The Cutting Edge of Data Science: imre’s Analytics Guru Discusses New Trends and Technologies

In the rapidly evolving landscape of data and analytics, staying ahead of the curve is crucial for businesses looking to leverage insights and drive growth. To shed light on the latest innovations and developments in this dynamic field, we sat down with Maryann Kuzel , Executive Vice President of Data & Analytics at imre. With a wealth of experience and a keen eye for emerging trends, Maryann offers valuable perspectives on how cutting-edge technologies and methodologies are shaping the future of data-driven decision making.

What are the most important innovations you’ve seen this year in the analytics space?

Without question, it is the proliferation of new products that make accessing Generative Artificial Intelligence (Gen AI) in analytics applications much more feasible and easier. Just six months ago, the internet chatter about Gen AI applications focused largely on prompt engineering and office productivity. Now there is a plethora of new products and embedded applications for analytics and omnichannel campaigns. Some of the most exciting applications include:

• Predictive dynamic segmentation: Gen AI algorithms working alongside machine learning that can be used to analyze vast amounts of customer data to identify new patterns and predict future customer behavior and trends with unmatched precision. This can help businesses create more accurate HCP and patient segments compared to classic segmentation from market research alone. It can also find actionable micro-segments in the data based on customer profiles and behaviors.?When these algorithms are embedded in a MarTech operating platform, they can automatically create new segments based on engagement data, customer attributes, and prescription data to keep content relevant.
• Personalization at scale in a highly regulated environment: It’s no secret that Gen AI with machine learning has the potential to achieve nirvana in omnichannel marketing – marketing to a segment of one. But in pharmaceutical marketing, the Medical-Legal-Regulatory creative review requirements have created challenges to this level of personalization. Some companies have begun using Gen AI to highlight messaging that has already been approved versus totally new content. Some companies even provide “similarity” or “risk” scores to help identify content that may require a more detailed review versus changes in visuals or headlines that make content more relevant to subgroups. As Gen AI continues to advance, further innovations will continue to enhance this process making review of content variations faster, but always keeping the human in the loop.
• Advanced Business Intelligence: Inclusion of Gen AI into analytics dashboards makes it possible to generate deeper, more relevant insights, and reduce time to action. Real-time updates have been feasible for many years and are now standard best practice. What’s new is the power of Gen AI to automatically detect anomalies in the data with a high degree of accuracy, and to do it in near real time. Alerts can then be automatically sent to decision makers who can take immediate action for optimization.

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How do you see analytics innovation evolving in the coming years?

Gen AI is still in its infancy and its accuracy and applications will continue to grow at an exponential pace, reducing time to deeper insights. This will create relevant personalized experiences at scale and save time by automating repetitive tasks so analytics professionals can spend more time on creative problem solving and critical thinking. Gen AI will not replace machine learning as each tends to solve different types of problems, and in many cases can be used together beneficially. Broader availability and adoption of no-code/low-code solutions will significantly accelerate model development time, bringing solutions to market much faster and at lower cost.

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How will cross-functional collaboration between the analytics team and other departments shift in the future?

Cross-functional collaboration between analytics and other departments is critical to turn insights created by Gen AI and machine learning into action.

Too often, data and insights exist in organizational siloes. When the marketing decision makers do see the data, they may not understand or trust it, or they use it inappropriately as a judgement of what went right or wrong rather than as guidance to continuously improve performance.

Until organizations build a strong data culture and embrace widespread adoption of data sharing and data literacy, the ultimate business value derived from Gen AI and machine learning will be limited. A “data culture” may sound amorphous, but when you see it, you can immediately recognize it. For example, in companies with a strong data culture, data and business intelligence are shared across departments, and are easily understandable to non-analytics managers and decision makers. Teams are working with the same set of facts and insights when collaborating. Decisions are based primarily on data-driven insights rather than gut instincts.

Unfortunately, despite a clear rallying cry for the last several years, not nearly enough progress has been made in advancing data literacy so a true data culture can exist. And while building a data culture is not easy and can take time, there are a few steps every company can take to get started:

1. Ensure your analysts are strong data storytellers so that information and insights are presented in an easily understandable way that makes the optimal business decisions readily apparent. Strong data storytelling is a learned skill that requires both art and science.
2. Create an internal education program and tie the learning to a current marketing campaign initiative. Remember, one training course is not enough. It’s important to reinforce that learning by ongoing communication of tips, insights, and successful use cases.
3. Designate organizational champions from each department who disseminate use cases, identify successes and roadblocks, and work together to create solutions for continued success.

And lastly, companies should create a formal change management program. This requires proactive, visible and ongoing championship from executive leaders, and establishment of a measurement and optimization plan to monitor and improve success.

How does analytics provide a competitive advantage in our industry?

It’s been proven in many studies that high analytics maturity is positively associated with superior shareholder returns, financial performance, and company performance. Personalization done right is proven to generate high levels of customer satisfaction and a strong ROI.

But let’s talk about the benefits at a more granular level. More than half (28 of 55, or 51%)?of the novel drugs approved in 2023 were approved to prevent, diagnose or treat a rare disease or condition . The impact of these rare diseases affect each patient in a very personal way. HCPs struggle with diagnosis as they may see less than one patient a year, resulting in a lengthy diagnosis process of four to five years on average . Referrals to academic centers of excellence may require patients to take a long trip or have an overnight stay.

Patients need education, knowledge, and support that is personalized to their situation, such as journey stage, comorbidities, overall health status, access to care, presence of caregiver support, and impact from other social determinants. Each HCP needs education and resources right at the moment they are assessing a patient for a rare disease and making treatment decisions. That information needs to be timely and relevant to that HCP’s specialty, diagnostic/treatment role, expertise, practice setting, and patient population characteristics.

Data analytics powered by Gen AI and machine learning make this level of deep audience intelligence, precise just-in-time targeting, and personalized content and support, feasible. That’s a big win for patients and HCPs, and a huge competitive advantage for marketers.