5 Steps Toward Pharmaceutical Analytics Nirvana

Has your company reached “analytics nirvana?”

Companies that have reached the pinnacle of the analytics maturity curve view analytics as mission critical and use to it create competitive advantage and differentiation. There is a palpable passion and energy surrounding data and analytics in the organization’s hallways. Data and business intelligence are pervasive throughout the organization and easily accessible to non-analytics managers. A wide variety of structured and unstructured data along with a smoothly functioning technology infrastructure are used to power work across every department, including marketing, sales, and clinical research. Perhaps, most importantly, data-driven decision making is routine and an embodiment of the company culture.

Pharmaceutical leaders at the top of the maturity curve effectively use data and analytics to create competitive advantage and differentiation when they:

  • POWER personalized and anticipatory experiences
  • CREATE predictive health intelligence
  • CONNECT the healthcare ecosystem with intelligent service systems
  • DEMOCRATIZE data throughout the organization
  • TRANSFORM data into easy to understand stories
  • GENERATE real-time insight for on-demand decisions

If you work at a pharma company that has yet to reach analytics nirvana, you’re not alone. In general, few pharmaceutical companies have reached this enviable stage.

Yet becoming truly data-driven remains a pressing issue for most pharmaceutical executives. There is a race to win — and for good reason. As pharma and healthcare companies compete in the new outcomes economy where even specialty drugs quickly become commoditized, in order to win, those that succeed also must compete on data and analytics. They recognize that a robust data and advanced analytics program is no longer optional — it’s critical to their commercial success. In fact, a recent study from the International Institute of Analytics, “Analytics Maturity Powers Company Performance,” proved that high analytics maturity is positively associated with superior market valuations, shareholder returns, financial performance, and company performance.

This study also showed that the benefits of moving up the analytics maturity curve are progressive with each stage of maturity achieved. That’s good news for pharmaceutical companies that haven’t reached analytics nirvana. Any step forward — regardless of where you are on the road to maturity — is certain to generate significant business value. Given the urgent challenges pharma companies are facing and the central role that data and analytics play in addressing them, it’s imperative that companies extract immediate value from their data and analytics programs now, while simultaneously accelerating their own journeys on the data and analytics maturity curve.

Here are five things you can do now to begin your forward momentum.

1. Find your starting point

Conduct a formal analytics maturity assessment evaluation to determine where you are. A well-established quantitative survey and benchmarking tool from unbiased sources such as TDWI or the Institute of International Analytics (IIA),combined with qualitative interviews can accurately pinpoint where you are and what your next steps should be. You may be pleasantly surprised by the results. Regardless of what your baseline is, chances are good that you will find pockets of innovation around the organization. Socialize those innovations and celebrate the innovators to silence the skeptics and generate enthusiasm for progressive advancements in your data and analytics capabilities.

2. Build a strong foundation

There are no shortcuts here. To make data-driven decisions, you need to retire the spreadsheet and build a sound foundation in data and business intelligence. If you are just getting started, you’ll need to build a data warehouse, connect critical data sources, and implement a modern BI tool.

A modern BI tool can increase revenue and profitability by enabling near real-time measurement and optimization as well as enabling rapid root cause analysis of exceptionally strong or weak performance. It also democratizes data throughout the organization, which not only informs decisions, but also standardizes how decisions are made and infuses a data-driven culture into the everyday workflow.

Innovation in BI tools has transitioned from enabling visual data discovery, which is now mainstream and mature, to smart data discovery that meets rapidly evolving data and analytical requirements. For example, streaming data from health sensors and unstructured health data is increasing the velocity and complexity of data. Deployment of AI applications, including machine learning and natural-language processing, is now mainstream. And in a data-centric company, non-analytics managers need easy access to the data.

These smart data BI tools include natural language processing and machine learning to improve data analysis. According to Gartner, natural language generation and AI will be a standard feature of 90% of modern BI platforms by 2020 and will deliver twice the business value. These new tools also include natural language query and natural language generation for text- and voice-based interaction, which will fuel the growth in citizen data scientists. Gartner predicts that by 2020, 50% of analytics queries will be generated using search, natural language processing or voice, or will be auto-generated.

Despite these advances, it’s important to focus on human-centricity and keep the human in the loop. These modern BI tools still require pharmaceutical and healthcare subject matter experts to understand the data, ask the right questions, and interpret the results correctly in order to achieve improved business outcomes.

One of the first things to do with your new smart data BI tool is to integrate performance measurement and optimization at the brand and portfolio level. Many brand teams measure performance for the channel or audience they are responsible for, such as a professional digital campaign or a DTC initiative. With your new tools, you’ll be able to easily view all of your cross-audience and cross-brand data in one place to create optimizations that quickly will generate a return on the investment in building your data and BI foundation.

3. Close the loop from data to action

As Thomas Edison once said, “The value of an idea lies in using it.” However, translating data to insights, insights to action, and action to measurable business results and outcomes are easier said than done. How can you close the loop?

  • Change the data conversation. Business leaders may make decisions on gut instinct, relying on experience and intuition instead of data or using data selectively to validate a decision already made. Both data and experience are important. If the data contradict your instinct- based decision, dig further until you have a solid, thorough data story, then let the facts guide your action. Changing an organizational culture to be data-driven can take time, but anyone can start to change the dialogue immediately.
  • Align your data and analytics strategy with marketing strategy.Analysts need to be embedded throughout the entire strategic and tactical planning workflow from start to finish. Too often they are brought in at the end of a project to measure performance, only to find out they did not have all the data inputs they need or are working against unclear learning objectives. To create the most value, analysts need the business and cultural context to create a clear and engaging story for the decision makers.

