CMO vs CIO: Why it shouldn't be a battle
Zeta Global uses its vast database of profiles and technologies like artificial intelligence to help companies improve their marketing return on investment. Customers include British Airways, American Airlines, Citizens Bank, Ralph Lauren and Sprint. ZDNet talked to the company's CIO, Jeffry Nimeroff, to find out more about the company's plans and priorities.
ZDNet: Tell me a little bit about your company and the focus of the business.
Nimeroff: Some 10-plus years ago our founder David Steinberg and our co-founder [and ex-Apple and Pepsi CEO] John Sculley saw the power in the future of data as it related to marketing challenges and broader challenges.
They looked to form an organization that was driven by data to perform marketing execution. Over time, Zeta has worked to generate relationships with individuals on the web through first-party data sites -- meaning websites -- that data controls. The idea was to do that along with forming partnerships with large publishers, so that we could construct a dataset that is more powerful and more uniquely positioned than any other in the market.
To go along with that, they took the more standard path of building the proper technology platform to activate data.
That gives us, in effect, the tech platform to market our message to individuals, but also a dataset that is unique and lets us know what we should say to specific individuals at specific times.
And we can work in both retention and relationship marketing -- the more standard path -- as well as on the acquisition side.
The retention path is, say, an airline sending their monthly newsletter to their customers and maybe they offer coupons to continue to be a customer. The acquisition side is, obviously, fraught with a little more challenge. How do you find prospects? Address prospects? How do you know you are doing it well and efficiently?
Can you give me an example of how you have helped a company in this area?
It's really what we do daily. Our clients come to us with a varying level of expertise they are already potentially good at the areas in which they work.
They will have a nice, first-party list. They know their customers well. They gain insights from what we call 'signals' in which customers and an organization interact. And because of that, they're able to put together what they would call a 'cadence' around marketing -- it's this message at this time.
The things they are good at, we are good at automating -- so it's simple for them to execute based on the things they know about their customers and their business.
But we also -- because of our data science group, artificial intelligence and so on -- probably mine their data better than they can, which increases what would be a good ability on their part to execute. It increases that. So that ability can give them better means to target and micro-target as opposed to macro-target.
You have bought this other organization, Disqus. Can you tell me about that?
When we know an individual we see them in the context of our ecosystem. They respond to emails, they visit first party websites, they show a disposition in terms of what they like and don't like and so on. That is the beginning of a signal-like ecosystem. We have always wanted to add more data, more richness.
So Disqus, while being a commenting platform -- it provides great publisher tools for managing commenting -- is also a first party website and web experience.
When Disqus is deployed on a publisher site, I can go in and without [the] publisher having to set up any infrastructure I can comment on sites. The mechanics of commenting are provided but the person wanting to use them has to log in.
The discussion is not anonymous. At some point you need to represent yourself as an individual and, because of that, we have a key -- usually an email address -- to start to link commentary back to the ecosystem as well. I can now see and, do things on the internet. I can see and interact with emails, discussions and comments all across the ecosystem.
That is really powerful -- especially when you start to use AI and start to use just commentary, such as "he likes this, he doesn't like that." And Disqus, because of its penetration -- 4.5 million websites and growing -- is gaining more information.
As a company, are you concerned about perception around social media and data collection?
I think a lot of times the notion of value versus threat comes from the unknown. I think with Zeta and Disqus and organisations we might partner with, the chain is very public in how we execute. We are very public in what we do and how we do it, and what our success criteria are.
We're very explicit in our security and compliance as measured by certifications that are broad in general through to very specific. And we are checking and testing these things on an almost-daily basis. Working within that construct helps. But we know that's not perfect.
On the flip side we have to be realistic about a set of any population that wants to interpret data gathering as bad. And we're unsure about what can be done about data gathering. Even if it's not actually bad, people can still have the general belief that it is. It's really the group in the middle where you present all the proper procedures, controls and compliances. You present what Zeta does and how it does it. And then you start, or try, to move them through the presentation of facts.
We don't sell data. We activate data, meaning we're the ones ingesting data. Our goal has been to be successful in helping our clients message better. We kind of layer that way.
Can you talk about what plans you have going forward?
We are looking to broaden the question of 'what is a signal?'. We are looking to work with high-quality organisations that capture internet signals. We work across what we control in the ecosystem to capture more granular signals. Again, we don't sell data, we use the signals to enrich profiles and those profiles are segmented so that appropriate marketing messaging is deployed.
The IoT is a really interesting, signal base for us. It is part of the ecosystem that is becoming much more focused. And it's like when you get a signal from a general device, say someone browsing a web page on a phone versus the way they interact with their smartwatch as a heart monitor. To me, that's the next level.
We see our ability, in a compliant way, to capture signal information as really being a differentiator and it's the next wave. You know we went from big data to bad data and now the move to smart data means that we are getting too much data, so how do we work in real time and do it effectively and in real time?
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