What is your job title? How do you use social listening in your work?
I am a senior research manager at Microsoft. I’m part of the Social Intelligence Practice (SIP), which is part of Microsoft’s Research + Insights team.
It’s the ‘Age of Copilot’ at Microsoft and the ‘dawn of the AI era’ more broadly – and social listening is perfectly placed to help make sense of this moment and also unlock its technical potential within our workflows.
My work swings from the very granular and the very technical to a 30,000 foot view at a moment's notice – and I love it! It’s so rewarding to partner on big company-wide initiatives (like the State of AI, Brand Love, Product Growth, Security) and also provide quickturn insights on breaking news (X.com, OpenAI, Xbox, Copilot) that I know leadership and my peers both value.
Beyond subjects and stakeholders, I’ve been leading the methodological development efforts within SIP. During my tenure, we’ve seen the success and expansion of experimental semiotic analyses and nonroutine pulse reports turn into sought after programmatic research for the company. Leveraged by engineers, execs, creatives, comms, researchers and retailers alike.
Since it’s the ‘dawn era’, SIP is also prioritizing how we can thoughtfully leverage AI – like efficient query curation, emotion classification and prompt-based discovery. Through experimentation, I get to build great visuals, craft strategic insights, highlight compelling verbatims and spotlight hilarious-yet-poignant user-generated content in new, exploratory ways.
These efforts push the SIP forward and, in turn, help Microsoft meet our customers’ needs.
What’s your background? How did you get into social listening?
I started my career in the research + analytics department of a communications agency in San Francisco, CA. It was a great way to gain experience implementing qual + quant research methods; we prioritized tailoring our methodology to understand the specific question at hand. In turn, I was trained to lead focus groups, conduct in-depth interviews, draft surveys and dig into big behavioral data – and, yes, explore social data.
I found that my outputs were strengthened by the inclusion of social insights. I always recommended including them as a part of a research proposal. It provided a rich, unprompted view – which I loved weaving alongside more traditional research methods and data science figures. (I think of that elephant metaphor – different methods are ears, tusks or tail; all together they give a more complete picture of the elephant.) That said, I loved the evolution, thoughtfulness and creativity of social more than my research that centered on predictable SPSS cuts.
Social intelligence, in my opinion, can be one of the most fun and exploratory research disciplines. From memes + emojis to linguistics + analytics, it is full of untapped possibilities. It can go back years or be up to the minute. Parameters can be customized – looking wide and conceptual or narrowing in on a specific trend, feature, product or brand.
I meandered away from the social intelligence discipline to gain a broader experience with other data science + research methods – but I’m thrilled to be back exploring social data full force at Microsoft.
What has been your biggest achievement?
At Microsoft, my biggest achievements have been providing emerging insights to leadership – both in near real-time as decisions were being made as well as providing a longer historical view to inform long-term business strategies.In either case, social intelligence gives them a fuller, thoughtful context to better understand our industry, products and consumers.
For example, COVID-19 first came on my radar as a serious topic in early 2020, as I kept my eye out for emerging trends. Due to the nimble nature of social data, we were quickly able to build out daily reports around the topic, followed by deep dives into new terms gaining speed like ‘social distancing’ or ‘telehealth.’ As lockdowns occurred and traditional in-person research was disrupted, appetite for these reports in grew. Our team + suppliers rose to the occasion. We proved over and over the value social intelligence has around trendspotting, checking social reaction to fast-moving stories as well as providing product and brand insights.
Today, we continue to keep a close eye on emerging, transformative topics on the horizon, and our social data research has subsequently helped our stakeholders, colleagues and leaders understand emerging topics as well as product launches, name changes, industry events, product deep dives and much more. The team at Microsoft has grown – as has demand. Each new report and each new team member makes me proud to be a practitioner in this growing and important discipline.
What’s the boldest mistake you’ve made? What did you learn from it?
Ooh, I think the boldest mistake would be building an audience lookalike model – which was an incredibly comprehensive and thorough endeavor. I was so so excited about it. The techniques were cutting edge and I had cross-functional support, newly vetted data sources and backing from data engineering. It was going to revolutionize our processes!
However, weeks later, I came back to reality after I saw the results.
I realized the model’s output provided 1) ‘no duh’ insights and 2) it’d be incredibly tough, time-intensive and expensive to implement. Oof. Bad bad bad. It would, in fact, not be revolutionizing our processes. And, while we did get some use out of the work, the project wouldn’t be providing the paradigm-shifting impact I had been hoping for.
My biggest takeaway from that endeavor was when kicking off a project I now prioritize thinking about how to leverage the learnings. I repeatedly ask myself “So what?” and “Now what?” while in the design stage of research, and not just “We can!”.
What would be your dream project to work on?
The projects at Microsoft have been awesome to work on! I’m never bored. Great questions to tackle with great colleagues, stakeholders and suppliers. AI! Quantum computing! TikTok! Open source! Hard to ask for more.
However, since we’re talking dreams here, I’m currently obsessed with the Las Vegas Sphere – so I’d love to conduct social intelligence research for the venue, providing insights on their projections, concerts and memes. Perhaps a future Copilot x Sphere collab?
Regardless, fingers crossed for a Beyoncé residency.
Do you think there’s a right way and a wrong way to use social data?
Absolutely. As with all data + research, I think it paramount to provide:
- Good stewardship. I work hard to prioritize dignity and compassion in the work that I do, with whom I collaborate with and to whom my research may impact. IMO, if we lose sight of these principles, poor and thoughtless usage will hold us back from unlocking strong future insights.
- Good context. I always think of the Dr. Who “Is four a lot?” meme. Four dollars…no, but murders?? It reminds me that information in isolation is ultimately a disservice to my colleagues who will be leveraging it. Social data is no different.
Are there areas where you think you should be using social data for but aren’t currently?
Ugh, there’s only so many hours in the day! Our team’s ‘thoughtful no’ research list is lengthy – and I wish we could tackle every worthy request and idea.
What’s your favourite data source to use and why?
Long form! NLP techniques for interpretation are stronger than ever. Which is great – since industry shifts in the 2020s have ushered era of fewer character limits, aka longer posts. :)
That said, I’m excited to take on more audio + video sources in the coming months.
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