Everyone is aware of the term ‘Artificial Intelligence’ or ‘AI’ now and that it has big implications in pharma commercial activity. But there are a lot of misconceptions about what it is, how to use it, and what it can do for your results.
A lot of companies are using it, but many are still sitting on the sidelines wondering if it applies to them and, if so, how to get started and get the value from it that they keep hearing about. The key things foremost in the minds of the non-users appear to be:
I understand it can be beneficial for increasing productivity and generating faster revenue – but how?
Will it take my job?
Simply put, AI can be seen as the intersection of mathematics and computing and designed for a world of big data (either structured or unstructured) to solve real-world problems, used to recognise behavioural patterns through computational learning.
The best way to think of AI is a tool that will save you time and allow you to do your job better to enable you to achieve better results. AI is a powerful tool for helping people do a far better job by taking away the guesswork and being able to take in and process more data than a human could ever dream of, and then find the important patterns and connections that humans would miss due to being physically impossible to synthesise that level of data.
What are examples of where it is being used commercially in healthcare?
Here are just a few of the ways pharma marketers are using AI to enhance their efforts and improve their Key Performance Indicators (KPIs):
1. Identifying Rare Disease Patients
According to Takeda Pharmaceuticals, the average time taken to diagnose a rare disease without technology is 7.6 years and comes after countless tests and physician visits.
Delays in diagnosis because of the rarity of the conditions can often lead to exacerbated severity. Now, using AI, we have been able to identify all of these patients within the data sets 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 success of our work in this space, we have been not only applying this to rare disease but also to subtypes of oncology with outstanding success.
2. Recommendation Engines/Suggestion Engines for Content Engagement in Marketing or Sales Calls
In sales applications, we can use AI to identify the optimal next message to give an individual physician to enable greater engagement and move them to prescribe your brand faster. This is one of the first applications we did in this space.
3. 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 data allows us to make more precise predictions about tomorrow. By integrating AI into the physician targeting analysis, you can identify which physicians should be your targets for maximum sales results.
4. 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 actual content, and even novels, that many can’t tell apart from human work. Eularis has used this kind of AI to write personalized subject lines to email invitations to market research participants. Doing this with AI, we found we could increase acceptance by 29.3%. Content marketers may want to look at revamping your content strategy with AI powered tools.
5. Customer Segmentation and Customer Personalised Marketing
While pharma segmentation approaches combine prescribing levels with attitude and behaviour factors outside of prescribing, they use a few variables and are siloed by brand. The extent to which marketers can segment their customers comes down to the data. By unifying these data silos, including market research language data, and applying AI, we can gain a 360 degree view of the customer and identify stronger behavioural segments. With these, you’ll be able to align the brand strategy with value propositions that speak to a narrow market segment.
6. Context Marketing and Omni-Channel Marketing
It can take 20-30 sales and marketing touch points before customers prescribe or purchasing your product. The more value you can add at each touch point, the more successful you’ll be. But providing a cohesive experience across the entire customer journey can be challenging, especially in an omni-channel campaign.
With context marketing, you can add contextually relevant and personal experiences based on the activity and needs of the individual. Combining data and advanced AI modelling allows us to identify, by customer, the next best content, in the next best channel, in the right sequence, at the right time. This results in maximum customer engagement and faster journey to the brand.
Eularis conducted a project doing this across many brands and for 30 countries for one client and it added over a billion dollars in incremental sales. We are currently planning this same approach for a large global healthcare agency so they can use it for their clients. It is exciting work, yet complex, and requires the right experts to be involved to achieve your goals.
7. Customer Journey Mapping
Although content management systems were a wonderful leap in technology, Artificial Intelligence takes things to a new level. You can now uncover the changing nature of the customer’s relationship with the brand, ensure that you disrupt the journey in a positive way, and fulfil all the customer’s expectations in order to maximize engagement.
These are sorts of questions we are now answering:
• What is the unique journey for each customer?
• What is the optimal sequence of content for that customer to drive brand adoption?
• What are the optimal sequences of touch points to drive brand adoption?
• Which profiles of customers are best predictors of potential for increased business?
• Which tactics drive customer adoption in this journey?
• What is the optimal resource allocation across digital and non-digital channels?
• When a customer drops off the journey, which is most valuable to re-engage and what is the best way to re-engage them?
• Which customers should we not engage reps with?
• Which customers use a competitor brand but are vulnerable to switch with the right content and touch points?
• What is the portfolio cross-sell for any specific customer (i.e. given a large portfolio of brands, we can determine the optimal sales and profit outcome)?
8. 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 AI 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 behaviours to determine what specific messaging will influence individual physicians to change behaviour reliably. We realized similar algorithms could apply to understanding patients’ adherence and lack of adherence behavioural causes, enabling rapid identification of non-adherent patients while using personalized approaches to influence the individual causes for each patient. We have successfully done this now many times.
9. AI Search Engines and Chatbots in Pharma Market Research
Many companies have masses of PowerPoint documents, the content of which is known by the team. But what happens when team members leave and their replacements want to find information? It depends on the existing team bringing the new members up to speed. That’s why knowledge can easily be lost with job transfers.
