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using ChatGPT in life sciences sales
June 8, 2023

Artificial Intelligence, ChatGPT, and the Future of Life Sciences Sales

using ChatGPT in life sciences sales(This article originally appeared on Performance Development Group. And the author used ChatGPT to help write it.)

Artificial Intelligence (AI) is everywhere these days, creating opportunities, questions, and concerns about exactly how much of our thinking we want computers doing for us. Perhaps the most recognizable face of AI is ChatGPT, a free (for now) natural text query system that uses aggregated data to answer questions using everyday language like “What is the best way to make eggs?” and “What’s the best breed of dog for children?” But what can AI really do for Life Sciences sales? Before we answer that question, let’s answer a few others.

What Is Artificial Intelligence?

Most of us use AI every day; we just may not know it. Most online platforms analyze our behavior and use it to feed us advertisements that the AI thinks reflect our preferences. You’ve probably had Facebook feed you ads for refrigerators when you’re thinking about buying a new one, or Spotify recommends songs you’ll like, or Amazon provides endless product recommendations. Adaptive learning platforms like Axonify and Allego use AI to figure out where learners have expertise and where they have gaps, and they provide content that meets their specific needs. That’s AI at work, analyzing a lot of data and reaching conclusions.

AI is a digital system that mimics human reasoning but can usually process vast amounts of data. An AI system doesn’t just follow rules but learns from interactions with users and creates new rules. That means AI can solve problems that it wasn’t programmed to solve.

Why is AI valuable? First of all, AI can analyze vast amounts of data from different sources very quickly. And AI loves to learn—it can be taught to learn, it can learn on its own, or it can make its own rules. The more data it has, the smarter it gets. And in some ways, AI can monitor itself; it can make predictions based on the data it has consumed, and it knows when it doesn’t have enough data and needs to find more.

What Is ChatGPT and How Will It Help Life Sciences?

ChatGPT is a natural language processor developed by OpenAI. “Natural language processor” means that you can ask it questions in normal language (“What kind of dog is best for children?” “Who were the Marx Brothers?”) and ChatGPT will pore through its stunningly large body of data to form an answer, also in normal language. It does the two things AI does best: analyzes enormous amounts of data to reach a conclusion, and it learns from its mistake, which suggests that each successive answer should be more and more accurate.

If you really want to have fun, ask ChatGPT who you are, or your boss, or your mother-in-law. My inquiry “Who is Rich Mesch?” resulted in a paragraph of embarrassing praise followed by a paragraph of utter nonsense; your mileage may vary. But ChatGPT loves to learn—it’s constantly asking if you’re satisfied with your answer and whether it could do better. In a lot of ways, ChatGPT is a sophisticated guessing machine, but it gets smarter with each guess.

How is Life Sciences using ChatGPT? Well, Marshall McLuhan said, “The medium is the message”—so I thought I’d let ChatGPT answer that question. The following section is written entirely by ChatGPT’s AI:

“As an AI language model, ChatGPT can assist Life Sciences sales in several ways. Here are some examples:

  1. Customer Support: ChatGPT can act as a virtual assistant to help customers find the products they need, provide information about product features, and answer frequently asked questions. This can help free up sales representatives’ time to focus on more complex customer inquiries.
  2. Lead Generation: ChatGPT can also be used to generate leads by engaging with potential customers and collecting their contact information. This can help sales representatives to identify potential clients and reach out to them with targeted sales pitches.
  3. Product Recommendations: ChatGPT can use data on customer behavior and preferences to provide personalized product recommendations to customers. This can help sales representatives to promote products that are more likely to be of interest to specific customers.
  4. Marketing Insights: ChatGPT can be trained to analyze data on customer behavior and market trends to provide insights that can help sales representatives to refine their marketing strategies and better target potential customers.

Overall, ChatGPT can be a valuable tool for Life Sciences sales, helping to streamline customer support, generate leads, provide personalized product recommendations, and offer valuable marketing insights.”

So, real life writer back again—let’s play detective and confirm if what ChatGPT is telling us is true.

How Is Life Sciences Sales Using AI Now?

