Debjani talks about hyper-personalization use cases based on her company’s customer DNA modeling technology.
Sramana Mitra: Let’s start by introducing our audience to you as well as to ZineOne.
Debjani Deb: I am the CEO and Co-Founder of ZineOne. We are a Valley-based company. We started our journey in 2014. The goal of that journey was to provide a solution to what the next generation needs in terms of how brands engage with them.
In this generation, when we are used to standing on the pavement at the airport or train station and be able to get information at our fingertips, what’s the expectations from our retail brands and our banking brands as to how to interact with us?
ZineOne has been at the crux of that innovation in trying to lead the way in what the next generation of consumer engagement looks like.
Sramana Mitra: Double-click down on level. Do some use cases. Who is the customer? Illustrate how you are doing it.
Debjani Deb: In the last 10 years, the primary method of engagement with audiences has been through email and sometimes by push notifications. Fundamentally, it’s been email and a lot of advertising. As technologies have evolved in this day and age of big data and automation, how does that make-up of communication change?
For example, I am walking into a mall. I have the Nordstrom app on my phone. Wouldn’t it be nice that as I walk into the mall, I get a notification that Nordstrom is having their bi-yearly sale and that I have $100 in rewards points available to me?
Today that’s not available. Today you would have to walk into the store, walk up to a teller who would tell you that you have $100 available. If you take that example, it’s the ability to surface the right information or the right consumer at the right time on the right trigger. In this case, the walking into the mall was the trigger.
The value-added information was that I have rewards points to use. Also, it’s a sale day. If you go across industries, our endeavor is to use AI and machine learning to know what a consumer wants in any given moment in time in any given location so that it adds value to the consumer journey.
Sramana Mitra: What are the data sources that you are tying into this use case?
Debjani Deb: As I am walking into the mall, I have the Nordstrom app. The mall has a geofence that ZineOne has deployed around it. When the app crosses that geofence, it triggers an event.
That event comes to the ZineOne cloud system. There’s automation there that instructs it to go check if you are a rewards customer and if there are any events happening today. It will retrieve that information and based on machine learning, it will give it in pieces only if it makes sense at that point in time.
Intent is very important to know because you don’t want to spam the user. Our machine learning algorithms are understanding the intent of the user and giving him or her the right information in that moment in time.
Sramana Mitra: What is the future of retail? If retail is your primary category and retail is getting clobbered in this crisis, what happens?
Debjani Deb: That’s an excellent question. We think of our capabilities and platforms as horizontal. We have banking customers. We have quick service customers. We have e-commerce customers. We have hospitality and EdTech. This capability of understanding intent in the moment using AI and ML is truly a horizontal functionality.