Why analytics investments have yet to pay off in pharma
With Dan Wetherill, Associate Principal, ZS
Companies have overwhelmingly recognized the importance of sales and marketing analytics as a means to understand customers, engage them intelligently, and identify new markets and growth opportunities. But while investments in data analytics are on the rise, many in the pharma industry are finding that their initial investments will take time to realize their full promise.
ZS partnered with The Economist Intelligence Unit on a study with 110 U.S-based pharma senior executives. The study results draw on survey findings, interviews with senior corporate executives and desk research to explore the current state of sales and marketing on the analytics maturity curve.
The study found that most business and analytics leaders (69 percent) view sales and marketing analytics as “very” or “extremely” important to gain competitive advantage. Most (85 percent) report that their organizations have a “specific plan” to upgrade their capabilities in the near future or are already investing in doing so, but say they have not yet achieved broad, positive impact across their organizations thus far. Clearly, this transformation remains a work in progress.
Why have analytics investments yet to pay off? The study found three “broken links” to realize the potential of data analytics investments. Pharmaceutical firms will need to focus on three sets of priorities to upgrade their analytics: their analytics processes, platforms and their organization.
ANALYTICS PROCESSES—REPAIRING BROKEN LINKS IN THE ANALYTICS VALUE CHAIN
The first priority is to improve analytics processes. A key finding from our research is that analytics execution, itself, usually isn’t the main problem. The real issues are in how organizations connect the parts of the analytics system, both at the front end (where companies define the problem and design the solution approach) and the back end (where they translate results into meaningful actions that change the customer experience in some way). In fact, nearly half of analytics and business leaders define the front end (problem definition: 50 percent) and back end (action/change management: 45 percent) as the best opportunities to improve the analytics value chain in the next 12 months.
For example, consider a company facing an unexpected decline in sales for a mature product. To understand what’s really going on, the business leader needs to first figure out where the losses are the most dramatic. It is a business problem, but it needs to be translated into analytics requirements. Essentially, the problem needs to be framed the right way so that the analytics team can probe for answers and generate solutions based on its findings. After the analysis is completed, the insights must be translated into meaningful, data-driven actions that close the gap in performance.
Moreover, once the company develops an analytics-based solution, that solution needs to be contextualized into meaningful actions that change something about the “last mile”: the actual experience that the company delivers to its customers. This is where many companies fall short. Even if they have a strong in-house analytics function that can generate clear insights about the market, they need to apply those insights to improve the customer experience.
BIG DATA ANALYTICS PLATFORMS—CONNECTING DATA AND ANALYTICS CAPABILITIES
The second priority is analytics platforms. Even though most of the companies in our research study are investing in both analytics functions and cloud-based big data infrastructure, only 8 percent have linked the two. That is an astonishingly low number. Part of the challenge is that implementing big data infrastructure is a major project that takes years to complete. During that time, the company’s priorities can shift dramatically, new data sets can emerge and the overall healthcare environment can evolve.
To more effectively link big data infrastructure to the analytics function—in a world of rapidly-changing data—companies need to create more flexible data infrastructures that can respond with greater agility. As the healthcare ecosystem continues to shift, new data sources are emerging, making it harder for companies to predict how to leverage these sources to best analyze the new industry structure. As a result, companies need to plan for this with the one element that they can control: the structure of their big data platform.
ORGANIZATIONAL ENABLEMENTCOLLABORATION ACROSS BUSINESS AND ANALYTICS LEADERS
Currently too many firms have advanced analytics or data science teams that operate in silos—almost as a back-office function—and thus develop answers and insights that are out of sync with the company’s real problems and realistic solutions. When that happens and business leaders get analytics results that don’t really help them, they lose faith and revert to making decisions the old way—based on gut instinct.
To solve this at an organizational level, companies need to break down those silos and foster more direct collaboration between the data scientists (who understand the numbers) and business line executives (who understand what really happens with the end customer). Those are different functions—they have different skills, strengths and objectives—and their leaders often speak different languages. Success will come when analytics and business leaders meet as peers, identify a specific problem and work together to solve it.
