Decoding Digital Footprints (Part Two): A multi-faceted approach to market research, competitive intelligence and customer insights in healthcare
In this Q&A with our partner, Salem Lassoued, Founder of Data Organica, we discuss the transformative impact of digital behaviour analysis on the healthcare industry. As digital technologies permeate every aspect of healthcare, they leave behind vast amounts of data—digital footprints—that hold the key to understanding market dynamics and consumer behaviour more deeply than ever before.
Commercial Eyes partners with Data Organica to blend traditional market research and digital behaviour analysis to give a comprehensive view of the patient and healthcare professional (HCP).
Given the new and exciting nature of this combined approach, we look under the hood of the complexities of digital behaviour analysis and discover how it is being used to drive innovation, enhance patient engagement, and ultimately lead to more informed healthcare solutions.
What are some of the unexpected insights that digital behaviour analysis has revealed about the decision-making processes of healthcare professionals and patients?
Digital behaviour analysis has revealed several unexpected insights that challenge traditional assumptions about the decision-making processes of healthcare professionals (HCPs) and patients.
One of the most striking revelations is the disconnect between stated preferences and actual behaviours, which can drastically alter our understanding of their needs and how they interact with healthcare systems.
Traditionally, it was assumed that HCPs relied predominantly on peer-reviewed journals and clinical data for making prescribing decisions. However, our analysis has shown that HCPs also extensively use less formal sources, such as online forums, discussion groups, and even social media to gather information. This diverse sourcing is not merely supplementary but often pivotal in shaping their perspectives and decisions, particularly when dealing with rare disorders or new treatments.
In terms of patients, one surprising insight is the complexity of the pathways they follow before reaching a treatment decision. For instance, while it’s assumed that patients follow a linear journey from diagnosis to treatment, digital behaviour analysis often reveals a non-linear path involving multiple digital touchpoints. Patients frequently circle back to research symptoms or alternative treatments even after initial consultations, indicating that their confidence and understanding of their condition evolve dynamically rather than linearly.
Another unexpected insight is the significant influence of online patient communities and peer reviews, which can sometimes outweigh professional medical advice in the decision-making process. Patients in these communities share personal experiences and treatment outcomes that can sway others’ decisions about accepting or rejecting treatments, highlighting the need for healthcare providers to engage with and monitor these platforms.
Furthermore, digital behaviour analysis has uncovered the strong role of emotional triggers in both HCP and patient decisions. For HCPs, the emotional outcomes of past patient cases can influence decisions about future care plans more than statistical outcomes. For patients, emotional support from online communities often guides their treatment choices, sometimes even before professional consultations.
Lastly, an unexpected but critical insight is the impact of interface design on user engagement and decision-making. Simple changes in the layout and accessibility of information on medical websites have been shown to significantly influence both HCP and patient behaviours, such as the likelihood of exploring detailed drug information or following through with appointments.
These insights demonstrate the power of digital behaviour analysis not just in capturing real-time data, but in deepening our understanding of the complex, multifaceted nature of decision-making in healthcare.
What are the tools and technologies used in digital behaviour analysis for answering key intelligence questions?
We employ a suite of sophisticated methodologies that integrate diverse data sources and apply advanced analytics to distil actionable insights from complex digital interactions.
Our approach combines several strategic frameworks to understand and predict behaviours of healthcare professionals (HCPs) and patients effectively.
We utilise comprehensive systems that aggregate and harmonise data from various digital interactions. This includes data from online platforms where healthcare discussions and engagements occur, seamlessly integrating information across multiple touchpoints to provide a holistic view of user behaviours.
Our analysis leverages cutting-edge analytical techniques that include natural language processing and machine learning. These allow us to interpret vast amounts of unstructured data, identifying patterns and sentiments that inform the decision-making processes of HCPs and patients. This analysis goes beyond simple metrics to provide deep behavioural insights that guide strategic decision-making.
