16 Examples of Successful Applications of Artificial Intelligence in Pharma Marketing Part 1

I recently presented the plenary session at the ePhMRA annual conference in Warsaw. Covering how AI is transforming pharma sales and marketing, I provided examples Eularis had completed for pharma client projects.
 
Several of the attendees sent me emails afterwards wanting to know more about the specific examples I gave, which were as varied as our client product needs. It was interesting to learn how few of these types of applications they were familiar with, and I thought the readers of this blog would want to know about them, too.
 
I’ve written about many of these topics before, and I’m including those links at the end of each section in case you are interested in digging deeper into a specific topic.
 
 
1. Identifying Rare Disease Patients
According to Shire Pharmaceuticals, the average time taken to diagnose a rare disease without technology is 7.6 years and comes after countless tests and physician visits. This creates a high cost to the healthcare system, not to mention much suffering for the patient. And some cases are even worse.
 
Delays in diagnosis due to the rarity of the conditions can often lead to exacerbated severity. In the past, this meant many patients were never diagnosed, or the condition was so late stage by the time they were, it didn’t help them.
 
Now, using AI, we can identify all of these patients within the data sets (EHR and claims data) within minutes after the initial time spent data wrangling and creating the algorithms. Then every time a new patient enters the healthcare system, that patient is immediately identified using the algorithms.
 
In fact, with the potential of this approach, and the success of our work in this space, our AI project requests has moved from HCP marketing to identifying patients in both classic rare diseases, or sub populations of specific cancer types.
 
https://www.eularis.com/blog/the-application-of-artificial-intelligence-in-rare-diseases
https://www.eularis.com/blog/applications-of-ai-in-rare-disease
 
2. Recommendation Engines/Suggestion Engines for Content Engagement in Marketing or Sales Calls
A lot of the big disruptive players use AI-powered clustering linked to customer data to create very personalised product (think Amazon) or content (like Netflix and Spotify) recommendations. And they aren’t the only ones. Many content marketers use this technology to improve engagement, and so do many in pharma sales and marketing.
 
In sales applications we can identify the optimal next message to give an individual physician to enable greater engagement and move him or her through the customer journey faster. In marketing we use this within the same sort of approach, serving up the right content in the various channels by individual physician or patient.
 
And given they are AI powered, the more data they have, the more they learn. So they get even better at making very relevant recommendations.
 
Learn more here:
https://www.eularis.com/blog/re-you-giving-your-reps-the-tools-they-need-to-succeed
 
3. Predicting and Modifying Patient Adherence
The only way to effectively tackle patient non-adherence is to identify the individual causes for individual patients and deliver a personalized solution on a patient-by-patient level. Automated Artificial Intelligence can help ensure that each patient gets the right message or solutions relevant for the reason of their own lack of adherence.
 
Eularis has been applying Artificial Intelligence to understand physicians’ prescribing behaviors to determine what specific messaging will influence individual physicians to change behavior reliably. We realized similar algorithms could be applied to understanding patients’ adherence and lack of adherence behavioral causes, enabling rapid identification of non-adherent patients while using personalized approaches to influence the individual causes for each patient.
 
This can be automated to consistently add new patients’ data whereby the Artificial Intelligence algorithms learn from each patient’s data input. This allows personalized approaches to each individual patient without a prohibitive time requirement from the Pharma company.
 
With an understanding of adherence issues, careful planning and the use of powerful AI tools, brand managers and team members can eliminate anxiety and uncertainty, and find success through higher ROI.
 
If you’re interested in learning more, I’ve written about this topic in more detail here:
 https://www.eularis.com/blog/using-artificial-intelligence-to-improve-patient-adherence-results-and-patient-outcomes
 
4. Precision Physician Targeting
Many pharma companies are still relying on historical information to make physician targeting decisions. But the market is not about yesterday, it is about tomorrow. Fortunately, today the available data allows us to make more precise predictions about tomorrow. By integrating AI into the physician targeting analysis, you can identify numerous things that can create strong physician targeting and results.
 
For example, on an appropriate timeframe we should be able to predict ‘Which doctor has the most potential to write a script for a patient appropriate for our brand, today?’ and to help the rep understand ‘What should be the priority, based on the most recent data, to gain more scripts of our brand?’
 
Ultimately with channel data added, we should also be able to identify, ‘What messages and channels and sales and marketing actions will enhance that outcome’. In addition, with appropriate patient data we could also identify maximum share per physician based on their patient population.
 
Take a deeper dive into this topic here:
https://www.eularis.com/blog/predictive_physician_targeting_are_you_missing_out_on_valuable_prescriptions
 
5. Content Marketing on Steroids with AI
Content marketing in Pharma faces steep competition as more and more companies jump into the content marketing game. AI can help you stay ahead of the pack with algorithms to produce content, source it, optimize it and distribute it so that it reaches your customers when they want it through their preferred channels.
 
We now are in the era where AI tools can write content and even novels that many can’t tell apart from human work. In fact, a Japanese AI tool wrote a novella that was nominated for a prestigious literary prize.
 
You need a lot of data – but there is a lot of content publically accessible on most topics, and once AI understands the rules, like most things, it beats humans at it. At the moment, many marketers are already using AI generated copy for email subject lines and ad copy, which have been shown to reduce cost per lead by 31% in one study.
 
We have used this kind of AI to issue invitations to clinical trial participants and found we were able to increase acceptance by 29.3%. The more data we have, the higher this will rise. We can also apply this kind of approach for inviting participants into market research for rarer conditions to increase acceptance rates as well.
 
Without AI, a lot of this is number crunching and guesswork. With AI, all is possible far faster than imaginable. So content marketers may want to start looking at revamping your content strategy with AI powered tools.
 
Learn more here:
https://www.eularis.com/blog/why-ai-is-shaking-up-content-marketing-in-pharma 
 
6. Customer Segmentation and Customer Personalized Marketing
While typical pharma segmentation approaches combine prescribing levels with attitude and behavior factors outside of prescribing, they tend to use a limited number of variables and are siloed by brand. You are likely to find the same physicians in multiple brand target lists within one pharma company.
 
The extent to which marketers can segment their consumers comes down to the data that they have – or can get access to.  By unifying these data silos, we can find far richer customer information. And when we apply AI, we can combine unlimited customer variables into the algorithms for a 360 degree view of the customer and data in real time.
 
This means you can segment dynamically, honing in on preferred channels and messaging to connect with individuals based on their individual needs and behaviors at any given time.
 
An individual’s behavior will change at different times for different reasons. I often use driving as an example. If I am in my home country and have to go out of the city, I will drive. However, if I am in a foreign country, I tend to use Uber. Just as location affects my transportation choice, there are variables that affect customers’ prescribing choices. And AI can account for that.
 
You’ll be able to align the brand strategy with value propositions that speak to a narrow market segment. For example, what type of marketing approach does your customer appreciate? Depending on who they are and where they are in the buying cycle, they may prefer educational opportunities such as webinars or calls from sales people. AI can segment these for you and maximize your marketing budget by reaching the right people through the right channel with the right messaging.