No More Digital Tracking? Communication Challenges for the Pharma Industry in a Cookieless Ecosystem

We can’t talk about health industry communications in Latin America without understanding the complex landscape in the region; the political, economic, and social scene is diverse and ever-changing. The region has seen a mix of stable democracies and periods of political instability. With new governments already installed and elections to come, the uncertainty still reigns. Reforms in the public and private health system are taking place in different countries and increasing regulation to the Pharma Industry being discussed.

The digital transformation of the industry is imminent; many have already taken steps to optimize their internal processes. Additionally, the pandemic accelerated the shift to a more “virtual” approach to communicate and connect with healthcare professionals, partners, and consumers. According to a market-research study conducted by the Mexico Internet Association on how doctors use the Internet, “93% do research on new products and diagnostics, 86% to received information on new product launches and 57% use internet to have virtual sales representative meetings”.[1]

In addition to these changes, the industry is facing the wipeout of third-party cookies (Google cookies). This presents a formidable challenge for companies operating in the region, necessitating a thoughtful reassessment of advertising and communication approaches, where innovation and adaptation will play a critical role.

Third-party cookies are small pieces of data that are created and stored by websites other than the one a user is currently visiting. These cookies are generated by third-party domains, such as Google, and often for the purpose of tracking and collecting information about the user’s online behavior and preferences. Unlike first-party cookies, which are created by the website the user directly interacts with.

In the article “Inside the Google media team’s cookieless measurement tool kit”, they point out: “For decades, third-party cookies have served as a one-stop shop for marketers to meet their measurement needs. These identifiers have multiple uses, such as conversion measurement and attribution, while helping us communicate the value and impact of our work. But consumer and regulatory privacy shifts have compelled the industry to seek new ways of measuring marketing success that don’t rely on third-party cookies.”[2]

Precision in Targeting

Third-party cookies have long been the linchpin of precision targeting, enabling communication and marketing specialists to tailor messages to specific user segments. Balancing the need for personalized communication with privacy and regulatory compliances becomes increasingly intricate, and implementing a strategy to collect first party data will be the key to building effective communications.

Transparency regarding what personal data is being collected and how is going to be used will help build trust with consumers. Cisco 2022 Consumers Privacy Survey found that “76% of respondents said they would not buy from a company who they do not trust with their data. In addition, 81% of respondents agreed that the way an organization treats personal data is indicative of how it views and respects its customers.”[3]

Navigating strict regulatory frameworks

One of the distinctive challenges for the healthcare industry lies in the strict regulatory frameworks that govern its promotional practices. Balancing the need for direct and personalized communication with consumer privacy and regulatory compliance becomes more difficult, striking the right chord between reaching the target audience and adhering to regulations will require an innovative approach and the reimagining of current strategies.

Embracing contextual relevance

As the industry bids farewell to cookies, contextual relevance, based on the content of webpages or the context of user interactions, can offer a way forward. Collaborating with platforms that understand regional distinctions and languages can facilitate the delivery of meaningful, contextually driven messages that resonate with diverse audiences.

AI-driven marketing segmentation

AI is a transformative tool, revolutionizing the way companies understand and engage with their target audiences. Through sophisticated data analysis and machine learning algorithms, AI enables healthcare marketers to identify consumer behaviors, preferences, and demographics. AI can assist in segmenting the market based on factors such as patient or health professionals’ demographics, treatment adherence patterns, and even social media trends. Having timely access to this information and being able to update it is crucial to be able to make strategic decisions and adjust accordingly.[4]

The challenges posed by the elimination of third-party cookies are undeniably substantial, yet the healthcare industry has a history of innovation in the face of adversity. Navigating these uncharted waters requires a combination of technological adaptation, respect for regulations, and a commitment to consumer privacy. As stakeholders in health and well-being, industry leaders must collaboratively explore new roads, ensuring that the critical dialogue between the sector and its consumers and professionals endures, in an era defined by privacy, access to information and digital transformation. Only through new solutions and joint efforts can the industry continue to communicate effectively and responsibly in the absence of the familiar tracking tool provided by third party cookies.

[1] https://irp.cdn-website.com/81280eda/files/uploaded/Estudio%20sobre%20los%20Ha%CC%81bitos%20de%20los%20Me%CC%81dicos%20en%20Internet%20en%20Me%CC%81xico%20AIMX%202021%20versio%CC%81n%20pu%CC%81blica.pdf

[2] https://www.thinkwithgoogle.com/marketing-strategies/data-and-measurement/measuring-marketing-success/

[3] https://www.cisco.com/c/dam/en_us/about/doing_business/trust-center/docs/cisco-consumer-privacy-survey-2022.pdf?CCID=cc000160&DTID=esootr000875&OID=wprsc030156

[4] https://insights.axtria.com/blog/machine-learning-ml-driven-segmentation-and-targeting-of-physicians