• Diverse data - Across brands and therapeutic areas, social data is complex, unstructured, unformatted, and comes in various shapes and sizes (e.g., text, video, images, emojis, slang expressions). While there are several social listening tools available in the marketplace, most of them focus on processing text, and to a limited extent, images. Interpreting video content takes either investment in transcription software or significant human analyst capacity.
• Siloed social data – Many in-market social listening tools do not often consolidate digital information beyond social media. This routinely deprives decision makers of a holistic view of information from diverse digital data sources such as Google search queries, content virality, news, product reviews and ratings, web analytics, campaign analytics, and owned social media channels. This incomplete view of insights may unintentionally lead to insufficiently informed decision making.
• Decentralized social listening – Driven by diverse business priorities, different functions within life sciences companies including market research, medical affairs, HCP communication, patient engagement, drug safety, digital marketing, corporate PR, and brand management may prioritize social listening projects locally or based on need, thus forgoing the synergies which could be driven by a consolidated operation.

The case for enterprise-wide social listening for pharma

An NLP-driven enterprise-wide social listening program can help life sciences companies effectively mitigate the above-mentioned challenges and realize the full potential of insights from social listening to inform patient-centric product development and customer / patient engagement.

The size and heterogeneity of format, content and sources of social media data pose a challenge in data collection, organization, and processing. The big data problem can be tackled using advanced NLP technology which enables the processing of large amounts of unstructured data into a meaningfully organized state. However, technology has limitations in analyzing non-textual formats such as video and images. Human intelligence can help not only solve the challenge of analyzing non-textual data formats but also provide actionable recommendations through insights consolidation from data in various formats and from varied sources. Finally, to ensure a holistic view of insights from diverse data sources beyond social media, combining the data and bringing it onto a single platform can help overcome the challenge of making partially informed data-driven decisions.

Enterprise-wide social listening can allow life sciences companies to create a consolidated and comprehensive social media and digital knowledge resource that can monitor the overall reputation of not just brands but also the company, and be leveraged across business units and functions. If conducted coherently enterprise-wide social listening can reduce technology subscription costs, drive efficiency in data processing, and effectively serve multiple internal clients. In addition, life sciences companies can also integrate social media insights with findings from other internal information assets, thus validating insights and driving better informed decisions.

For instance, audience analytics, HCP outreach and patient engagement teams can benefit from findings on social media user segments as well as the type of content shared by them. Insights regarding patient perspectives on disease and treatments, brand and company perception across user segments, online product reviews and ratings, and website and campaign analytics can be leveraged by the competitive intelligence, market research, brand management, corporate and R&D teams. Further, influencer identification can be immensely useful to HCP outreach and patient engagement teams looking to develop digital and social media outreach programs.

The perfect recipe

IQVIA’s advanced-NLP based social intelligence offering provides an optimum configuration of components needed to drive long term value, including:

• Data procurement: Diverse and deep data integrated from various data sources such as social media, web analytics, search, viral content, and advertising research, and hosted on a cloud architecture in compliance with data privacy regulations
• Technology: Unstructured text processed through an advanced NLP engine using techniques such as sentiment analysis, topic modeling, named entity recognition, keyword extraction, and text classification
• Visualization: Real-time dashboard using data visualization tools that present quantitative and qualitative KPIs with the ability to filter and visualize data subsets of interest which can be used to identify patterns, trends, and relationships within the data
• In-depth Insights: Qualitative analysis, insights consolidation, consultative problem-solving and actionable recommendations through human intelligence Digital Pharmacovigilance: Timely adverse event reporting to ensure regulatory compliance

Next steps

Start with an internal audit of the organization’s current state of social listening. For instance, is your company monitoring social media conversations merely to identify and mitigate risks to reputation? Or is there a holistic program to proactively incorporate ‘voice of the customer’ into commercial programs. Next, identify areas where customer insights are insufficiently represented and what are the resulting potential loss of opportunities. Further, identify internal departments which could routinely benefit from consumer insights, followed by establishment of specific commercial and brand objectives. Lastly, design and implement a comprehensive social listening program optimally configured with the right set of information sources, technology, human skills, and processes.

References

1. Sermo & LiveWorld. (2023). Survey Finds 57% of U.S. Physicians Have Changed Their Perception of a Medication as a Result of Info on Social Media. New York: https://www.businesswire.com/.