Three Analytics Priorities to Help Pharmaceutical Companies Break Through Healthcare’s Increasing Complexity

Dan Wetherill

“[T]here isn’t any more truth in the world than there was before the Internet or the printing press,” writes statistician Nate Silver in The Signal and the Noise. In other words, just because we have the technology to capture and access increasing amounts of data doesn’t mean that all of that data is useful. “Most of the data is just noise,” Silver writes, so sorting through all of that information is becoming more important than ever.

Similarly, just because we now have the technology to analyze that data and generate actionable insights doesn’t mean that those insights will be acted upon in a timely or impactful way. Those working with sales and marketing analytics know this all too well. Despite the promise of such technology, many companies are not yet embedding analytics into the fabric of the business and ultimately transforming the end customer’s experience.

With the help of the Economist Intelligence Unit, ZS recently studied the performance of companies’ analytics capabilities across a range of industries and found that companies’ results have been underwhelming. Among 448 respondents, 70% rate sales and marketing analytics as “very” or “extremely” important to their competitive advantage, but just 2% report that they’ve managed to generate a “broad, positive impact” from their analytics investments thus far.

Dan Wetherill, an associate principal at ZS who led the study’s analysis, explains the disconnects and discusses three priorities that can help pharmaceutical companies distinguish signal from noise.

Q: The study found that a lot of companies are working hard to realize the promise of big data analytics, but few are fully succeeding. Is that true in the pharmaceutical industry?  

A: Definitely. Companies recognize the need for analytics and they’re making investments, but most haven’t found a model that works in generating transformational ideas that go all the way to the customer. To be fair, it’s also true in most other industries. 

Q: What are the biggest problem areas?

A: One is simply that the healthcare environment is getting far more complex, so there are more variables for companies to capture and analyze. The changes in the industry over the past decade have had a huge impact on the way decisions get made and on the way organizations collaborate. Instead of a single decision maker, there are now multiple parties that collaborate and influence the way care is provided. The analytics has to adapt to reflect these broader dynamics, and to more accurately predict and shape the way companies go to market.

You also have things like the push to personalize medicine, where it’s becoming more important for companies to create a unique experience for customers. That requires companies to interface not just with doctors, but also with patients and other providers throughout the healthcare system, so there are more touch points to measure, analyze and improve, and all of that data creates pressure on analytics systems to process and make sense of it.

And thus far, many organizations have failed to integrate big data into their analytics processes. Big data tends to be an IT play. While IT is critical, the success of big data investments appears to be heavily tied to the ability to break down traditional silos and work across functions. Those functions have different mindsets, and they’ve traditionally operated independently, leaving many companies struggling to pull together all of the pieces to drive the impact that they want.


READ REPORT: Broken links: Why analytics investments have yet to pay off

The study highlighted problems at the front end of the analytics value chain, or how companies frame a specific problem or business issue. How does that show up among pharmaceutical companies?

A: Right now, there’s a lot of ad hoc work in analytics. A problem comes up, the company runs numbers and swoops in to fix it. It’s a fire-fighting model, and if you have a talented team, you can get by in the short term by fighting fires, but that doesn’t lead to real transformation because you didn't address the root cause of the problem. In some cases, you may not have truly understood the root cause of the problem. The next time it comes up, you have to fix it all over again.

Framing the problem the right way means you understand it­, using data, and can address it systematically. That frees up the company to think more strategically. Instead of fighting fires all of the time, you can look forward and develop transformative business ideas.

Q: What about the back end of the value chain, making sure that insights get applied all the way to the customer?

A: That’s clearly a problem area at many pharmaceutical companies, which tend to get caught up in the business-as-usual parts of the work but may find it difficult to more systemically drive impact and change. Imagine a brand manager working for someone who asks for an analysis—say, looking at the redemption rate of a co-pay card. If that analysis doesn’t lead to a specific decision or action—something that changes the way that the company interfaces with the customer or ultimately improves profitability—then it probably isn’t driving the kinds of impact that go above and beyond. You might have needed to do it to understand where you are, but you’re not really improving anything.

Q: So how can companies turn this around?

A: A core insight from the data is that it’s not how much companies invest in analytics, but how they invest it. That’s the silver lining of this story. The way to get more bang for your buck is by taking best practices and embedding them into your business processes so you can get ahead of the market, create a differentiated experience and enhance your relationship with customers.

Q: Specifically, what should they focus on?

A: I think that there are three priorities. The first thing is working in an integrated way across the organization by breaking down analytics and data silos. Increasingly, success means collaboration between the analytics leaders and the executives who run the business units. It’s two executives sitting down as peers and saying, “What is the real priority here, beyond business as usual, and how can we collaborate on that?” 

All too often, when a problem comes up, someone in the organization says: “I don’t think we can fix that. That’s outside of my control.” Industry leaders are going beyond that by redesigning the way they work. They’ll say, “If we can’t address that problem using our current process, let’s break the process down and rethink it.”

Second, while companies should nurture a mindset of experimentation, excessive experimentation with new, best-of-breed analytics tools can distract from real value creation. Right now, many companies are trying new things, and if it doesn’t work, they roll it back and move on, but there’s a cost to this process in terms of time, talent and treasure. The experimentation part has to be tempered with the need for consistent tools that analytics teams can be trained on and that effectively integrate across the data and analytics silos. There is such an explosion of new analytics technologies that it’s taking companies a lot of effort to stay on top of the trends and sort through it all. The key is to make sure that the benefits of the new tools are balanced against the costs of other factors like upgrading analytics talent.

Third, people sometimes get lost in the numbers, and they forget how to actually translate that into something meaningful for the business and for the customers. The third priority—and the most important—is to always focus on improving interactions with the customer. Any insights from analytics have to go all the way to the last mile and change the customer interaction in some way. Otherwise, you’re just running on a treadmill and not getting anywhere. 

For more analysis and insights from the study, read “Broken links: Why analytics investments have yet to pay off.

Download this Q&A.

About the Expert

Daniel Wetherill is an Associate Principal with ZS and is a thought leader within the firm’s Analytics Services group and oversees one of ZS’s analytics services delivery teams. He has more than 15 years of experience helping clients in the pharmaceutical and healthcare industries develop their marketing and sales analytics capabilities to drive cost efficiency and commercial effectiveness, and has led many engagements in areas such as management science, marketing analytics, sales analytics, forecasting, primary and secondary market research, and performance reporting. Daniel’s experience spans multiple industries, including healthcare, high tech, insurance, hospitality and transportation. Daniel is involved in directing research on analytics industry trends and has presented on analytics topics at similar industry conferences. He holds an M.B.A. from the Columbia Business School and bachelor’s degree from Princeton University.

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