Mehul Jindal
Marketing Strategy Meets Data Science | Social Network Analytics and Metric Design
Chicago, Illinois, United States
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Mehul Jindal leads marketing for the Americas at Optimas, a global industrial manufacturer headquartered in Chicago. She brings fifteen years of applied marketing experience, including leadership roles at UBS Wealth Management USA and Atlas Network. That background gives her a perspective that is rare in AI research: she has spent her career as the person who has to act on the numbers.
Mehul is also a PhD candidate in Data Science at Harrisburg University of Science and Technology. Her research looks at how AI models use social network structures to predict consumer behavior, and it draws on machine learning, social network analysis, and marketing. The questions it raises matter well beyond academia, for practitioners, policymakers, and anyone whose choices get shaped by a model.
She holds a master’s in Integrated Marketing and Communications from Georgetown University and a master’s in Data Science, with a concentration in marketing and AI, from Harrisburg University. Her goal is to turn research into guidance marketers can actually use.
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Somebody Chose That: Findings and Best Practices for Metrics Leaders Can Trust
Every metric on a dashboard is the end of a chain of decisions, and most were made by someone who never wrote them down. Which average gets reported. Where the cutoff sits. What “engagement” gets divided by. Each one looks like a technical detail, and each one can flip the story a leader hears.
This session draws on two vantage points: fifteen years in marketing, most recently leading marketing for the Americas at Optimas, and doctoral research on how AI models read social networks to predict consumer behavior. I’ll share what I’ve found about where metrics most often mislead. Popular measures like reach turn out to be weak stand-ins for influence. Averages hide the story in skewed data. Small definition choices quietly reorder rankings. From there, we’ll turn those findings into practices you can bring back to your team.
You’ll leave with:
The most common hidden decisions behind business metrics, and how to spot them
Best practices for documenting and defending metric definitions
A checklist for questioning a metric before it ships
Language for explaining these choices to non-technical stakeholders without losing the room
Small Data, Big Decisions: Analytics When You Don’t Have Millions of Customers
Most data and AI advice assumes consumer-scale data: millions of users, clean clickstreams, endless training examples. Industrial and B2B companies live in a different world, with fewer customers, long sales cycles, and messy records spread across distributors and partners. Leading marketing at a global industrial manufacturer has taught me which analytics approaches survive that reality and which fall apart. This session shares best practices for making sound decisions with sparse data: what to measure, what to stop measuring, when a simple model beats a sophisticated one, and how to be honest with leadership about uncertainty.
Takeaways:
Common analytics practices that break down in small-data environments
Approaches that hold up with limited records and long cycles
How to communicate uncertainty so leaders still feel equipped to decide
Mehul Jindal
Marketing Strategy Meets Data Science | Social Network Analytics and Metric Design
Chicago, Illinois, United States
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