We apply predictive models to forecast future behaviours and trends. This is crucial in the healthcare sector where anticipating changes in treatment adoption or patient compliance can significantly impact health outcomes and market dynamics. Our models are refined continuously to adapt to new data, ensuring they remain relevant and accurate.
To capture and react to the rapidly changing dynamics in healthcare, our technologies process data in real time. This enables us to provide timely insights that are critical for making immediate and informed decisions in a fast-paced environment.
We transform complex datasets into intuitive visualisations that bring clarity to intricate patterns and trends. This helps stakeholders understand the implications of the data without needing to delve into the technical details, facilitating easier decision-making.
Our approach is deeply rooted in behavioural science, ensuring that the insights we generate are not just data-driven but also psychologically informed. This integration helps in understanding the underlying motivations and triggers that influence healthcare behaviours, providing a more comprehensive outlook on how HCPs and patients make decisions.
By leveraging these frameworks, we are able to uncover unique and often unexpected insights into the digital behaviours of HCPs and patients, which traditional methods might miss.
How does digital behaviour analysis help in identifying market trends and consumer preferences more effectively? Could you discuss a case where this analysis led to a pivot in marketing strategy or product development?
A prime example of how digital behaviour analysis can redefine patient support initiatives involves a biopharmaceutical company developing a new biologic treatment for Crohn’s Disease.
Digital Behaviour Insight
Our digital behaviour analysis delved into online communities where patients with Crohn’s Disease actively engage around their experiences. This analysis revealed a unique coping mechanism within the community: a dark and humorous tone frequently used to discuss their condition. This was a stark contrast to the typical patient persona and highlighted a significant cultural trait that resonated deeply within this group but was not widely recognised outside of it.
Strategic Pivot and Patient Advocate Integration
Armed with this insight, we advised the client to pivot their strategy for the patient support program. We recommended incorporating elements of humour and relatability that reflected the community’s communication style. Moreover, during our analysis, we identified a patient advocate who had a large following on social channels and epitomised the unique humour and resilience of the Crohn’s community. This individual became the face and central focus of the new patient support program.
Outcome: The revamped patient support program, featuring the patient advocate, was met with overwhelmingly positive feedback. Engagement rates soared, with significant increases in program sign-ups and active participation. The presence of the advocate not only helped make the information more accessible but also built a stronger emotional connection with the patients, fostering a sense of community and belonging.
Can you discuss a case study where digital behaviour analysis provided a significant competitive edge in the healthcare industry?
An exemplary case of how digital behaviour analysis provides a significant competitive edge in the healthcare industry can be seen in a campaign aimed at enhancing outreach to healthcare professionals and individuals dealing with Hepatitis C. This initiative strategically targeted regions with a higher prevalence of the disease, employing digital behaviour analysis at every step to ensure effectiveness.
Digital Behaviour Insight
Our campaign strategy was deeply informed by digital behaviour analysis, which integrated geographical and health prevalence data to precisely target the area’s most in need. This data-driven approach allowed us to understand not just where the disease was most prevalent, but also how people in these areas interacted with health information online. By analysing digital behaviours, such as search patterns and engagement with existing health resources, we tailored our messaging and media placements to align with local habits and preferences.
Strategic Pivot
Critical to our strategy was the use of behavioural heuristics, which were identified through our digital behaviour analysis. We tested various Calls to Action (CTAs) tailored to resonate based on regional insights, assessing their effectiveness in real time. This methodology provided a clear view of which messages performed best, leading to dynamic adjustments in our campaign to optimise engagement and impact.
Outcome: The campaign demonstrated outstanding success, marked by enhanced engagement rates and deeper penetration into targeted communities. The data-driven and behaviourally informed approach led to a significant uptick in interactions, with the tailored CTAs driving higher click-through rates and overall engagement than more traditional, non-targeted approaches.
