Social media has permeated almost every aspect of our lives and has undoubtedly become one of the world’s most popular ways for people to connect. Around 3.96 billion people worldwide use platforms such as LinkedIn, Twitter, and Facebook for an average of about 145 minutes a day. The number of users is estimated to grow by almost an additional billion in the next 3 years.1 Going by this trend, it is safe to say that social media has become a human habit.
Zooming into the pharma industry, in particular, a growing number of healthcare professionals (HCPs) are using social media for research and to access medical information. 4 out of 5 doctors use social media in their practice, with 56% of them using HCP-only networks such as SERMO and Doximity, and 32% of them using open platforms such as LinkedIn and Twitter.2
A large number of HCPs actively use LinkedIn to share their thoughts and opinions on a wide range of topics. Healthcare influencers on the platform have more than 2.4 million followers combined, creating a large community of people seeking healthcare-related content.3
Besides HCPs, millions of hospital executives, nurses, and other industry professionals are also active on LinkedIn. Figure 1 highlights the approximate volume of diverse industry experts available on the platform today.4
Twitter has also emerged as one of the most engaging platforms for HCPs to connect. The number of HCP users on the platform has increased 6-fold in the past decade, with a total of 600,000 users recorded in 2019.5
This number continues to grow as more HCPs become digital-savvy and seek continued social media engagements.
Healthcare professionals share useful information on social media. They debate healthcare policies and practice issues, promote therapies, interact with patients, peers, and so on. As a result, they increase their awareness of medical news and discoveries, educate patients and communities, and improve overall health outcomes.
Mining this data presents a promising opportunity for pharma companies as it provides insights into the key drivers of HCPs’ social media conversations, the type of content they share, what events they are engaging in, and how they prefer to engage online. These insights help commercial teams to laser target their prospecting efforts with relevant content, enabling them to build and nurture HCP relationships in a truly meaningful way and eventually convert them into business opportunities.
This technique is popularly known as social selling, and companies across industries have been leveraging it for decades.
By monitoring and analyzing HCP-generated content across social media platforms, social selling techniques give pharma companies the market intelligence that they need to drive better sales engagements. Many top pharma companies have already begun adopting social selling tactics to enhance their customer relationships and improve brand management.
With social selling, pharma companies can:
Better understand HCP behaviors, interests, opinions, and expectations
Refine engagement strategies with relevant content based on HCP preferences
Generate leads through targeted marketing campaigns
Shorten sales cycles by automatically profiling prospects and qualifying them based on the collected data
Stay relevant by posting informative content that HCPs prefer, ultimately increasing brand awareness and thought leadership presence
Build new relationships and nurture existing ones with meaningful engagements
According to LinkedIn, 78% of social sellers outsell peers who do not use social media.6
Since social media platforms are saturated with millions of diverse HCPs from all walks of life, the amount of data generated is exponential. The unstructured and fragmented nature of this data can be overwhelming for pharma companies as they navigate through a sea of information to identify the right target audience and understand their preferences.
This is where Natural Language Processing (NLP) can help. It is a machine learning technique used to convert large volumes of unstructured data into a structured format. NLP models can be trained to recognize HCP sentiment in social conversations by identifying language patterns that reflect their opinions, interests, and expectations.
HCPs’ data from social platforms are first extracted by leveraging scrappers or third-party tools such as Sprinklr. These unstructured data are then structured in 4 steps:
1. Named Entity Recognition: This is a process to recognize information units such as organizations, locations, names with domain-specific topics including diagnosis, medication, anatomy, and more from unstructured text. The goal is to develop domain-independent techniques to automatically detect named entities with high accuracy
2. Intent Classification: Extracts insights on customer intentions by automatically associating words or expressions with a particular intent
3. Sentiment Analysis: Mines data to extract insights on opinions and emotions from text, and classifies them into a positive, negative, or neutral category
4. Topic Modeling: Automatically divides the text into shorter, topically coherent segments, and is used in information retrieval from large documents
The structured data is then leveraged to generate insights into the current trends in HCP conversations, their expectations, and perceptions on a broad range of topics. Sales representatives use these insights to drive relevant conversations with HCPs, personalize their content strategy, and drive meaningful social engagements.
With customer-centricity taking center stage today, there has never been a better time for pharma companies to embrace social selling techniques to find new customers and nurture existing ones by crafting personalized pitches to meet their needs. NLP makes this simpler to achieve by structuring vast amounts of data quickly, increasing speed-to-insights, and helping pharma companies plan better engagement strategies.