Two Key Questions About Data Analytics in 2022

PM360 asked experts in data science and analytics what areas are still causing marketers the biggest headaches and where the largest improvements are being made to make their jobs easier. More specifically, we asked them:

  • What are still the biggest data pain points that life sciences companies/marketers are struggling with? How can companies work to overcome these issues?
  • What are the latest and biggest improvements in how data, analytics, and artificial intelligence/machine learning are making marketing more effective? What areas of marketing are most improved and what do marketers need to know to fully leverage these new opportunities/technologies?

Simon Andrews

The biggest pain points from both data management and an analytics perspective are in building and maintaining a true plug-and-play system including these modular components: integration, mastering, privacy, and analytics. Oftentimes, full-stack analytics are “built in the garage,” meaning they’re designed and used once, or built once and kept in operation by an army of staff. With all the data and technologies available today, organizations simply cannot afford a two-year, high-risk, data integration project.

The best system’s design ensures that everything operates continuously and persistently, truly embracing the rapid prototype model. To achieve this, designs should be robust, allowing components to be modified painlessly as needs change without tearing the whole thing down. The most important step is to build the analytics roadmap. First, you must identify which use cases you are trying to address, clearly identify dependencies between them, and then start at only the simplest case. Any change champion will tell you that basic function, clear results, and user engagement are required to make an analytics program successful. Additionally, this approach allows stakeholder feedback to be incorporated immediately, ensuring that incremental results appear rapidly.

Jennifer Salvucci

The biggest, most stubborn challenges marketers face are often not caused by limitations with the data itself. Poor adoption, organizational alignment, and low data fluency across the business are often to blame. For example, omnichannel execution requires field reps to fully utilize and engage analytic tools and data-driven behaviors to orchestrate Next Best Action. It challenges digital marketers and brand teams to think differently about how they use data to better understand and engage customers. Without field rep buy-in, embedded digital-first capabilities, and agile mindsets, the marketer will fall short in recouping the value of the investment.

To mitigate these risks, marketers should apply a people-centric approach when introducing new solutions, including:

  • Build the Case for Change: Communicate the “why” and value potential incorporating the needs and challenges of key stakeholders into the strategy.
  • Develop a Winning Plan: Engage leadership and stakeholders early (and throughout the process). Drive momentum around adoption by rewarding data-driven behaviors.
  • Communicate Effectively: Tailor communication and training for the intended recipient.
  • Be Agile: Work in sprints, celebrate quick wins, and adjust the plan along the way.

Gregory Gallo

The biggest data pain points our partners/clients face is that while there are analytic processes for integrating disparate data to provide insight on initiatives and programs, access to the data, timeliness of receiving said data, and vetting/agreement on analytic process all contribute to delays in receiving findings and then being able to act on them. Further, given that behavior change by patients, providers, and payers is measured in quarters/years, not days or weeks, even the best innovations are not optimized as rapidly as they should be. This diminishes patient outcomes and brand performance.

Another challenge: it’s hard to incorporate patient data when coming from outside of clinical settings. Companies can overcome this by partnering with all disparate parties as part of the project scoping process before initiating the work, rather than starting to gather data and decide on analysis at the end of a study period. If all groups know the importance of their role, what their contribution will consist of, and the general—if not specific analytic process and outcome measure targets—then the ability to evaluate innovative solution programs can be improved, sped up, and programs can be expanded or modified to accelerate the positive changes desired.

Read Roberts

The biggest opportunities created through advances in data analytics are the dramatic new efficiencies in marketing personalization and automation. Data processing technology now allows us to granularly segment audiences, enabling marketers to personalize messaging specific to customer categories, and then follow up with intelligent, automated communication. The result is content most helpful to customer needs and wants, without coming across as annoying.

Computer-based processing is at a point in which tremendous levels of data can be attributed to individual users and analyzed to segment targets into categories based on their interests and customer journey. With categories in place, a business can consider each segment’s decision-making process to develop a personalized marketing workflow for each group.

After segmentation has been established, “personalized workflows” can be put to use by scheduling communication through marketing automation software. Each segment can receive a cadenced flow of messaging related to their interests and behavior. These personalized workflows lead to loyal, repeat customers who refer friends and family. Based on a recent study by New Epsilon, an estimated 80% of customers are more likely to make a purchase when brands offer personalized experiences.

Chaitanya Badwe

Analytics and AI/ML-driven solutions are improving the decision making across product lifecycle, especially in marketing. With improved understanding of disease heterogeneity and advancements in precision medicines, finding relevant patients with a specific condition can be challenging especially when diagnosis codes are not available in data sets. AI/ML-driven solutions can help maximize the potential of available information and help marketers devise an effective go-to-market strategy with minimal investment.

A two-pronged strategy has proven to be quite effective, which starts with identifying relevant patients using AI/ML modeling. Such models leverage a broad range of inputs such as prescribed treatments, co-morbidities, sites of care visited, and physician specialties in the care team to predict relevant patients who might be eligible to receive a treatment. Secondly, identify the network of caregivers who are likely to be the decision makers for relevant patients and the timeline for key decisions. Such strategies are especially effective in the context of orphan and ultra-orphan diseases where identifying patients can be challenging.

Lastly, it is equally important to nail down messaging where advanced data science solutions can help identify distinct physician personas and map the influence network.

Amy Patel

There has been such a democratization of the availability of data analytics in helping drive marketing decisions, and this technology is becoming more of a core element of how marketers measure and make decisions about the effectiveness of their channels.

We continue to see more ways in which artificial intelligence and machine learning create efficiencies and allow marketers to access unstructured data and information. For example, we’re able to use AI and ML to look for trends in healthcare data that we weren’t able to do at scale before—particularly in the use of EHR data. And when dealing with large swaths of unstructured data, AI and ML can help to either mine that data for insights or pull it together to create a 360-degree picture of our consumers.

Previously, we didn’t have the tools that could undergo the challenging exercises of merging different disparate data sets together. And in the healthcare data space, especially, we now have a lot more opportunities to pull different pieces together and create that stronger picture or understanding of our consumers to get them the health content and care they need at the right points along their healthcare journey.