Workin’ On the Pharma

  3 m, 13 s

Marketing for Pharma? We’re Listening.
Part of the appeal of the show Mad Men was not just its dedication to recreating the look and feel of the 1960s, but that it was a story of advertising and marketing. Those characters crafted advertising campaigns based on “expertise,” which was really just their own emotional connections to a given product. Unfortunately, while this made for great drama, it doesn’t make for the best marketing strategy—especially in today’s world where doing this would mean actively ignoring your customers.

A killer instinct for what people needed but didn’t know they needed used to be all a great marketer needed. So the story goes, anyway. Because to our ears, that sounds like a pretty risky (and expensive) strategy. But, on the other hand, people generally know what they need – and they’re happy to tell the world all about it. So isn’t it better to listen to them and serve up exactly what they want on a platter? We think so.

The good news is that there’s already an extensive amount of information available for anyone to track and research in online forums, social media, and product and service reviews in today’s world. On top of that, companies these days all have their enormous databases bursting with sales, demographics, and decision-making history. And every bit of it is customer research gold. So when customers log on, call up or email to discuss problems, solutions, or products, they’re giving companies a blueprint for reaching them with relevant, targeted marketing or exactly-what-they-asked-for customer service solutions.

However, the sheer volume of data makes it impossible to keep up with without some help. That’s where AI, ML, and NLP come in. Using an NLP platform and your industry expertise, you can mine the data in the cloud and on the web to create a map of themes that deliver valuable, actionable insights into customers’ emotions and behaviors. This gives you the roadmap you need to create a customer outreach or marketing strategy that meets people where they are.

Let’s use the pharma industry as an example. Companies can use AI-supported platforms like Semantria to examine what patients are saying about their pharma and pharma-related experiences across various contexts: anything from patient surveys to customer care calls to Tweets.

This sort of solution is what our customer AlternativesPharma (case study) used to create a thematic map centered around 10,000 data points based on more than 2,000 specific queries. Instead of wasting employee hours, creative energy, and time making a best-guess strategy, they used their institutional knowledge to create a proprietary set of specific queries that used customer-submitted data to craft the best marketing solutions for those customers. Once the patients bought into their strategies, healthcare providers and authorities followed.

Another example of how AI-based text analytics and NLP can help pharma companies better market to constituents is through content aggregation across the entire organization. Our customers encounter a significant problem: the siloing of data and inconsistent tagging of content that shields insights from departments that need to access it. With disease- and condition-specific taxonomies created by combining both existing tags and those suggested by Lexalytics’ models, all teams, especially marketing, can benefit from the insights therein to hone and market campaigns to key constituent groups.

Of course, it’s not just pharma that can benefit from platforms like Semantria. Any industry that operates on multiple tiers and deals with large amounts of data can also win. To go back to the Mad Men example, the show’s characters played to their clients and gave them what they thought the people from Hershey’s or GM wanted to hear. Using this technology, we can help companies listen to their customers’ words, giving both precisely what they want.

Learn more about how AI and NLP can assist with pharma matters.

Categories: Insights

Leave a Reply

XHTML: You can use these tags: <a href="" title=""> <abbr title=""> <acronym title=""> <b> <blockquote cite=""> <cite> <code> <del datetime=""> <em> <i> <q cite=""> <s> <strike> <strong>