what is already possible. 
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How to Make Your Company Ready for Artificial Intelligence

Now that we are seeing many pharma executives appreciating what AI can do for them in their role within pharma, they are wanting to take advantage of AI. However, in many cases when they start, they then find many organizational roadblocks rear their head and hinder their efforts. Artificial Intelligence (machine learning, big data analysis, cognitive computing, deep learning, intelligent agents etc.) is essentially the ability of machines to utilize data and reasoning to achieve an objective. The amount of data that can be integrated is essentially infinite, and means that AI can churn through trillions of data points and analysis far more effectively and rapidly than a human can.
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