Consumers are increasingly learning about potential drug therapies from advertising across the media channels they use regularly – whether it be broadcast or streaming TV, social media, display ads, or streaming radio. One survey found that 63% of patients learned about new treatments through pharmaceutical ads, but capturing and retaining consumer attention continues to be an uphill battle for pharmaceutical marketers, with many consumers experiencing advertising saturation and fatigue. Life sciences marketers are scrambling to find innovative ways to individualize customer interactions across multiple channels, whether that be digital or traditional direct-to-consumer (DTC) media.
AI and machine learning are emerging as powerful tools to facilitate smarter, more targeted marketing outreach. In fact, over 70% of brands agree that AI will fundamentally change personalization and marketing strategy overall. For life sciences marketers, advancements in AI and sophisticated data analytics tools offer the ability to deliver timely, more relevant marketing messages to consumers.
Leveraging localized real-world data to target consumers
Conventional pharmaceutical marketing to consumers has historically done well in educating broad patient audiences of the treatment options available to them, although these broader messages are often not as relevant as they have the potential to be. Taking on new strategies to personalize outreach, smart life sciences marketers are leveraging real-world data to reach the right patients through approaches that are more predictive and targeted in nature, yet still HIPAA compliant.
Examples of real-world data are healthcare insurance claims data, including pharmacy claims, and consumer attributes, which include demographics, attitudes and interests, and media preferences. By applying AI and machine learning tools to real-world data, while using the data in a privacy-safe and HIPAA-compliant manner, marketers can predict patients’ care needs and deliver relevant information to consumers when they are most likely to find it beneficial.
For example, patients with bipolar I and bipolar II disorders can be treated with a combination of mood stabilizers, antipsychotics, and antidepressants, along with psychotherapy and substance abuse treatment. Identifying the right treatment combination for a patient’s depressive symptoms can be complex, and patients may have to try several medications before finding the right approach, which can take a toll on their day-to-day lives. However, AI and machine learning tools can analyze real-world data for diagnostic tests, medical treatments, and prescriptions to identify bipolar I and bipolar II patients who may benefit from certain therapeutic approaches.
Marketers can then deliver more relevant brand communications to those audiences that explain the potential benefits of a pharmaceutical brand and educate them on the potential side effects. They can also leverage real-world data to understand the media preferences and attitudes of bipolar I and bipolar II patients. If those patients’ preferred media channels are social media, online news sources, and audio, marketers can prioritize communications to those channels, making it more likely patients will ask their provider about a pharmaceutical brand in a fully educated way, and potentially shorten the time it takes HCPs and patients to settle on the right treatment plan.
Finally, these DTC strategies are also dynamic, evolving in real time by integrating updated data from diverse sources. Machine learning continuously adapts to provide life sciences companies with the necessary resources to understand the evolving complexities of an individual’s patient journey.
Leveraging AI and machine learning to engage healthcare professionals
If a provider and patient are not completely aligned about the potential benefits of a certain drug therapy, or it’s not a brand that immediately comes to mind for the provider, it’s less likely that the provider will prescribe that drug. Life science marketers ideally want to synchronize their brand communications to both brand-eligible patients and their physician at the same time. That’s where leveraging real-world data through advanced machine learning models can engage healthcare professionals to act when it will most benefit their patient(s).
Real-world data is enabling life sciences marketers to align overall franchise marketing with single-brand marketing and synergize HCP and consumer communications. This ensures that companies are better equipped to drive increased awareness around treatment options to both patients and providers alike. For example, life sciences marketers leveraging AI tools can time media placements to reach both patients and their HCPs just before the prescribing opportunity takes place. In adopting this synchronized approach, AI not only amplifies exposure to necessary treatments, but also boosts the likelihood that a patient and their provider will be more aligned on their treatment options and will convert to that new therapy.
Using AI and predictive analytics to market directly to HCPs is also proving to enhance therapy adherence with timely, actionable information. For example, many patients with Medicare have historically faced coverage gaps due to the Medicare “donut hole,” where patients face significant spikes in out-of-pocket costs after a certain threshold is reached. This can prompt switching to a lower-cost generic, or even stopping their medication altogether.
Life sciences marketers can leverage AI and machine learning tools to predict when patients are facing these coverage gaps (data that is likely unknown to their provider) and proactively deliver relevant information about financial support programs to their treating physicians through their EHR during at-risk patients’ visits. Not only does this help patients maintain their current therapy, but it ialso ncreases the likelihood that the provider will continue prescribing that brand.
At a time when personalization is heavily swaying consumer expectations, pharmaceutical marketers must embrace machine learning and real-world data as essential resources, creating deeper and more meaningful engagements with patients in need. By leveraging these technologies across omnichannel marketing strategies, companies are not only enhancing engagements across channels but are also delivering personalized messages with a new level of precision and care to ultimately improve targeting and help patients transform their outcomes with new treatments. As life sciences continue to embrace the digital ecosystem, companies that place data-driven personalization top of mind will remain best positioned to drive outcome improvements for their consumers.
About Doug Besch
Doug Besch is the Chief Product and Chief Technology Officer at OptimizeRx. With nearly 20 years of experience in life sciences leadership, Doug demonstrates expertise in product strategy and innovation within the life sciences industry.
Prior to his current role, Doug served as Vice President of Market Access & Payer Solutions for Clarivate and Vice President of Payor Product & Innovation at Decision Resources Group. Besch was also co-founder and the Chief Product Officer for Rx Savings Solution, helping members and payers reduce drug costs through a combination of clinical technology, transparency, member engagement, and concierge support. His professional journey began as a pharmacist for the Walgreens Boots Alliance.
