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Dipin Oberoi is a social listening expert who has worked at Walgreens, Ava Labs and Microsoft and recently joined JPMorganChase as a senior social intelligence and insights associate. He will be speaking at Marketing Brew’s upcoming event, The Art and Science of AI in Marketing, on February 25.
Ahead of the event, we caught up with him to hear how he and his teams are using AI for social listening, which you can hear more about live at the event.
How are you and your teams using AI today?
We mainly use it to process the massive amount of social listening data we process. Instead of manually sifting through millions of consumer conversations, AI helps us quickly spot trends and sentiment shifts. We also use it to bring together competitive intelligence and turn complex analyzes into reports that salespeople and executives can actually use. It’s really about moving from insight to action faster, not replacing strategic thinking.
What’s the best real-life application of AI you’ve seen in marketing?
Honestly, predictive spotting trends for retail. We can now observe what consumers are talking about four to six weeks before it appears in search or sales data. We saw certain beauty ingredients gain traction in conversations long before they became mainstream requests. That head start is huge when it comes to planning what will actually end up on the shelves when people want it.
Which AI applications are the most promising? Which are the least promising?
The most promising is anything that counteracts information overload. Tools that can take consumer reviews, social conversations and research reports and actually tell you what’s important. Personalization at scale is also exciting.
Least promising? AI-generated creative that tries to replace human stories. We tested it and it still lacks the cultural nuance and emotional connection. And I’m skeptical of anything that claims it can replace strategy. AI informs decisions, but the judgment still needs a human context.
What role should the advertising industry play in AI risk, ethics and regulation?
We cannot just wait for regulations. We need our own standards, especially around transparency in how we use AI for targeting and insights. The bigger issue is ensuring we don’t incorporate existing biases into our recommendations, especially as they impact diverse communities.
For us, that means being upfront about when AI is involved, keeping people informed about big decisions, and now setting up our own guardrails. The brands that do this well will earn more consumer trust, which is honestly just good business.
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