9 Ways AI Can Help Pharma Marketers
9 practical ways that AI can help marketers operating in the pharmaceutical and life science sectors.
AI (Artificial intelligence) is a machine's ability to perform cognitive functions associated with the human mind. It can help with drug discovery and development (and repurposing), healthcare decision supporting, and a whole range of other areas within supply chain manufacturing and optimisation. From a marketing perspective, it provides better insights into customer behaviours, allowing pharmaceutical companies to better understand their audience and create targeted campaigns. It’s influencing the industry more and more as AI matures.
AI is made up of two types: Weak AI versus Strong AI. Weak AI is trained to perform specific tasks, usually mimicking how humans perform basic actions and drives most of what is around us today, such as virtual assistants. Strong AI is an artificial intelligence that aims to mimic the intelligence of the human brain.
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Despite what people may believe, AI has been around since the 1950s, starting with Alan Turing, who introduced a test for computer intelligence. Fast forward 70 years and the capabilities of AI are never ending. It can create content, offer healthcare advice, control machinery for manufacturing and even create audio and video content for pharmaceutical marketers.
Generative AI and ChatGPT
Generative AI allows users to input prompts to create new content, like text, image, videos, sounds. ChatGPT is a notable example of generative AI. It allows you to have human-like conversations and assist with tasks in the same way a human would. It has many functions, such as writing essays (as was its original function), correcting grammar, answering simple questions, creating content, and even coding.
When it comes to AI, and such tools, there are still many concerns. You need to fact check everything that it is telling you as they are not always 100% accurate even when it might sound plausible. With ChatGPT, although it has been trained on vast sets of data, it has a lack of real-world knowledge. It predicts how it can best answer your question based on the data it has been taught, so usually it takes a bit of back and forth to get exactly what you are looking for. With this blog for example, ChatGPT assisted me in the creation, but it did not write it for me. While using it I noticed it does not give you a detailed response until you have inputted lots of information. It took lots of back and forth to get what I was looking for and even then, it didn’t give me exactly what I wanted it to. This is the state of AI – possibly in a stage where it is about to come out of beta. So, when we are using it in an industry that helps with suffering and saves people's lives, we can’t leave it in the hands of a machine in beta mode.
AIContentfy explores how ChatGPT could also be used in the field of drug safety and pharmacovigilance, where it could handle large amounts of data from clinical trials, electronic health records and social media to identify potential safety issues and adverse effects of drugs in a timely manner. In some cases, it already helps with some of this. The ways in which AI can and will support pharma is ever developing…
In this post, however, we will specifically look at how it can help pharma marketers. But first, let’s look at the benefits and drawbacks of using AI for marketing.
Benefits
AI is available 24/7. Let’s use chatbots for instance. Customers can go onto your website at any time of day and receive answers to their questions without having to wait. It can provide support from answering common questions to offering health advice, and as customers don’t have to wait for a human to respond, it provides much better quality customer service. You can also be more personalised with customers as AI tools can predict behaviours resulting in more relevant and engaging content being displayed to your audience.
In the pharmaceutical and healthcare sectors, the amount of data that is available and needs analysis is vast. AI-powered tools, for example, can assist HCPs to analyse patient data, which can lead to better health outcomes for patients. Data analysis can be done much quicker, as large sets of data can take days to process and analyse, however, with the power of AI it can be done within a matter of hours. Probably less. AI can do many things quicker than humans can, for example, if you are a social media manager it can create captions for a LinkedIn post, allowing you to effectively prioritise and manage other aspects of your marketing campaign.
AI can help save pharmaceutical companies time and money. It can automate processes for you, as well as optimising your marketing strategies, therefore creating relevant content in a timely manner. AI can also enhance clinical trials, it can analyse patient data, identify potential participants, and predict outcomes. This improves the efficiency of the process.
Drawbacks
As we alluded to earlier, AI is not as accurate as you may first think, despite the fact it has been fed vast amounts of data; at times, it’s just predicting what it thinks you want to hear. It may sound plausible, but you still need to fact-check all the information it is giving you. This is especially important in the pharmaceutical industry as AI doesn’t understand regulations and current processes, making integration with what is there already difficult. As AI is built off information that is already out there, it lacks innovation capabilities and the ability to create any new solutions or ideas. This means the tool could be feeding the same information to everyone, therefore your content or solutions or outputs won’t be deemed as unique or something people haven’t seen before.
