This is a series of three articles based on Debjani’s conversation with Sramana Mitra, founder and CEO of One Million by One Million (1Mby1M), a global virtual incubator/accelerator.
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, 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.
Sramana Mitra: Let’s do a couple of other use cases. Give me a use case that is online. Let’s do an e-commerce use case.
Debjani Deb: Let’s take an e-commerce vendor. You are shopping on Overstock. You have taken the first click. You landed on the first page. You’ve taken the second click, and now you’re on the product page.
The machine learning algorithms that ZineOne has deployed on the website of Overstock is looking at your individual browsing right now. By the fifth click, it’s determining with about 90% accuracy whether you are going to checkout in that session or not.
If that algorithm determines by the fifth click that you are what we call the on-the-fence audience, it can nudge you with something that will incentivize you to go further in your customer journey. That nudge could be a loyalty nudge, a shipping nudge, or it could be an offer.
Essentially, this is very cutting-edge because thus far, there is no competition. Some of the big companies are doing it on their own. They are deploying what we call ML algorithms on a stream. Data hasn’t been stored yet.
Think about how fast this needs to be. ZineOne determines if the customer is not going to complete. That enables some sort of an incentive or a nudge. We’ve seen 12% increase in revenue on these kinds of things between test and control groups because you’re able to nudge them to completion. That’s an e-commerce use case.
Sramana Mitra: At the heuristics, what is driving the conclusion that you’re making? Out of five clicks, you’re determining if this person needs a nudge or is going to complete. What is the input?
Debjani Deb: This is a patent-pending technology. We’ve filed five patents on this. We call it the Customer DNA. Essentially, it’s the consumer genome mapping. I’ll talk a little bit about it. You know that the human genome has four nucleic acids that make up 32,000 gene sequences.
We’re using that analog as a framework to look at the clickstream data. We are mapping millions and millions that are coming into our cloud to understand what set of sequences or what set of behaviors displayed lead to what set of outcomes.
We are training our ML model through this notion of this consumer genome mapping to understand what displayed behavior will lead to what outcome.
Sramana Mitra: So you’re doing pattern matching. You’re tracing the logs and looking at the results of those logs on what happens. Then you’re drawing conclusions and learning based on that.
Debjani Deb: Essentially, the only nuance here is that it’s being done as a real-time stream. You’re seeing past patterns and sequences.
Sramana Mitra: You need the past data to do the pattern recognition and the heuristic tracing. Even before learning, you’re sorting the real-time data into buckets and then the learning algorithm is learning more on top of that.
Debjani Deb: Exactly.
Sramana Mitra: You said banks are also part of your customer base. What do you do for banks?
Debjani Deb: We think of ourselves as direct to consumers. The same principles apply, which is to say that I’m looking at somebody applying for a car loan. I’m looking at the behavior displayed. I’m making a call with regards to how I can add value to that consumer journey.
I am looking at somebody who is trying to pay a bill and they look confused. They’re about to call the call center. I can pop up. The algorithms are running in the background. This is very different from the popup technologies that exist today.
Your most important desire is not to spam the user. You will do this maybe once every day. There are controls in regards to how often you can interject.
Sramana Mitra: Is the popup customized to different clusters of behavior?
Debjani Deb: Typically, it’s not a popup. The way it renders is that the website itself changes. You wouldn’t even know that it changed. We try very hard to keep away from any kind of hard intervention. The site is changing based on what you are doing.
To answer your question, it’s done at an individual level. What ZineOne is doing is taking that analog of a human DNA. We are maintaining what we call a customer DNA. Every click that I have done either on the website, app, or store is my DNA for Nordstrom for example.
Nordstrom is maintaining a customer DNA of a customer’s activities across all digital endpoints at all times. If NordStrom is going to intervene on my journey, the intervention will be 100% personalized to her journey and her journey alone.
Sramana Mitra: Is there any variation on what we have discussed already that illustrates any other type of use case based on other types of customers, or have we done a sufficient job?
Debjani Deb: There’s one very exciting variation that I’m excited about. This is channel agnostic. It is being deployed around apps and websites, stores, and kiosks.
The most recent rendition has been an integration with Alexa. Voice is becoming very prominent with regards to personalization. For us to trigger a voice command is the same as triggering a message.
Recently, we integrated at one of our hospitality companies. If you walk into the room of this hotel, the TV will greet you. It’s channel agnostic. We are looking forward to this in smart cars. The decision engine of what you need versus what I need remains the same. The rendition of that could happen on any screen as 5G evolves.
Sramana Mitra: Very interesting. What do you see as adoption levels of your kinds of technology in your target customer base?
Debjani Deb: We are creating a category. We are creating the next generation of what is possible in consumer engagement, which is driven by heavy intelligence at the edge. Therefore, the adoption is with customers who are the leaders of each vertical. We are seeing adoption at the top of the curve. These are folks who would have built it themselves. They will lead the way.
Our strategy has been that we are going to go to the most mature and most sophisticated brands across verticals and have them adopt it. That’s what we are seeing. In the last 18 months, things have changed dramatically.
Sramana Mitra: What do you see as open problems from your vantage point?
Debjani Deb: We’ve just touched the tip of the iceberg. I imagine a world where your customer engagement endpoints are proliferating at a significant rate.
If you’ve ever been in a Tesla, you know that the entire middle screen is the computer that is driving around. McDonalds bought this company called Dynamic Yield for smart menus. You can only imagine the proliferation of smart endpoints in the next five years. Big data is a given. It’s a thing of the past.
Between those three things, there’s the need for automation to harness that level of data and intelligence so that you can add value to a consumer. There’s just a spectrum of possibilities. It’s endless. ZineOne is doing some of it. I want every consumer engagement to be location-aware, environment-aware, and moment-aware.
That’s just the beginning. Entrepreneurs in the next five years need to think about the spectrum of possibilities with new bandwidth and intelligence endpoints coming online and how they can add value.
Sramana Mitra: Excellent. Thank you for your time.
This interview was first published in One Million by One Million Blog in May 2020.