Priya Raman Answers Big Questions with Big Data

When businesses have questions about consumers’ spending habits, Priya Raman answers them with sophisticated analysis

Priya Raman has always loved data. “The numbers love me and I love numbers,” she says. As she prepares to transition into a role with a Seattle-based tech company, Raman frequently reflects on her personal passion for data analysis, which she cultivated by getting a master’s degree in economics. “It was always the data, the statistical part of the economics that really excited me and interested me,” she says. “Any organization that I joined, I tried to gauge whether they also looked at data as front and center, and then only joined organizations that saw data that way.”

Raman began her career as a data expert doing market research for Nestlé and other companies. Her work helped consumer brands answer two questions about their customers: Why do people buy what they buy? And based on those motives, what decisions are customers likely to make?

“They always wanted to know why consumers weren’t responding to their advertising and how they were doing in the consumer’s mind vis à vis their competition,” Raman says. “It was really cool to help bridge the gap between what the consumer was thinking and how they were behaving, put data around it, and help marketers make decisions.”

That market research experience helped Raman transition to in-house data roles at companies such as General Electric and HUB International, the insurance firm where she worked most recently. As an in-house data and analytics expert, Raman had a richer understanding of how her work applied to the business.

In turn, Raman works hard to deliver actionable information for the businesses that put their faith in data. Rather than working in isolation and experimenting for its own sake, she provides value by making sure the data can contribute to the revenue or cost directly.

To be able to achieve that, she argues that analysts should understand myriad sources of data. “If you want to do true advanced analytics, you can’t just look at one or two sources of data as being your bible truth,” Raman says. “Whether it’s internal or external data, understand it, document it, play with it. See how you can start using it.”

At HUB International, data lakes (repositories that accommodate various formats and sources) rather than data warehouses became Raman’s go-to platform—a common trend in the industry. By doing so, teams are able to bring in all kinds of structured and
unstructured information, put it on the cloud, and take elements out if they’re not relevant anymore.

Data lakes, Raman explains, allow businesses to analyze a wider variety of sources than was previously possible. “That breadth of information allows companies to understand the customer and the behaviors much better than we would have done with our own internal data,” she says.

By integrating multiple sources of information into a data lake, Raman can generate advanced analytics that go beyond simple measurements. Data has three tiers: the “what” (measurements of a business’s activities in a specific area, like sales), the “why” (the reason that the business is thriving or struggling in that area), and the “so what” (the alternatives that the business might explore for changing outcomes). Advanced analytics involve the latter two tiers of data.

Raman’s talent for data analysis not only sheds light on consumer habits, but also helps to define strategy. At HUB International, Raman’s team innovated by developing propensity models, which predicted what certain sets of customers would likely buy. For example, a customer who buys home insurance might be likelier to buy flood insurance—and, knowing that, salespeople could improve their results.

Helping her peers stay profitable is part and parcel with Raman’s passion for numbers. “I’ve always looked at my function as being a strategic function,” Raman says. “I need to understand how can I improve the top line or the bottom line for the company.”