There is a lack of emotion from AI, obviously as it isn’t a human, so it lacks empathy. In healthcare, for example, empathy is important for understanding and addressing people’s concerns, which means it may struggle to provide the right support to patients. There are also ethical concerns of where data is stored and/or used for as data can be repurposed without the person’s knowledge.
9 Ways AI Can Help in Pharmaceutical Marketing
Now we’ve set the scene, let’s look at 9 practical ways that AI can help marketers operating in the pharmaceutical and life science sectors.
1. Google Analytics Reporting
One way GA4 uses AI is through predictive audiences, where the AI algorithms segment users based on their likelihood of showing specific behaviours. They are constructed using predictive metrics that have analysed user behaviour patterns to forecast future actions. This means pharmaceutical marketers can tailor their strategies more effectively to the correct audience segments.
You can also link predictive audiences to Google Ads to improve media performance. It can improve the capabilities of Google Ads’ similar audiences by identifying the top percentage of users who are most likely to make a purchase. Meaning you can target new users who have similar characteristics to that top percentage.
The Google Analytics insights panel can automatically identify opportunities from your data that would be more difficult to do manually. It helps identify unusual/notable changes in your data and informs you immediately. You can also create your own customer insights for things you want to focus on and that are more important to you, which will help you better understand your customer behaviours in order to make more informed decisions.
2. Enhanced Personalisation
Predictive personalisation has the ability to predict customer behaviour based on past interactions and patterns. It uses historical data like clicks, opens and engagement. This means customers can receive personalised recommendations to optimise their experience, improving overall satisfaction. AI analyses customer data to segment audiences correctly and more effectively, to ensure they receive engaging and relevant content. Personalisation has been used in digital marketing for the last 10 years or so, but AI is now making the process of learning about a user’s preferences easier, and quicker. This has a significant impact on the user's customer journey as you are actively tailoring messages towards them.
3. Analysing Data
Analysing data usually follows these steps. It starts with collecting data; deciding what it is you are wanting to learn and collect data from sources that are trusted. Then it goes onto data cleaning where you remove irrelevant information to ensure the data is as accurate and helpful as possible. This leads onto data analysis, in which you can begin to look for trends and patterns in the clean data. Lastly is data interpretation, this is once trends and patterns are available, meaning you can make informed decisions about the marketing approach.
Machine learning is used to process large amounts of data to identify patterns and use them to make future predictions. Machine learning learns based on the information it is fed, and the more information it is fed the better. Machine learning is best suited for vast amounts of data as it can provide more accurate results. Natural Learning Processing (NLP) gives computers the ability to interpret and generate human language. The algorithm reads text to interpret and analyse correct and regular responses. This can then generate new content in the same way humans would.
4. Predict Future Trends
AI algorithms can predict future trends, meaning pharmaceutical companies can use this information to optimise product launches and anticipate market demands. It can also monitor social media platforms to track pharmaceutical products and healthcare topics that your audience are engaging with. By doing this, companies can identify issues and discover opinions to engage with customers more effectively on the topics they are most interested in, meaning the chances of a two-way conversation is increased.
AI algorithms can also detect and categorise such topics, keywords and hashtags mentioned in social media conversations. By monitoring these discussions - in real-time - marketers can identify trending topics, monitor industry conversations and become relevant and up-to-date, in an industry that is ever changing.
5. Generate Content Ideas
AI is great for content creation. It can suggest content ideas, write the text for you, create enhanced visuals and graphics, suggest trends and keywords and hashtags, as well as schedule the publication of that content. In pharmaceutical marketing it can be difficult to constantly produce new and exciting ideas, however, AI tools such as ChatGPT can create topics tailored to your company that you can then build off and make your own.
Usually, the more specific you are the better, as it can automatically generate captions, which can include audience information from the tool analysing your input. AI tools can provide you with structures for articles. It can give you headings, talking points, and whatever area of content that is becoming the stumbling block. You can then tweak the structure until you are satisfied to ensure it fits with your company and matches your theme. This will speed up the process and allow you to focus on other tasks within your remit.
