Tanmay Saraykar

What’s the one thing you wish you’d known when you started in social listening?

I wish I had a better view of how the social listening space would evolve. Specifically, it would have helped to get a better sense of the positions the stakeholders (technology providers, research companies, agencies, clients) would take and the role they would play in the coming years. Also, I wish I knew that there would be disproportionate investment in favor of technology as compared to human skill development. When I started social listening almost 13 years ago, this was a new field of research at least in the life-sciences industry. The early social listening tools produced largely quantitative metrics and there was almost no reference to NLP or AI. Audio and video monitoring was minimal, and applications of social research were limited. Of course, the tools evolved rapidly but even with their initial limitations, the industry initially laid a stronger focus on technology and automation and underestimated the role of human intelligence to drive actionable insights. Over the years we discovered with experience that pure technology was insufficient to generate desired results. Human intelligence, with a thorough understanding of capabilities as well the limitations of the tools was essential to bridge the gaps. 

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Have you had any experiences that have made you want to quit? What made you keep going?

There is never a dull moment in social intelligence. I liked social intelligence right from the start and my interest only grew with time. Many times, we faced significant challenges such as insufficient social data to generate rich insights, insufficient research budgets, skepticism of research methodology, short project deadlines, and hard-to-convince clients. These seem insurmountable and demotivating at times, but if you take a problem-solving attitude, the focus tends to be on getting past the obstacles rather than being overwhelmed by them. This kept me going. 

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What role does tech play in your social intelligence process? Where do people contribute?

In my opinion, people drive the end-to-end social intelligence process and technology is an enabler. Social intelligence technology’s role is to procure information, filter relevant data, organize, categorize and process information, generate quantitative and semi-qualitative KPIs, detect and highlight trends, and provide real-time reporting capabilities. Throughout the process, people need to make decisions at critical stages, such as checking research feasibility, deciding the problem-solution fit, setting direction of research, configuring technology, consolidating analytics, interpreting results and based on findings, making decisions to act or not to act. In a nutshell, it is the combination of technology and human intelligence that works best. 

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Who have you seen as a mentor in your career?

Over the years, I had the opportunity to work with several mentors. The two mentors who had the most influence on me are Amit Sadana (former SVP at IQVIA, currently Chief Strategy and Operating Officer at Apeiro) and Siva Nadarajah (former General Manager at IQVIA, currently President at Jogo Health). 

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Most embarrassing mistake you made in a social listening project - what did you learn from it?

The most embarrassing mistake we made in a social listening project was to deliver unprocessed information to a client instead of spending sufficient time analyzing the information, generating insights and providing actionable recommendations. This was at the very early stages of the practice. Clients expect us to conduct in-depth analysis, interpret results and suggest action. The most significant learning from this experience was to never miss the mark in terms of client expectations. 

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How do you see the future of social listening evolving?

I think the social intelligence industry is at a fascinating stage of evolution. Several things are happening. For instance, while advancements in technology, especially in advanced analytical techniques, use of NLP and Generative AI have brought efficiency to the analysis process, they have also raised expectations from social insights and there is pressure to deliver on those expectations and generate tangible output. At the same time, newer tools with such AI capabilities are entering the market although they are not mature enough to justify replacing existing infrastructure. These tools will have to establish differentiation quickly so that investment can be justified. In the meanwhile, there is an explosion of content in the short video format such as TikTok videos, YouTube shorts and Instagram reels and monitoring such content hasn’t fully matured. I have seen new tools which have video monitoring capabilities and those will see quick adoption. Overall, in terms of evolution, we will have to adapt to these changes and quickly put in place technology configurations, processes, and methodologies to continue to support businesses with actionable social insights. 

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What’s the most useful data source? Are there any you find useless? Why?

My answer to this question would be subjective. I believe the usefulness of a data source depends on the research objectives. I speak from a purely healthcare perspective. In healthcare, platforms which host detailed conversations (such as blogs, forums, Facebook) or describe long term experiences (YouTube) are quite useful in terms of getting deep patient insights. Lately, there is growing activity of patients on TikTok videos, Instagram reels and YouTube shorts and those are quite insightful too. In my experience, when we analyze posts related to medical conditions, therapies, drug brands, medical devices or consumer health products, we are either trying to test a hypothesis, or measure pre-defined metrics or discover new insights. In all cases, it is difficult to determine in advance whether a specific data source is useful or not. At times, we have found limited value in analyzing content from a specific platform, such as Pinterest.  

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How have you been able to win over ney-sayers throughout your career?

Yes, in a big way. My role in social intelligence often involves overcoming complex challenges and effectively mitigating skepticism of stakeholders yet unsold on the value of social intelligence.

For instance, initially, because of the unstructured nature of social data, the scientifically and analytically minded client community showed low trust in social insights or their utility in driving clinical development or commercialization decisions. To mitigate this skepticism, I co-authored and published at least 7 scientific publications and research papers which were showcased at large scientific congresses and conferences, thus bringing analytical robustness, clinical validity, and scientific rigor to the discipline of social intelligence. Further, I also designed robust analytical methodology to win client’s confidence and trust output. My team and I successfully raised awareness of the significance of social data and promoted its use for research. As a result, in the recent years, I have seen a rising interest in social insights from a variety of stakeholders at pharma and consumer health companies.