By creating a database of all your market research PowerPoint files and applying AI search to it, you effectively allow everyone on your team to have the entire database of knowledge at their fingertips. Plus, they can search it in multiple ways, even if they don’t know whether what they are seeking is in there. The AI natural language processing will interpret what they require and identify the content they are seeking.
We can even layer chatbot technology so the marketers don’t even need to type a search. They can simply ask like they would with Siri or Alexa.
10. AI Enhancing Customer Call Centres
There are so many ways we can integrate AI into pharma call centres to replace IVR and humans. By adding AI, such as natural language processing and machine learning, instead of giving a set of choices that are recognized set keywords, the system can understand the question and deliver the response or action. AI is great at prediction when big data is involved, and call centre data is big data. AI can identify early trends in customer behaviour and provide this to the call centre team so that they can handle customer needs more effectively. This could reduce drug switching and lack of adherence, and benefit sales and marketing planning. The use of AI as digital humans can replace the call centre human function. The Bank of America have reduced their call centre staff by 50% in 2019 by replacing them with a digital human, and their customer satisfaction has gone up!
11. Key Opinion Leader (KOL) / Thought Leader (TL) Mapping
Traditional and AI approaches to identifying KOLs in a therapy area use many of the same sources, including publications, conference abstracts, Sunshine Act data and patent applications. The fundamental difference is that with AI, the data is constantly updated, analysed automatically and can identify things traditional approaches miss.
The AI approach uses public data to map KOLs and TLs in a way similar to how the CIA maps terrorists and drug cartels. Our clients who are doing this can use the data in different ways to address questions across the organization, from Sales & Marketing to Clinical and Discovery. These are just some of their wins:
• Validated the strategic brand plan
• Pressure tested the client’s view regarding who were the top influencers
• Identified blind spots in the MSL engagement strategy
• New early phase pre-patented opportunities were uncovered
• Rising star and fresh faces identified for recruitment
• Better publication planning
• Congress interaction planning and communication
• Rapid identification of optimum influencers for clinical trials and research collaborations
How to get started:
Transforming your marketing in such a dramatic way can seem overwhelming. And there is a lot that goes into designing an effective AI solution, but it doesn’t have to be intimidating. If you take a little time and effort to prepare and know what to expect, you can avoid complications and frustration later.
Step 1: The first step is to increase your AI comprehension. You don’t have to be a data scientist or programmer yourself to develop marketing strategies that integrate AI, but it is important to know the concepts, capabilities, and basic processes.
Step 2: Next, decide on use cases you want to prioritize. There are so many possibilities with AI, so it may be helpful to seek case studies and examples from other companies, even if they are outside your industry. Then brainstorm how you can adapt these to your situation. Use cases can include ways to improve upon existing processes, campaigns or efforts, and new ways to bring value to your digital marketing. Consider your most important KPIs and where you have the potential for the greatest impact from AI.
Eularis created a free 5 day challenge last year to help pharma teams and their agencies understand what AI is all about and all the fundamentals of what it is, how it is used in healthcare, and then how to identify the right challenge and use case to solve for your business. We are making the recordings of this available for you. There are 5 free videos that will be released one each day for 5 days, and exercises for you to do for your own situation. No video is longer than 30 mins and some much shorter. You can sign up and receive the links to these to help get you started on your AI journey here.
Step 3: When you know what challenge you want to solve with AI, the next step is to design a winning solution.
Step 4: The next step is identifying what the best data to use is and where to source it.
Step 5: Then planning the optimal AI techniques and approaches to use is next. This has to be from data scientists.
Step 6: Next, you will need to assess and plan your tech stack. Data science projects don’t always need a tech stack, but some do. You need to understand if you need one for your strategy, and if you do, what it will require of it.
Finally, you’ll need someone (or a team of people) with the technical skills and knowledge to make your AI strategy a reality. The off-the-shelf products that exist are full of dangers for those without an understanding of AI. Most have not been made for pharma specifically and therefore were not trained on the right data sets, which has led to some major challenges for some pharma using them – including a legal case in one instance. Even with the limited number of off-the-shelf products that do work with pharma data specifically, you will always need data scientists to get the power out of these as the data needs to be in the right format for the tools, or you will get very little from it. Garbage in garbage out! This could include partnering with companies like Eularis with extensive AI capabilities, or hiring AI professionals to work in-house or a combination of the two.
The examples provided in this article are only a few of the projects that we have already employed in our pharma engagements, but they are a tip of the iceberg. Every project is different and requires different data and different approaches to meet the client’s unique business needs. The 5 day challenge will get your understanding a little further along and help you shape where to begin, so don’t forget to sign up here and watch the free videos.
One of the biggest issues I see in AI project failure is not focusing on the right business challenge to solve with AI, — followed by not using the right data for it.
Give me 30 mins a day, for 5 days, to help you understand Artificial Intelligence and know how to identify your best business challenge to tackle with AI.