AI Technologies and Pharma Marketing Chapter 1
Generated by Stable Diffusion prompt: Portrait of a robot in the Baroque style painted by Rembrandt
In recent years, artificial intelligence (AI) has emerged as what a lot of marketers call, “a game-changing technology across various industries.” Even within the pharma marketing industry, AI enables marketers to understand their target audiences better, develop personalized messaging, and optimize their marketing campaigns for better results. With trends leaning towards integrating a lot more AI and machine learning (ML) use in the marketing space, there’s been a lot of buzz about what this means for our jobs' future. Buckle up, because we’re going explore what AI is already doing in pharma marketing, get to know a bit about ChatGPT (dun dun dun…), and talk about what an agency’s strategy could look like when it comes to working with, not against, AI.
This stuff is dense, so I’ll be breaking this out into three chapters. Now let’s dive in!
Chapter 1
A snapshot of integrated AI technologies in Pharma marketing
AI has been a hot topic in the marketing world for a few years, and for good reason. These technologies have made it so that a wide variety of AI tools available for marketers can help with tasks such as analyzing data, optimizing campaigns, and personalizing content. For many of us, you have either worked on projects incorporating AI-powered tools or you’ve likely experienced them out in the wild yourself. Below are examples of AI tools for pharma marketing.
Chatbots
AI-powered chatbots can be used to provide 24/7 customer support, answer frequently asked questions, and help guide website visitors through the sales process. Novo Nordisk introduced Sophia, Novo’s first chatbot built specifically for people with diabetes. Sophia was born after Novo noticed spikes in online traffic to its Diabetes Education website between 11 p.m. and 1 a.m., reinforcing the need for information outside standard HCP hours. This is a great example of a need being met head-on by Novo.
Recommendation Models
Recommendation models use AI to analyze customer behavior and make personalized product recommendations based on their interests and past purchases. An article from NS Healthcare states that drug discovery has been in decline since the 1950s and, as such, can be a challenging and often futile process, where an estimated two-thirds of all clinical trials to find new medicines fail. That’s where Dr. Eliseo Papa comes in. Back in 2019, Dr. Papa was the AI engineering lead over at AstraZeneca, worked on innovative ways to optimize drug discovery. His team’s approach to this was an attempt to make the feedback cycle from trial to market shorter and allow for better decisions. They did this by organizing all the data, then working on a recommendation system, just like what we’ve all seen on Netflix.
This recommendation system uses three main ways of filtering data in order to find a suitable target drug for scientists to begin testing – collaborative, content-based and knowledge-based. Once these filters have been applied, the resulting data can be used to find medical information and papers on a specific disease that would have otherwise been too difficult or too expensive to link together.
Dr. Papa said scientists must embrace these uses of AI in the pharmaceutical industry because the amount of data they have to handle today is too large. (Source)
Predictive Analytics
Predictive analytics is the use of data, statistical algorithms, and machine learning techniques to identify the likelihood of future outcomes based on historical data. Let's say you're trying to predict if it will rain tomorrow. You might look at data from the past few days, like the temperature, humidity, and wind speed, and use that information to guess whether or not it will rain. That's basically what predictive analytics does but with a lot more data and a lot more complex calculations.
Predictive analytics can be used in a wide range of applications, from financial forecasting and risk management to customer relationship management and supply chain optimization. By analyzing large amounts of data, predictive analytics can help organizations identify trends and patterns that would be difficult to detect through traditional methods.
Below are some examples of how AI can be leveraged for predictive analytics.
Customer behavior prediction: AI can analyze customer data, such as purchase history, website activity, and social media interactions, to predict future customer behavior. This can help businesses identify potential churn, cross-selling and upselling opportunities, and tailor their marketing campaigns accordingly.
Fraud detection: AI can analyze transaction data to identify patterns and anomalies that may indicate fraudulent behavior. This can help businesses prevent fraudulent transactions before they occur.
Sales forecasting: AI can analyze historical sales data to identify patterns and make predictions about future sales trends. This can help businesses better understand demand and plan inventory and staffing accordingly.
Risk assessment: AI can analyze data to identify potential risks and predict the likelihood of future events. This can help businesses make informed decisions about risk management and mitigation.
Below is an excerpt from a case study provided by Odaia, a leader in life sciences predictive analytics and commercial insights. ODAIA's AI-powered platform, MAPTUAL, leverages a proprietary analytics engine to help pharma companies prospect, qualify, and engage HCPs, which helps reduce patients’ time to therapy.
“Near real-time data empowers the salesforce
By using MAPTUAL Field, representatives are able to hyper-personalize tactics and engagement channels for each customer. This results in more meaningful, data-driven conversations. Near real-time data aids the field force by showing them which channels have historically had the greatest impact. Representatives can engage HCPs by focusing efforts through these channels.
MAPTUAL Field’s predictive ability assigns each HCP a PowerScore from 1–10. This score, which updates in near real-time, indicates the propensity of that HCP to deliver on the brand’s objective. MAPTUAL Field also automatically identifies the “Next Best Channels” and “Next Best Audience,” allowing smarter customer prioritization. The intuitive design makes it easy to focus on the most impactful insights first. It does this via predictive segmentation tailored to high-level business objectives (as determined by the brand team during implementation). With this information, sales representatives can see at a glance which HCPs are Growers, Shrinkers, Rising Stars, and more.”
