Overview
- Social media marketing faces unique challenges in the pharmaceutical industry due to strict regulations.
- Generative AI is a hot topic, offering potential efficiencies while also creating new risks for accuracy and targeting.
- Key insights from Digital Pharma East include the need for omnichannel strategies and the promise of AI in creative development.
- AI-generated content can lead to issues like inaccuracies and misrepresentation, requiring careful monitoring.
- Companies should develop guidelines for AI usage while focusing on fact-checking to avoid disadvantages in the future.
Social media marketing can be a challenge in industries with strict regulations, such as healthcare or finance. However, you can still launch an effective digital marketing program to help drive toward business objectives (see our previous article, Establish a Social Media Presence in the Highly Regulated Pharmaceutical Industry).
These industries, like many others, have been impacted by generative AI, one of the biggest developments in 2023. While policies — both at a company and national level — continue to be discussed, what are the potential impacts AI may have on digital marketing in an already heavily regulated industry?
Spark Growth and Influential Executive CEO, Elissa Liu, joins us to shed light on this topic.
Jackie: Elissa, thanks for joining us for another interview. Last month, you went to Digital Pharma East. What were some of the key insights you took away from the event, particularly as it relates to digital marketing in the pharmaceutical industry?
Elissa: Thanks, Jackie. Digital Pharma East was a great learning experience and I had a lot of good discussions during my time there. I would say there are three big insights.
First, the continued importance of an omnichannel, patient-centric approach, though we heard that measurement — such as attribution and ROI — continues to be a challenge for most pharma marketers. There are more and better tools coming onto the market to support measurement challenges, and we’re excited to implement several solutions with our clients this coming year.
The second major insight would be that AI is currently a technology that presents a lot of promise and is already creating efficiencies, though it still needs a lot of strategic input and oversight.
Lastly, there are many data providers creating opportunities to target patients using clinical and contextual data based on a range of sources. These hyper-niche targeting methods provide a great opportunity for rare conditions, but the big question for more common conditions or widely used products is whether niche or open targeting will win.
We also heard one of the speakers mention that a senior contact at Meta indicated that a year or two from now, targeting will no longer exist. The algorithm will do all the work to get content in front of the right patients or customers. Our team plans to continue to test open versus niche targeting to determine the most cost-efficient methodology in terms of cost per conversion or cost per script.
J: That brings us nicely to the next theme I want to talk about. Generative AI has been one of the biggest topics across all industries. Was that the case at Digital Pharma East? What were some of the discussions around AI?
E: Yes, AI was the most discussed topic at DPE. This included both generative AI for creative and content generation, as well as machine learning algorithms that could enable the surfacing of campaign optimization opportunities.
There was an interesting session in which the group used generative AI tools like MidJourney and GPT-4 to brainstorm brand and messaging concepts, develop a name and logo, and core messaging for an AI chatbot that would serve patients with a particular condition. Our team did a similar exercise in the spring, and it was interesting to validate much of what we’ve seen and experienced in terms of the strengths and limitations of the tools for brand creative development.
DeepIntent launched a new AI-enabled campaign optimization tool called CoPilot, which efficiently identifies potential bids, audiences, placement, and creative optimization opportunities using machine learning. This will allow our media buyers to spend less time analyzing low-level data, and focus on executing the identified optimizations that they believe will be most impactful, as well as bigger picture insights and strategy.
J: The pharmaceutical industry is heavily regulated already in terms of what can and can’t be posted on social media. While genAI continues to be an unregulated platform (at least for the time being), we are seeing more companies creating guidelines or even restrictions on its usage, and governments around the world discussing the platform more closely. When it comes to social media regulations, do you expect the growth of genAI to have an impact there?
E: I think genAI creates some additional challenges in a highly regulated environment. Consider one of the biggest issues: AI hallucination. There will be more cases of ad or creative copy that may not be entirely accurate because they’ve been mistakenly attributed to a legitimate study or relevant data and statistics.
