LLMs Aren’t Just for Chat Apps – They Can Boost Pharma Sales Teams’ Customer Reach, Too

Among high-level sales, pharmaceuticals rank among the hardest products to sell, especially in today’s fast-paced market, where new and specialized drugs are approved every week. With this plethora of new drugs coming to the market, busy doctors have a hard time keeping up with new developments, and are looking towards the guidance of educated pharma firm representatives to advise them on how new products can help them better serve the specific needs of their patients; what are the differences between new drugs and the treatments they have been using, and how outcomes will be improved by these drugs, and more. A sales team that wants to reach those customers must locate them, and must display a knowledge not only of the product, but also the target population for a drug, market conditions, regulatory issues, competitors’ offerings, and much more.

Gathering this information – much less mastering it – is a difficult, time-consuming, and tedious process, especially for sales teams at smaller pharma firms, where resources are likely limited. But for sales teams that utilize advanced data collection and analysis technologies – perhaps especially at small firms – the process is much smoother and easier. Specifically, sales teams can use AI/ML solutions that analyze large datasets – using large language models, or LLMs – to extract insights on customers, products, patient journeys, regulatory issues, and anything else they need to connect with HCPs, and close sales.

Automated LLM-based analysis of data sources using AI and machine learning-powered algorithms is not only the most effective way to extract these insights; in a world that gets more complicated and data-laden on a daily basis, it’s really the only efficient option available. Doing this manually would constitute a long, iterative process that would be prone to human error. And even a successful iteration of that data would – because of that potential for human error – likely result in a brittle foundation that would not be optimized to fully utilize the business potential of the data. In addition, sales teams would need analytical applications to parse the data and deliver the actual insights and knowledge they need – and developing such applications in house would likely be beyond the capabilities of most pharma organizations.

The best way teams can meet these challenges is to deploy an AI/ML platform that will provide them with the guidance they need, as they need it. Such platforms can enable teams to independently do everything they need to acquire these insights including collating the data sources, applying the requisite LLMs, and utilizing the applications that will enable sales teams to quickly and efficiently get the insights they need. The advantage of deploying such a platform over other solutions – especially over hiring a consulting firm to develop these insights – is that working with a platform gives teams full and continuous control over the process, enabling them to tweak the data as needed in order to zero-in on the insights they need, And with agile LLM-based AI-powered platforms, the process of acquiring sales insights is as simple as pressing a few buttons,

This is especially relevant for sales teams at small pharma firms, which often specialize in providing solutions to specific conditions and diseases – and which often have limited resources, which, if they do exist in the organization, would likely go towards research, not data science for commercial operations.

Data abounds today, collected from a wide variety of sources, both inside and outside the organization. When data is analyzed by algorithms based on LLMs that parse the data through natural language queries, all of the information from a rich variety of sources is put into context. This context provides sales teams with the insights they need on products, presentations, customer needs, industry information, data relevant to specific HCPs and their patients’ needs, along with much more.

LLMs are at the heart of advanced text analysis, such as that provided by ChatGPT and other advanced AI-based engines. Far from just a tool to write essays or poems, ChatGPT based on general LLMs can analyze data from many sources and synthesize insights that provide new paths to solve problems. Using LLMs that encompass data about pharmaceuticals, the medical industry, patient cohorts, community information, regulatory data, and much more, sales teams will be able to discover more potential customers, new and better ways to approach them, present their products, close sales, encourage repeat sales, and more.

Platforms that utilize this technology make mining the data for these insights – and applying them to specific sales situations using applications designed for that purpose – enable sales teams to get down to business, engaging with customers and closing deals. Such platforms support real time automated creation and storage of a data foundation without requiring sales teams to use code, as well as automated application of the algorithms utilizing the LLMs created by the data analysis.

The automated process integrates any number of data sources, cleans and enriches them to improve the data quality, and then auto generates an elaborate database with 360-degree tables for every HCP in the relevant therapeutic universe, including factual, historical, measured, calculated, and predictive features, as well as models, dashboards, and KPIs, all cataloged with a self-exploration search engine to match users’ requests with specific data assets. Via such platforms, teams get everything they need to engage with customers – and close sales.

For years we’ve been hearing about the “coming AI revolution,” the one where advanced generative AI will vastly improve our lives – helping make a wide range of human activity easier and more efficient. Now it appears that we are on the cusp of that revolution – and the model presented by ChatGPT and LLM technology, where text and data can be analyzed for more and better ways of doing things – including helping pharma companies reach the right HCPs with better solutions that will help make their patients healthier. Such technology can go a long way towards providing sales teams with the tools they need to help HCPs make that happen.