Competitive Edge: This initiative not only elevated awareness and education about Hepatitis C but also set a new standard for how targeted, analytics-driven communication can be implemented in healthcare marketing. By basing each element of the campaign on robust digital behaviour analysis, we were able to craft an approach that was not only responsive to the data but also highly adaptive to the evolving dynamics of patient and professional engagement. This proved a potent strategy in cutting through the competitive noise and positioning our approach as a leader in effective healthcare communication.
What predictive models have you developed from digital behaviour analysis, and how have they been applied in real-world scenarios?
Let’s refer back to the previous example, the healthcare campaign we conducted, aimed at improving engagement with healthcare professionals (HCPs) in areas with high prevalence of Hepatitis C.
Model Development
We developed predictive algorithms that analysed behaviour patterns across digital platforms to identify which HCPs were most likely to engage with educational content about Hepatitis C. These algorithms took into account not just direct searches about Hepatitis C but also related healthcare topics, participation in online forums, and response patterns to past campaigns.
Implementation
By predicting engagement, we could tailor our communications much more effectively. For example, for HCPs predicted to be high engagers, we used more detailed, research-heavy content, while for those less likely to engage deeply, we simplified the information and increased the frequency and visibility of our messages.
Outcome: The use of these predictive models allowed us to significantly increase the effectiveness of the campaign. We saw higher engagement rates, more proactive inquiries from HCPs, and increased participation in our educational programs. This was not just about reaching more HCPs but reaching them in ways that resonated best with their current behaviours and likely interests.
This is another example of how integrating digital behaviour analysis with traditional methods offers us a richer, fuller picture of both the actual and aspirational aspects of consumer behaviour. By understanding both where people are and where they want to be, companies can craft strategies that are not only effective today but also evolve with their audience’s aspirations.
How can digital behaviour analysis enhance customer centricity, particularly in terms of product development, market access and marketing strategies? What metrics or KPIs do you recommend organisations track to measure the improvements in customer centricity derived from digital behaviour analysis?
The integration of digital behaviour analysis in healthcare is fundamentally changing how we define success. Instead of traditional metrics that might reflect the company’s perspective, we’re shifting towards “customer-in” metrics that truly gauge the value we deliver to patients and healthcare professionals (HCPs). This shift is crucial for enhancing customer centricity across product development, market access, and marketing strategies.
Traditionally, product success might have been measured by the number of units sold or prescriptions written. Now, with a customer-centric approach, we focus on how well a product meets the actual health needs of patients—how it improves their health outcomes or simplifies their healthcare routines. Digital behaviour analysis helps identify these needs and preferences more accurately, guiding development that aligns with real-world use and patient feedback.
Instead of merely looking at market penetration as a measure of success, we now evaluate how well our products reach the right segments. Are we effectively addressing accessibility issues? Are treatments reaching underserved populations? Digital behaviour analysis provides the insights needed to tailor our market access strategies to ensure that every patient who could benefit from our treatments has the opportunity to do so.
The effectiveness of marketing in healthcare shouldn’t just be evaluated by campaign reach or engagement metrics alone. Instead, we consider how these strategies increase patient knowledge, influence health literacy, and ultimately support better health management. By using digital behaviour analysis, we can craft messages that resonate more deeply and deliver them through the channels most frequented by our target audiences, ensuring that our marketing efforts contribute directly to improved patient outcomes.
Strategic Shift in Metrics:
The move from “company-out” to “customer-in” metrics involves a deeper alignment with the healthcare journey of each patient and the professional needs of healthcare providers. It’s about seeing beyond mere transactions or interactions to understanding and measuring the real impact of our interventions on health outcomes and experiences.
What ethical considerations should companies be aware of when considering digital behaviour analysis? How does Data Organica ensure compliance with global data protection regulations such as GDPR when conducting digital behaviour analysis?
When it comes to digital behaviour analysis, ethical considerations are paramount, especially in how we handle data privacy and comply with regulations like the GDPR. At Data Organica, our focus is on analysing group behaviours rather than individual actions, which shapes our approach to both ethics and compliance.