In addition, AI-generated images can sometimes create strange outcomes, such as a 6-fingered hand, an extra limb, or a body part displayed at an unnatural angle. These types of issues need to be monitored closely. AI models may also have been trained based on biased data, which may not accurately represent a fully diverse and representative audience. This could lead to misrepresentations or unintended implied claims.
J: Lastly, do you have any final thoughts on AI in digital marketing overall?
E: While businesses are in the process of developing company policies around the usage of AI, I don’t think focusing solely on the negatives of the platform and completely removing it from usage is an effective option. In late August, OpenAI announced “ChatGPT Enterprise,” which is aimed at businesses and is meant to be a more private and secure genAI platform. This illustrates the demand for these tools within a more professional setting.
Fact-checking remains a critical component when using any generative AI platform. GenAI still has a long way to go, but companies and marketers that stick their heads in the sand and totally ignore it may find themselves at a disadvantage in the future.
J: Thanks for joining us for another insightful interview, Elissa!
If you’d like to catch up on our preview interviews, visit our sister site, Influential Executive to read her perspectives on 2023 social media trends and ChatGPT trends for executive social media.
And if you’re looking for more guides on social media marketing in the pharmaceutical industry, check out our previous articles below:
- The Do’s and Don’ts For Pharma Brands on Social Media
- 3 Ways The Pandemic Changed Pharmaceutical Marketing
Here’s how we marketed a B2C pharma brand on social media!
Frequently Asked Questions
How is AI impacting digital marketing in the pharmaceutical industry?
AI is creating real efficiencies in pharmaceutical digital marketing while still requiring heavy strategic input and human oversight. Generative AI tools like MidJourney and GPT-4 are used to brainstorm brand concepts, messaging, names, and logos, and machine learning surfaces campaign optimization opportunities such as bids, audiences, and placements. AI was the most discussed topic at the Digital Pharma East conference, spanning both creative content generation and optimization algorithms.
What are the main risks of using generative AI for pharmaceutical marketing content?
The biggest risk of generative AI in pharmaceutical marketing is AI hallucination, where ad or creative copy may be inaccurate because it has been mistakenly attributed to a legitimate study or to relevant data and statistics. AI-generated images can also produce strange outcomes like a six-fingered hand, an extra limb, or body parts at unnatural angles. AI models trained on biased data may fail to represent a diverse audience, which can lead to misrepresentations or unintended implied claims, so all outputs need close monitoring in a heavily regulated environment.
Should pharmaceutical companies use generative AI or avoid it because of regulations?
Pharmaceutical companies should adopt generative AI with guidelines and oversight rather than removing it entirely, because focusing solely on the negatives is not an effective option. Fact-checking remains a critical component when using any generative AI platform, and companies that ignore the technology completely may find themselves at a disadvantage in the future. The demand for professional-grade tools is illustrated by OpenAI’s ChatGPT Enterprise, announced in late August 2023 as a more private and secure platform aimed at businesses.
Why is measurement and attribution still a challenge for pharmaceutical marketers?
Measurement, including attribution and ROI, continues to be a challenge for most pharmaceutical marketers despite an omnichannel, patient-centric approach being a top priority. More and better tools are coming onto the market to support these measurement challenges. Patient targeting is also evolving through data providers that use clinical and contextual data, which works well for rare conditions, while the open question for common conditions is whether niche or open targeting will win on cost per conversion or cost per script.
What AI tools were highlighted for pharmaceutical campaign optimization?
DeepIntent’s CoPilot was highlighted as a new AI-enabled campaign optimization tool that uses machine learning to identify potential bids, audiences, placements, and creative optimization opportunities. This lets media buyers spend less time analyzing low-level data and focus on executing high-impact optimizations and bigger-picture strategy. For creative work, generative AI tools like MidJourney and GPT-4 were used to develop brand and messaging concepts, names, and logos.