We mentioned ChatGPT earlier. From the conversation you have with ChatGPT, it gathers the information they know so far to learn patterns and give an accurate prediction of what you might be looking for. Ultimately, we would recommend that pharma marketers do not use ChatGPT for content creation (what it was originally created for) as it likely doesn’t possess the outright understanding of how the industry works, and its regulations. But it can be used in the research phase of a content task, providing that the content requires previously created and published content to do so.
6. AI Powered Social Listening
When creating content on social media it’s helpful to address the preferences of your audience to ensure such content becomes engaging. This requires you to look at conversations and trends on social media to see how your audience engages with it. AI tools can give you an insight into audience sentiment which allows you to produce content you are confident your audience will appreciate and want to engage with. It can also detect and categorise topics, keywords, and hashtags mentioned in social media conversations.
AI-powered social media monitoring tools can also detect potential PR crises or negative sentiment spikes in real-time. By alerting pharma marketers to these issues promptly, AI enables proactive crisis management and damage control strategies, minimising reputational damage. This of course requires internal resources and team process that can handle the information when it arrives – but doing it this way might mean that the insight arrives at your desk quicker than it normally would.
7. Creates Images and Visuals
Using AI to create images will give you an advantage as they are brand-new, never-seen-before images. They are high quality and not available in stock image libraries, meaning your posts will stand out to the audience as they are unique. Tools such as Midjourney can help you create unique images. Some of these tools can offer background removal, predesigned templates, text generator and image enhancements.
Our design team are experimenting with AI for imagery, and once again, the outputs are impressive but are still not always suitable for use within the pharmaceutical sectors. Of course, AI is being used to analyse medical imagery for diagnosis, and it can also be used for data visualisation, but limitations for marketing teams may be evident. Images can be enhanced using AI – where the quality of medical images by reducing noise, improving resolution, and enhancing contrast – but it would be challenging for AI to completely create an image that can represent a medical brand, that could also be approved by the various departments it would need to be to be approved.
8. Chatbots and Virtual Assistants
As previously stated regarding the benefits of AI, chatbots can prove powerful in the pharmaceutical industry. Chatbots are powered by NLP to provide the appropriate assistance without the need of a human. They can offer medical advice 24/7, making you more accessible and trustworthy. This will increase satisfaction and allow patients to build a stronger level of trust with you. OneRemission launched a chatbot with the aim to help cancer survivors, fighters, and supporters learn more about cancer and post-cancer health care, where users can speak to an online oncologist 24/7.
Chatbots have been around for some time, and are powered by Natural Language Processing (NLP) algorithms so they can provide appropriate assistance and responses without the need for a human. Predictive text, which is also powered by NLP and AI, anticipates what a user will say next. This is a real area in which marketers can adopt AI to not only improve their website user experience but also improve the marketers’ efficiency with day-to-day tasks.
9. Marketing Automation
Marketing automation refers to technology that manages marketing processes in order to effectively market products. It simplifies time-consuming responsibilities so you can save time. It can save lots of time (rather than doing it manually) and streamline marketing operations and has been discussed extensively on the Orientation Marketing blog.
AI can analyse vast amounts of customer data usually associated with marketing automation tools/CRMs and can segment audiences based on various criteria such as demographics, behaviours, preferences, and purchasing history. This segmentation allows marketers to tailor their messages and offerings to specific customer groups more effectively, often via email marketing. It can determine the best timing, frequency, subject lines, and content for each recipient, increasing open rates, click-through rates and conversions, and can really excel in such areas of digital marketing. If you are not using marketing automation, this is likely holding your efforts back. If you are using marketing automation, now is the time to introduce AI to automate tasks further.
Supporting (not replacing) Pharma Marketers
Artificial intelligence is prominent in the pharmaceutical marketing industry and is only becoming more common. It is extremely valuable to understand how you can implement it into your marketing strategy to improve efficiency, predict customer behaviour and improve customer experience. By adopting natural language processing and machine learning algorithms, marketers can gain a deeper understanding of their target audiences and reach them more effectively.
Despite this, you still need to remain aware of the risks that come with AI, and how not everything is completely accurate. Our philosophy on AI marketing in its current format is to use it to help, not to lead; for research, not for implementation.
For more on strategic marketing approaches in the pharmaceutical sectors, visit our section on strategy.