At Data Organica, we believe that ethical considerations are not just a legal requirement but a cornerstone of our business practice. By focusing on non-PII and group behaviours, and ensuring strict compliance with GDPR, we maintain the integrity of our research and uphold the trust placed in us by our clients and their customers. This responsible approach allows us to deliver valuable insights while safeguarding individual privacy and adhering to our ethical standards.
Focus on Groups: We specifically focus on patterns and trends among groups of people rather than individuals. This approach means we’re not dealing in personally identifiable information (PII), which significantly reduces privacy concerns. By analysing aggregated data, we can glean insights into behavioural trends without compromising individual privacy or dealing with adverse event reporting.
- Data Partners: We ensure that all our data partners are fully GDPR compliant. This compliance extends to how they collect, store, and handle data, ensuring that any data we use adheres to the highest standards of data protection.
- No Data Enrichment of First-Party Data: We do not engage in enriching first-party data with additional personal data points. Our work is focused exclusively on market insight research, which involves analysing existing data sets for broader trends and insights, not detailed personal profiles.
- Transparency and Consent: We maintain transparency with all stakeholders about our data practices. This includes clear communication on how data is collected, used, and protected. Additionally, we ensure that any data used in our analysis is subject to appropriate consent mechanisms, aligning with GDPR requirements and respecting user privacy.
- Security Measures: We implement rigorous data security measures to protect the data we handle. These include advanced encryption, secure data storage solutions, and regular audits to ensure compliance with all applicable data protection laws.
Employee Training: We regularly train our employees on GDPR compliance and ethical data handling practices. This ensures that everyone at Data Organica understands their responsibilities when it comes to protecting user privacy and handling data ethically.
We adhere to the principles of data minimisation and purpose limitation, ensuring that only the data necessary for a specific analysis is collected, and it is used solely for the intended research purposes. This not only complies with legal standards but also aligns with our ethical commitment to responsible data use.
How can startups and smaller companies in the Pharma, BioTech, and MedTech sectors use digital behaviour analysis to their advantage?
For startups and smaller companies in the Pharma, BioTech, and MedTech sectors, digital behaviour analysis can be a game-changer. It provides a level of market understanding and customer insight that typically only large companies could afford through traditional market research methods. Here’s how these smaller entities can leverage digital behaviour analysis to carve out competitive advantages:
Precision in Innovation: Startups can use digital behaviour analysis to identify specific unmet needs or pain points in their target markets. By understanding what patients and healthcare professionals are actively discussing and seeking online, these companies can develop products or solutions that directly address these needs. This offers a more strategic approach to product development, focusing on areas with a clear demand, thus maximising resources and enhancing product-market fit.
Identify Niche Markets: Digital behaviour analysis allows smaller companies to identify niche markets or subgroups within broader markets that may be underserved. By targeting these specific segments, startups can enter the market more effectively, avoiding the broader competitive landscape dominated by larger companies.
Personalised Marketing and Education: Understanding the digital behaviour of potential users helps startups tailor their marketing efforts to the preferences and behaviours of their target audience. This could mean personalising communication channels, timing, and content to increase engagement and conversion rates. Additionally, educational content can be customised to address the specific concerns and interests identified through behaviour analysis, enhancing relevance and effectiveness.
Rapid Response to Market Changes: Startups and smaller companies often have the advantage of agility compared to their larger counterparts. Digital behaviour analysis provides real-time or near-real-time data that can help these companies quickly adapt their strategies in response to new information about market trends, competitor moves, or regulatory changes.
Leverage Existing Data Sources: Unlike traditional research methods that can be cost-prohibitive, digital behaviour analysis can often leverage existing data from online sources, social media, forums, and other digital platforms. This makes it a cost-effective solution for startups that need to maximise every dollar spent.
Startups should focus on metrics that reflect their strategic objectives with digital behaviour analysis. These might include user engagement rates, conversion rates from digital campaigns, time to market for new products, and customer feedback scores. Tracking these metrics will help startups gauge the effectiveness of their strategies and make informed decisions about future investments in digital behaviour analysis tools.