4. Find growth opportunities/challenges

Once the foundation is in place, look for pockets of opportunity to demonstrate the value of more advanced data and analytics solutions. The key is to start by identifying a growth opportunity to exploit or a business problem to solve. Companies sometimes become enamored with the latest technology tools or new data sources then search for a problem the latest technology solution can address. While unguided data mining can lead to unexpected findings, this approach rarely gives you the pace of consistent progression and business improvement you seek.

One of the big growth opportunities in pharmaceutical marketing today that can now be addressed by data and analytics is efficiently reaching and engaging increasingly smaller groups of patients and physicians. This is necessitated by the rise of personalized medicine and treatments for rare diseases. As specialty care products reach more than 50% of prescription sales, pharmaceutical companies need to be far more precise in how they identify their target audiences for advertising and sales programs than traditional methods allow. Two advanced data and analytics methodologies can address this opportunity.

Use predictive modeling to identify the persuadables. Using predictive modeling and machine learning, marketers can identify their “persuadable” patients and physicians among the small populations they are trying to reach. For example, predictive modeling can identify physicians who are most likely to increase prescribing as a result of an increase in sales and promotional spend. This targeting approach is far more accurate than decades-old decile analyses. Similarly, predictive modeling can also identify patients who are more likely to engage with and benefit from an adherence program, which eliminates waste from intervening with patients who will be adherent without any support or those who will never be compliant.

Predictive modeling also can identify the right time to reach your persuadable physicians and patient. For example, it’s now possible to find cancer patients when they are in the short 4–6 week window between diagnosis and treatment decision. Predictive modeling also can identify when a persuadable patient is likely to become non-adherent so that interventions can be communicated at the time they will be most valued and effective.

Target individual people, not personas. Once you’ve identified the persuadable physicians and patients, data and technology now allows modern marketers to target actual people — not broad demographics, channels, segments or personas. This makes your targeting even more precise and efficient. This form of media activation, called people-based marketing, is powered by an identify graph, a database that houses and connects all the known identifiers an individual has, such e-mail addresses, physical addresses, account user names, device IDs, and all of their cookies. The individual is then anonymized and assigned an ID.

With people-based marketing, you can select the actual anonymized people you want to target based on their profile and propensity to take a desired action, suppress the people you don’t want to target, such as patients who already registered for your CRM program, activate and track response at the person level, and refresh and optimize audiences throughout the campaign.

This enables marketers to connect a person’s online and offline behaviors across multiple platforms and devices, and provide a unified view of each person, so the persuadable patient or physician can be recognized and targeted with the right content across every screen and realize a unified, cohesive experience.

This approach is consistently proven to increase engagement, leads, and sales. As a result, this transformation is spreading quickly across the marketing community. In a recent study conducted by the IAB in conjunction with the DMA and Winterberry Group, called “The Data Centric Organization 2018”, only 9.8% of respondents described their organization as extremely data-centric with respect to use of audience data today, but more than 44% said they expect to achieve this level of sophistication over the next year, by 2019.

The second big opportunity is to transform clinical trial recruitment with people-based marketing to accelerate clinical trial recruitment and get medicines to market sooner. Finding the right patients for clinical trials takes too long using traditional methods and is inefficient in reaching the right patient population. That’s because traditional digital recruitment methods cast a wide net based off of profiles or personas, leading to wasted dollars and suboptimal reach and frequency among the right target. People-based targeting is much more precise in identifying and reaching the target population, which allows you to recruit more patients faster and at lower cost.

5. Test and learn quickly.

As you plan your roadmap up the maturity curve, don’t try to tackle too many initiatives at once. Instead, start small with a well-designed set of experiments and grow from there. Identify growth opportunities by aligning your analytics strategy with your business strategy. Then evaluate these opportunities based on revenue generation, cost reduction, customer impact, time, cost, ROI, and other factors important to your particular brand or category. The next step is to prioritize the opportunities by categorizing them into buckets: quick wins that you’ll want to implement right away; foundational opportunities that are critical to success, and mid-term opportunities that are aligned to your overall strategy, but are less urgent or deliver results over the longer term. Then you’ll want to initiate a rapid cycle of test and learn initiatives to quickly advance your knowledge, demonstrate value, and move forward.

The journey up the data and analytics maturity curve may initially seem like a daunting and overwhelming task, leading to lack of focus and paralysis. However, that’s no excuse not to start. Creating a road map and dividing the journey into bite-sized pilots will generate immediate business value now — regardless of what stage of the maturity curve you are at — while moving you forward to progressively greater levels of revenue growth and profitability.

Maryann Kuzel leads a team of health data scientists and customer experience strategists in the application of big data, analytics and artificial intelligence across the global Publicis Health network. She has led business units, commercial teams and marketing organizations ranging in size from 10 to 100+ people. She writes and speaks extensively on data-driven customer experience transformation.

Prior to Publicis Health, Maryann worked at Omnicom for 8 years, where she built new capabilities in analytics, big data, digital, and customer experience. She began her career at Procter & Gamble, followed by positions of increasing responsibility in classic brand management for prescription and OTC medications at Bristol Myers Squibb, Roche and Bayer.

Maryann graduated with distinction from the Stern School of Business at NYU with an MBA in International Business and Economics, and received a B.S. in Chemical Engineering and Business from Carnegie Mellon University.

What do you think?

This site uses Akismet to reduce spam. Learn how your comment data is processed.