Let’s start by examining how pharmaceutical sales is using AI in general. One of the most common ways is through omnichannel selling. Omnichannel is sort of like multi-channel selling on steroids. The goal of omnichannel is to aggregate data from as many communication channels as possible in order to determine the needs of the customers and the messages that are most likely to resonate with them. What pages on the website are they hitting? What searches are bringing them there? What questions are being asked most frequently? What types of interactions do they value most?

If there’s one thing AI is good at, it’s aggregating and processing a lot of data. And this data can be used to recommend touchpoints for customers, called Next Best Action. According to Bhavana Ramesh from Grid Dynamics, “Next Best Action (NBA) can be created to recommend individual HCP-level content and channels based on all past interactions and touchpoints. The NBA engine converts past interaction data and campaign settings into recommendations, such as recommending the optimal channel for the next HCP engagement.”

Next Best Action is a double-edged sword. While it can focus sales efforts and deliver highly relevant messages to HCPs, it can also impact the human element of the interaction. Many reps are uncomfortable with AI-generated recommendations, and some resent having a machine-generated directive replacing their field-savvy. But using omnichannel strategies leveraging AI-driven suggestions is becoming more and more common in pharma sales.

So how is Life Sciences using ChatGPT? Well, ChatGPT is nothing if not honest; I asked ChatGPT the following question:

“What are some real-life examples of how pharmaceutical companies are using ChatGPT for sales?”

And, ironically, ChatGPT admitted it didn’t know:

“I apologize, but I am not aware of any real-life examples of pharmaceutical companies using ChatGPT for sales. The use of AI technologies in the pharmaceutical industry is a rapidly evolving area, and there are many potential applications of chatbots and other conversational interfaces for customer engagement and support. However, the use of such technologies in the context of sales is a relatively new concept and is likely to be subject to strict regulations and compliance requirements.”

I actually think I saw ChatGPT blush a little. So, if we’re short on specifics, what are the possibilities?

The Opportunities of AI for Life Sciences Sales Teams

If we think about the challenges of Life Sciences sales, both from the rep and customer perspectives, there are a few opportunities that seem pretty clear.

  • Therapy Knowledge Management. A rep carrying a portfolio of three products has a massive amount of information to keep in their head. An HCP, who may be prescribing dozens of medications for their patients, is expected to know everything about all of those products, from indications to dosing to adverse events. This is one of the many reasons the “reach and frequency” model is so popular in Life Sciences sales; it’s easy for HCP knowledge to fade or get back-burned, and the rep is there on a regular basis to remind and reinforce. Using a natural language AI system could help both rep and HCP keep the knowledge fresh.
  • Call Planning. Reps may make as many as 10 calls a day and are expected to have time to prep all those calls, remember the names and needs of all practice members, and recall the pressing issues from the last call. That’s a tall order, and that data often lives in different software platforms, requiring a multi-touch process to create an effective call plan. The result is that call planning either takes an unreasonable amount of time—or, more likely, gets short-changed or ignored altogether. An AI platform like ChatGPT could aggregate these massive amounts of data, provide guidance, and even complete a pro-forma account plan for each call. That would create great efficiency—and a lot of grateful reps.
  • Overall Productivity. Nobody enjoys managing data, and it takes up valuable time. AI can help manage that data, and similar to Next Best Actions (discussed earlier), can analyze data and recommend strategies for smart ways to work with customers. ChatGPT might make a handy virtual coach someday.

The challenge in all of this is that AI works best when it is trained—otherwise, it may reach conclusions that make sense to the AI but aren’t really reflective of how things work in real life.

What’s Next?

Way back in 2020, Forbes Magazine published an article called “If You Aren’t Using AI, You’re Falling Behind, According to the U.S. Patent and Trademark Office.” In the years since, AI has taken an even stronger foothold in the way we do business. AI is a bit intimidating, but ignore it at your peril. It’s here to stay.

About the author: Rich Mesch is the VP, Consulting at Performance Development Group. He has been working in performance improvement for over 30 years, working with some of the world’s largest companies to solve business challenges by improving human performance. He is the host of the podcast “Real Impact!”, co-author of The Gamification of Learning and Instruction Fieldbook, a blogger, and conference speaker.

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