How will machine learning algorithms support pharma marketers?

Everywhere you look, machine learning and artificial intelligence solutions are making your life a little easier, whether you notice it or not. From the GPS app on your smartphone to the fraud protections on your credit cards, intelligent software performs complicated tasks that would be otherwise impossible for humans to accomplish.

The world of healthcare is no exception. For example, machine learning assisted MRI scans have already proven effective at identifying cancers, according to Built In. Similar solutions have been implemented in electronic health record systems to find patterns among patient populations and drive better care. Soon, pharma marketers won't be able to avoid these solutions in their day-to-day workflows.

Understanding machine learning marketing use cases

Marketers have heard the potential benefits of machine learning solutions for years, but how is the technology utilized today? The number of potential use cases may surprise even veteran advertisers.

Here are a few examples of machine-powered marketing in action:

• Sentiment analysis: As more pharma reps are barred from visiting physicians in person, they lose the ability to accurately judge how these healthcare professionals feel about product pitches. If you can't see a prospect's face, how can you tell what they're feeling? Now, machine-driven sentiment analysis can parse through written communications and help you read the emotions of customers.
• Lead scoring: AI algorithms can assess every aspect of the customer journey and accurately score leads based on proprietary criteria such as web pages visited, emails opened and assets downloaded.
• Customer personalization: Marketers can develop content to place in an automated system that serves up different pieces of collateral depending on the customer's previous interactions with the brand.

In a healthcare context, marketers can leverage natural language processing to develop risk equations and better target HCPs. Solutions like predictive analytics can determine a lead's opinion of a product and make suggestions about what type of content can move the lead further down the sales funnel. Rather than sending out a cold email and hoping for a response, marketers can hyper-personalize their messages and increase click-through rates and related metrics.

Machine learning algorithms can utilize massive amounts of data to draw intelligent conclusions.

The future of pharma marketing is personalized

At a recent conference in New York, Daniel Gomes, chief innovation officer at Trident Tech was optimistic about AI's capacity to engage HCPs. He noted that machine learning is already being applied to many healthcare functions, and it only makes sense that pharma marketers will adopt the technology.

"We're in a complex and flooded digital landscape, and HCPs say they don't have time to read all of [their emails]," Gomes said. "There's no silver bullet here. It's about gaining some hints and insights about behaviors so when the doctor comes in and is considering deleting [emails], maybe we send him something that's more appropriate that he will want to engage with."

Marketers that can pull analytics data from print and digital channels will be uniquely positioned to deliver targeted messages at the right moment. For example, if marketers include a promotional code in their print advertisements, they can determine which leads come from that channel. Later, they can send relevant messages to those leads, based on the content they previously interacted with.

AI and machine learning represent sophisticated tools that can elevate traditional marketing channels, creating efficiencies along the way and driving more qualified leads to sales teams. To learn more about how to develop a robust multichannel campaign, connect with a trusted Elsevier consultant today.