To see what improvements in these three elements—processes, platforms and organization—can lead to in the real world, consider an example from the hospitality industry. Through its customer research, Starwood Hotels found that negative experiences had twice the impact of positive experiences in shaping overall guest loyalty. The company redesigned its loyalty program to links social media posts and online reviews from customers to its end-of-stay surveys, and uses analytics algorithms to flag areas that require the company to follow up. For example, if someone posts a negative tweet about his stay, the staff at that hotel gets an alert with any relevant information about the customer so that they can fix it before that person checks out. Within a year of adopting analytics across the company, Starwood doubled incremental revenue from targeted guests.
The good news for pharmaceutical companies is that this kind of improvement does not require massive investments. Instead, it requires working smart. That means making necessary changes to processes to frame business problems in analytics terms, design solutions, and translate them into actions that improve the customer experience in some meaningful way. Working smart requires linking big data infrastructure and analytics platforms in ways that allow firms to stay on top of changes in the industry and emerging data sets. Lastly, it requires that organizations put data scientists and business leaders together to collaboratively solve problems.
For pharmaceutical companies seeking to build up their analytics capabilities, these steps can help them capitalize on the potential impact of the technology and start using it to improve their performance today.
To provide an example for one sector of the industry, drawing from the results of the report and from his significant experience, here are some insights from my colleague at ZS, Ganesh Vedarajan, about where medtech needs to go.
Provided that companies can overcome a bit of organizational inertia, they will start making investments that pay off over time. The value is there, and it’s only going to grow for companies that start building the foundation. In the future, it will be impossible to survive in medtech without a strong analytics capability.
Dan Wetherill Associate Principal ZS Dan is a leader in global sales and marketing firm ZS’s analytics services group. 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.
Ganesh Vedarajan Managing Principal ZS Ganesh Vedarajan is a managing principal on ZS’s executive team and the leader of the firm’s global medical products and services practice. Ganesh advises clients across a range of issues, including brand marketing, marketing execution effectiveness, managed care marketing and contracting, sales strategy and commercial analytics and operations.
WHY ANALYTICS SHOULD BE A CORE BUSINESS CAPABILITY IN MEDTECH
Ganesh Vedarajan, ZS Managing Principal
Data-driven decision-making is fast becoming the norm for most industries, in an effort to drive efficiencies, reduce costs, assess and predict customer behavior, improve customer service, increase personalization and accurately plan for the future. For medical device manufacturers, that analytics-led evolution now has yet another impetus, thanks to the Affordable Care Act’s charge to accelerate hospitals’ focus on improving patient outcomes. Medtech firms need data-driven insights to better position themselves as hospitals’ partners in that effort—and to sell and cross-sell more effectively via compellingly bundled product and service offerings.
There are three main barriers that these companies need to overcome. The first is that the value of analytics has to become clear in the organization, and proving that ROI to senior management is a challenge. There’s still resistance and skepticism among some executives that analytics can create value. They continue to try to operate in a world where sales reps have a lot of power, and think that they can win through one-to-one selling.
The second barrier is related to change management and embedding analytics systematically into the existing business model, in areas such as customer profiling and targeting, sales force design and marketing effectiveness. Once leadership buys in, the rest of the company must buy in, as well. There is a lot of inertia to continue with the status quo. We must be clever in how we embed analytics into the business model and force analytics into the existing processes, involving stakeholders as early in the process as possible to garner their buy-in.
The third barrier is to build a world-class analytics capability, one that achieves scale efficiencies and allows companies to use the latest data sources available. The good news is that many medtech companies have already made some good investments in technology, so they’re in a better place to add to the IT stack than they were just a few years ago. Back then, they didn’t have a good understanding of many customer-facing activities because they didn’t have a solid CRM system. Now they do. Some companies have been frustrated that these investments haven’t paid off, but using analytics to improve customer intelligence is one way to increase the ROI.