Subscribe to Dr. Granville's Weekly Digest

Pfizer's Top 5 Lessons from Applied Analytics | Forbes

Image representing Pfizer as depicted in Crunc...

(Image via CrunchBase)

Global pharmaceutical giant Pfizer Inc. is transforming its use of analytics from a theoretical exercise into a real-time execution engine powered in part by tablet PCs and click-stream data  but primarily by the urgency of shifting business models for Lipitor and other products.

In a compelling Q&A interview with MIT Sloan Management Review, Pfizer VP of U.S. Commercial Operations David Kreutter describes how the imminent availability of generic versions of its blockbuster Lipitor product typify the company’s need to overhaul how it gathers information, manages it, and uses it to make rapid and market-based decisions.

“From a healthcare perspective, it’s absolutely great for patients to have a generic version available of Lipitor [a Pfizer cholesterol medication that reduces the risk of heart attack],” Kreutter tells MIT Sloan Management Review. “From a business challenge perspective, though, taking billions of dollars out of a corporation in one fell swoop is a little bit daunting.”

For anyone involved in speeding up operations, optimizing business processes, or figuring out how to generate maximum value out of business analytics, Kreutter offers some powerful insights into how rapidly changing customer demands and shifting market channels make it essential for companies to recast their information-management strategies to meet the needs of today’s tumultuous global marketplace.

While I urge you to read the entire piece, I’ve plucked out what I think are the five most-compelling points that Kreutter makes in the interview:

1) Balancing Precision with Fortune-Telling. “But I also think that some of that focus on precision is misguided. Because by definition, we’re trying to analyze the future, not the past. As I mentioned at the beginning of our conversation, analytics is not a descriptive exercise; it’s a predictive exercise. Therefore, by definition, there’s uncertainty: We don’t know everything about the future. Maybe some of our focus should be on helping the organization understand the bounds of uncertainty and the actions we can take within those bounds of uncertainty.”

2) Talent and Competitive Advantage. “The pharma industry in general isn’t state-of-the-art in the use of analytics, so when I think about talent and capabilities, I don’t look at what is Merck doing and how do I access that talent, I look at consumer packaged goods, financial services, telecom, and ask, how are they advancing their business? We haven’t even caught up to where they were five years ago. What are companies in those industries doing now? What talents have they accessed to drive that? How do I access that talent? The challenge is contextual knowledge.”

3) Early Detection of Emerging Patterns. “As we’ve evolved from a paper-based interaction model to a digital-interaction model and a multi-channel model, we’re getting a huge amount of information from our interactions with our customers. A lot of it is activity-based. When physicians visit our website, we know what they’re clicking on, we know what they’re clicking through to. We don’t have any greater data on how those clicks translate into prescription writing, but we’ve got more data from which to try to discern patterns, which we can use in a predictive way. That’s really what we’re trying to focus on now: can we detect patterns early-on, or at least much earlier than prescription writing, that will allow us to adapt more quickly to our customers’ needs as well as to the competitive environment?”

4) From “Intellectual Journey” to Operational Imperative.“Historically, I would describe our use of analytics at Pfizer as a kind of intellectual journey. People would have hypotheses or strategies that they would want to pursue through numbers. They would quantitatively analyze them, but for the most part, unless there was a glaring difference between the hypothesis and the analytics, people would pursue their strategies as long as they were compliant with our legal and regulatory requirements. That’s pretty much going away. Because we’re at a point where we can’t ignore any data telling us about the effectiveness of our business strategies. The stakes are just too high, and the resources to allocate aren’t the same as they were before.”

5) Strategic Intent Vs. Real-World Results. “If our strategy is to deliver certain messages in a certain order, we can see if the message was delivered that way. For example, if we know that a certain segment of doctors in South Florida have a heavy proportion of elderly patients, they will often want to hear about drug-drug interactions first (since their patients are on many medications). We can track if we executed against that strategy, and we can track if that strategy had the impact, the literal prescribing behavior, that we anticipated. It’s a huge level of insight into the basic operating model, and really helps us to figure out, if we don’t have the impact we hoped for, if our strategy was right but the execution was flawed, or if the strategy fundamentally needs to be rethought.”

Read full multi-page article by Bob Evans (from SAP) at  http://www.forbes.com/sites/sap/2011/09/12/pfizers-top-5-lessons-fr...

Views: 779

Comment

You need to be a member of AnalyticBridge to add comments!

Join AnalyticBridge

Comment by Lisa Kesselman Wells on September 14, 2011 at 11:49am

The author sounded familiar to me, Bob Evans. Sure enough, he is the same Bob Evans that writes a good all-purpose innovation-related blog which I read now and then! 

 

Thanks for posting this write-up about how Pfizer figured out what data was relevant (and what wasn't), as I hadn't seen it anywhere else.

© 2014   AnalyticBridge.com is a subsidiary and dedicated channel of Data Science Central LLC

Badges  |  Report an Issue  |  Terms of Service