Sathyaraj Asaithambi

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

I'd tell my younger self to embrace the messiness of social data. It's tempting to seek perfect order, quick insights but the true gems often lie hidden in the noise. Learning to navigate that chaos, to listen beyond the surface, and to uncover the nuanced human stories within.

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

As my organization’s priority is to create an impact on the maximum number of patients as quickly as possible, I feel proud that I did not want to quit anytime during my journey as I am in some way involved in supporting the vision. Pursuing social media strategies will require a more agile mindset. These types of modern research require a lot of experimentation with new tech and platforms before rolling them into operations and require solid big-picture thinking while recommending actions.‍

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

Tech plays a crucial role in data acquisition, data wrangling, and visualization of complex unstructured data. People still remain at the heart of the process - understanding of the pharma market, competitive landscape, customers' needs, interpreting algorithms outputs and importantly storytelling and actionable recommendations.

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Who have you seen as a mentor in your career?

Inspiration rarely comes from a single source, and my growth wouldn't be possible without a diverse network of mentors. Collaboration with colleagues, senior leaders, my managers, my team, white papers, publications, online communities, books, etc. At times, I consider data itself a mentor, constantly revealing hidden patterns and challenging me to interpret and translate.

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

I once got a little too excited about the data! In a feasibility study, I confidently declared we could answer all the key business questions using social data. But later, I discovered a crucial flaw: the data was primarily from the US due to limitations in geotagging on a specific platform. My initial analysis painted an inaccurate global picture. It forced me to be meticulous about data sources, filters, and limitations. Be bold, be curious, but never be afraid to admit when you're wrong.

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How do you see the future of social listening evolving?

In my view, uptake of social insights usage across the organization would be sophisticated to aid the understanding of patient community, predicting what customers are next going to demand of us, bolster omnichannel engagement strategies, combating misinformation, pairing social listening with edge computing concepts, generative AI to garner valuable insights. 

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

Patient online community boards, ATUs, real-world datasets, and omnichannel data are my favorite data sources to effectively complement and enhance Cx/brand strategies. Few of the closed channels (paid data) are overhyped with a lot of noise in the data.

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

Case studies are my weapon. By presenting real-world examples of how social listening would help address KBQs, discern insights and even save costs, I demonstrate its tangible value beyond theoretical promises.

Building a community with like minded people, creating tailor messages/narratives for various audiences ensure your message resonates.

As social listening study delivers strong actionable insights, I share the testimonials in larger fora, from industry awards to client testimonial, these validates and amplifies one's leadership. I tried highlighting the positive change SML enabled for different diseases/markets.

By staying focused, data-driven, and collaborative, I earned my place in the field, and that's a much more satisfying victory.