Key Message Optimization
Key message optimization is the process of refining and enhancing the central message of a communication, such as a marketing campaign, advertisement, or a piece of content, in order to increase its effectiveness and impact. Essentially, this means choosing the most important things to say in a way that is easy for people to understand and remember. AI can be a valuable tool in key message optimization by providing insights and data-driven recommendations to improve messaging strategies. Here are some ways AI can help:
Sentiment analysis: AI-powered sentiment analysis can evaluate the sentiment of online reviews, social media posts, and other forms of customer feedback. This can help brands understand the opinions and emotions associated with their messaging and make adjustments as needed.
Natural language processing: AI-powered natural language processing can analyze large volumes of text data, such as customer reviews, to identify common themes and sentiments. This can help brands identify key messages that resonate with their target audience and adjust their messaging accordingly.
Content creation: AI-powered content creation tools can generate high-quality content, such as blog posts or social media updates, that is optimized for specific target audiences. This can help brands create messages that are more likely to resonate with their audience.
A/B testing: AI-powered A/B testing can quickly and efficiently test different versions of key messages to identify which ones are most effective. This can help brands optimize their messaging strategy and improve overall performance.
When it comes to marketing in the pharmaceutical space, Precisionxtract has this covered with Access Genious, an AI-driven system that uses HCP-Specific data to lean into cost and coverage conversations, the types of conversation that drive actual prescriptions being written by HCPs. Access Genious filters real-time formulary, co-pay, prior authorization and plan restriction information against sophisticated data models to deliver the most statistically effective market access messaging to each HCP that marketers target.
Ad Optimization
Similar to key message optimization, AI can assist in ad optimization by analyzing large amounts of data to identify patterns and make predictions about which ads will be the most effective in reaching and engaging with a specific target audience.
When advertisers use AI for ad optimization, they typically start by feeding the AI system with data about their target audience, such as demographics, interests, and behavior. The AI system then uses this data to analyze past ad performance and identify patterns in what types of ads and messages have been most successful in engaging with that particular audience.
Based on these patterns, the AI system can then make predictions about which ads are likely to perform the best and recommend changes to improve ad targeting, messaging, and other factors that can impact ad performance. For example, AI can help advertisers determine the best time of day to show an ad, the best ad format (such as video, image, or text), and even the best colors and wording to use.
Here’s a few other ways AI can assist with ad optimization:
Personalized messaging: AI can analyze patient data to create personalized messages that are relevant to their health needs. For example, a pharma company can use AI to analyze patient data to determine the most effective message for someone with a specific health condition, such as diabetes or hypertension.
Targeted advertising: AI can analyze patient data to identify the target audience that is most likely to benefit from a particular drug. The pharma company can then use this information to create targeted advertising campaigns that are more likely to reach the right audience.
Optimal ad placement: AI can analyze data on ad placement to determine the most effective places to advertise a particular drug. For example, AI can analyze data on the websites or social media platforms that are most popular among a particular patient group and use this information to place ads where they are most likely to be seen.
Ad performance analysis: AI can analyze data on ad performance to determine which ads are the most effective in driving patient engagement and sales. This information can then be used to optimize future ad campaigns.
Personalization
AI-powered personalization means that a computer program uses data to learn about a person and then makes choices based on what it has learned to create a personalized experience for that person.
Think about when you watch videos on YouTube. Have you ever noticed that YouTube suggests more videos that you might like based on what you have watched before? This is an example of AI-powered personalization. YouTube uses data about what videos you have watched before to suggest more videos that you might be interested in.
Another example is when you shop online, and the website suggests other items that you might like based on what you have already put in your shopping cart. This is another way AI-powered personalization can work. The website uses data about your shopping history to suggest other products that you might be interested in buying.
AI-powered personalization can be used in pharma marketing to create more customized experiences for patients, healthcare providers, and other stakeholders. Here are some examples:
Personalized content: AI can analyze patient data to create personalized content that is tailored to the patient's specific health needs. For example, a pharma company can use AI to analyze patient data to determine the most effective message for someone with a specific health condition, such as diabetes or hypertension.
Customized email campaigns: AI can analyze patient data to create customized email campaigns that are more likely to resonate with the patient. For example, the AI system can determine the most effective subject line, email content, and call-to-action for each patient based on their health history and preferences.
Targeted ads: AI can analyze patient data to create targeted advertising campaigns that are more likely to reach the right audience. For example, the AI system can use data on patient demographics, health history, and other factors to create ads that are more likely to be relevant to the patient.
Predictive modeling: AI can use predictive modeling to anticipate patient needs and preferences based on their health history and other factors. For example, AI can analyze data on patient adherence to medications and use this information to predict which patients are most likely to benefit from a particular drug and then target those patients with personalized messaging and content.
Large Language Models
(ChatGPT, anyone?)
Large language models (LLMs) are computer programs that use artificial intelligence to generate text that sounds like it was written by a human. These models are trained on massive amounts of text data, such as books, articles, and websites, and use this data to learn the patterns and structures of human language. Once they learn how to write and talk like humans, they can do all sorts of things, like translating languages, helping chatbots talk to website users, and automatically generating news articles, product descriptions, or even entire books. These models are really good at understanding the patterns and structures of human language, which is why they can sound so much like us when they write and talk.
LLMs can be used in pharma marketing in a number of ways, but for the sake of time that I’ve already kept you here; here are just three examples.
Content generation: A pharma company could use an LLM to write blog posts about new drug research or patient success stories.
Chatbots: LLMs can be used to power chatbots that can provide customer support to patients and healthcare providers (similar to our first example of Sophia, the chatbot for diabetes patients over at NovoCare). The chatbots can answer questions, provide product information, and even schedule appointments.
Personalized content: LLMs can be used to create personalized content for patients and healthcare providers based on their health history and preferences. For example, the models can create patient education materials that are tailored to a patient's specific health condition.
Chapter 2 coming soon!