Jeffry Nimeroff, Chief Information Officer, Zeta Interactive
Jeffry Nimeroff’s introduction to data occurred at Boston University, where he declined a standard major program and pursued an intensified program in graphic design, which connected him with people who were studying databases.
“I was a computer graphics guy,” he says, and I met with people who wanted to know, ‘how do you store data?’ I applied common data science rigor in my dissertation in the area of computer graphics,” Nimeroff says. “Trying to find large similarities in pockets of data.”
Many of his college cohorts went into the movie business. His school, he says, probably has alums that have won hundreds of Oscars in technical achievement, the type of awards given during the pre-show the day before the broadcast.
“90% of them went to California to make movies, and I went into the multimedia Internet,” he says.
His career took him to a number of places, including stints as manager of technology integration at CDNOW and chief technical architect at Bertelsmann’s BeMusic.
“Various stops in my career put me in touch with people who taught me how to look at the quality of the problem as well as the quality of the solution,” Nimeroff says. “It’s part of my DNA to [enjoy] challenging problems and find critical thinking around those solutions.”
He now employs that philosophy at Zeta Interactive, which he joined in 2014 as chief information officer and runs the technology operation.
Zeta is a provider of big data solutions to multiple large brands, including AIG, Toys R US, UPS, and Progressive. Zeta Interactive just raised $45 million from GSO Capital Partners (a unit of Blackstone Group) and PNC Bank. The company provides “actionable data, advanced analytics and machine learning to help brands acquire, retain and grow customer relationships,” according to Nimeroff. The company’s proprietary profile database has upwards of 300 attributes per profile.
“We wanted to build the leading data-driven marketing company – one that worked at the intersection of Big Data, analytics, and technology to make data actionable at every touchpoint,” he said.
Nimeroff says that Zeta’s data and tech team organizes itself through a single implementation process with shared responsibilities. By that, he means there isn’t a silo between product enhancement and product/customer support.
“The notion of the IT help desk person who seems like they’re entire world is around support” never actually works that way, Nimeroff says.
Companies think that if they have one guy that tackles a challenge that comes in, then it frees up the other team to do the fun, proactive stuff. But Nimeroff says it never works that way.
“That one person gets overrun by the very nature of the problem and the people who were doing the proactive stuff gets called in to put out the fire,” he adds.
And when dealing with complicated algorithms and data modeling, it’s imperative to have constant communication with the sales team.
“The sales team has great conversations around the problem that our tech would be solving on behalf of their prospective list,” Nimeroff says.
“We have strong sales people and they know a lot about the products offer,” Nimeroff says. “They should know a bit about ‘the how,’ and who they should call on the phone tree when they get [deeper] types of inquiry.”
Personally, Nimeroff says the challenge of identifying solutions to marketing problems is an enriching task.
“Presenting the right message to the right person in the right channel and the right context is still a tremendous challenge to do consistently,” Nimeroff says. Success percentages are low which is why such a broad and deep approach to marketing still dominates the landscape.”
He adds: “Converting raw data to filtered information to inherent knowledge is a challenging and rewarding endeavor no matter what domain the data exists in. I have to admit that I like analytics, as an art and a science, in general: The math, the algorithms, the exploration.”
His philosophy is: “It’s not just about the great solution, it’s the great solution to a great problem.”
This is article is part of a series of profiles of data scientists and CIOs at analytics firms