In essence, digital behaviour analysis empowers startups and smaller companies in the Pharma, BioTech, and MedTech sectors to compete more effectively on a playing field often dominated by much larger entities. By providing deep insights into market and customer behaviours, it enables these companies to act with precision, agility, and a clear focus on customer-centric innovation.
What are the future directions for digital behaviour analysis in healthcare, and how should companies prepare to adopt these innovations? How do you see artificial intelligence and machine learning enhancing digital behaviour analysis in the next five years?
The potential of digital behaviour analysis in healthcare is truly boundless, especially as we look at the proliferation of digital devices and the exponential increase in data they generate. As more people around the world connect digitally, the depth and breadth of data available for analysis expand, giving us unprecedented insights into human behaviour in all its complexity.
Immediate Impact of AI in Digital Behaviour Analysis:
Expanding Data Analysis Capabilities:
The surge in global data isn’t just about volume; it’s about the variety and velocity of data we can analyse. We’re not just observing more behaviours; we’re understanding them in real-time, across diverse contexts. This vast data landscape is fertile ground for AI, particularly generative AI, to help us make sense of vast amounts of information quickly and accurately.
Current Applications of AI:
Even today, we’re not waiting five years to see the impact of AI; we’re living it. We use large language models (LLMs) to categorise and analyse billions of data points. At Data Organica, we’re developing our own LLM to enhance our capabilities further, ensuring that our digital behaviour analysis is as sharp and insightful as possible.
Broader Impacts on Healthcare:
The use of AI extends far beyond just data analysis. It’s revolutionising healthcare in areas like protein folding, reducing research and development timelines, and advancing material sciences. These applications show AI’s potential to speed up and enhance various stages of medical research and healthcare delivery.
AI in Patient Care:
AI’s impact is also direct in patient care. Innovations like Google’s AMIE are set to transform how doctors interact with patients, providing AI-driven support that can enhance care and improve patient outcomes. These AI avatars are not just tools; they are becoming partners in healthcare, offering new ways to interact and treat patients in a digital age.
How Companies Should Prepare:
- Embrace Generative AI: Companies need to invest in AI technology and talent now. The future is arriving faster than many might expect and staying ahead means being prepared to integrate AI deeply and broadly across operations.
- Focus on Scalable Solutions: As data volumes grow, so does the need for scalable AI solutions that can adapt and expand. Building infrastructure that can handle this growth is essential.
- Ethical Considerations: With great power comes great responsibility. As AI becomes a more integral part of healthcare, addressing ethical issues around privacy, consent, and bias becomes more critical. Companies must lead in developing AI solutions that are not only effective but also fair and ethical.
In summary, the integration of AI into digital behaviour analysis and healthcare is not a distant future—it’s a rapidly unfolding present. With the continued expansion of data and AI capabilities, companies like ours are not just participants but pioneers, shaping a future where healthcare is more efficient, effective, and connected.
As we move forward, our focus will remain on harnessing these technologies to deliver real-world benefits, transforming society and healthcare for the better.
Thanks to Salem Lassoued for contributing this instalment of the Innovation Series.
We hope this deep dive into the complexities of the healthcare innovation landscape has provided you with valuable insights and actionable strategies for navigating the challenges and opportunities in the Australian market.
At Commercial Eyes, we are committed to driving progress and success in the healthcare industry, which is why we partner with organisations like Data Organica.
Our team of experienced consultants are here to support you in achieving commercial success, from regulatory navigation and reimbursement strategies to market research and competitive intelligence.
If you’re looking to bring your healthcare innovation to market, or if you’re seeking expert guidance on any aspect of commercialisation, we invite you to reach out to us.
Together, we can turn your innovative ideas into reality, ensuring that your products reach the patients who need them most.
Jo Atkins
Head of Access Research and Intelligence
Jo is a registered pharmacist with post graduate qualifications in Health Economics and a Master of Management with a major in Marketing from the Macquarie Graduate School of Management...
View Profile