Aditya Bhamidipaty Transcript
Aditya Bhamidipaty joins host Brian Thomas on The Digital Executive Podcast.
Welcome to Coruzant Technologies, Home of The Digital Executive Podcast.
Brian Thomas: Welcome to The Digital Executive today’s guest is Aditya Bhamidipaty. Aditya Bhamidipaty is the Founder and CEO of FirstHive, a global customer data platform. He has been a serial technology entrepreneur solving problems for marketers.
His stewardship has helped FirstHive to be among the first few to imbue machine learning into customer data profiling, delivering six times ROI to some of the world’s largest brands on their marketing initiatives. Aditya started his career with Procter Gamble. Where he played a pivotal role in turning around a loss-making region into a profitable one in record time, which then became the highest growth area for PMG.
From there, he moved on to join iGate, a global technology and consulting solutions provider in London. He was responsible for the UK and Western Europe region before moving back to India for his first startup gig. He was the co-founder and CEO of eSmart Solutions, which grew into one of India’s leading loyalty and engagement companies. He exited E Mart in 2015 to work on a new product, First Hive.
Well, good afternoon, Aditya, welcome to the show!
Aditya Bhamidipaty: Thank you, Brian. Very good to be here.
Brian Thomas: Absolutely. Thank you for making the time and hailing out of that great part of Cupertino, Santa Clara area in California, close to that Silicon Valley area.
We have a lot of guests from there on the podcast, so I appreciate you making the time and Aditya, we’re going to jump right into your first question. Can you share what inspired you to transition from corporate roles at P& G and iGate to founding your own companies? What were the key challenges and milestones in this journey?
Aditya Bhamidipaty: Yeah, thanks, Brian. You know, I’m very excited to be on this podcast. My journey of entrepreneurship formerly began with me quitting IGate to starting my own startup gig. I would say my, my real kind of commitment to the idea of being an entrepreneur, was probably seated in me very early as a, as a young kid.
And I still remember actually being in engineering and wanting to do my engineering because I thought that would give me a good foundation for something I wanted to do later. So, I’ve had this entrepreneurial bug in me, if you can call it that, for a really long time. P& G was an incredible experience for me.
It was a, what I would have, I couldn’t have asked for anything better than the kind of role that I had at P& G. But quitting P& G to me was really one of my first kind of moves to commit towards entrepreneurship because I gave up on what I thought was an incredible career that I had at P& G, a great start and took up something which is very, very different from what I did at P& G.
To move to a system integrator like I get and running it in Europe. But one of the things that was common across my PMG, and I get days with, I was going to very similar kind of industries at PMG. I was with a large consumer CPG company. And then at IGate, I was basically selling to retail telecom media, and I had very similar companies as PMG as my clients.
So, I got to see the same broad, you know, opportunities, challenges. But from a technology perspective, something that I’d seen from being within PNG in a sales and marketing role. And then eventually my first gig really was born out of these two experiences. And then when I started e-Mart, it was me going back to something I had done very early on in my PMG days, which is to be able to support enterprises on their trade, marketing and loyalty.
And that’s how I co-founded my first company and took that to some of the largest enterprises in retail, manufacturing, banking, healthcare, and so on. And we did some incredible programs around customer engagement, loyalty, in fact, grew it to be among the top three, top five players in the space. Grew it to be like a 200, almost a 200-member company.
And yeah, so that was, that was very, very exciting. Multiple milestones that we traversed on that journey from raising our first capital to the first customer win, to going beyond starting to become a company where, which, you know, which is when you go beyond 10 people, 20 people. And then really getting into a scale mode when you have 100 plus clients and 150, 200 people team.
So very, very interesting I would say journey as an entrepreneur.
Brian Thomas: Thank you. I appreciate that. And you know, a lot of people do have that bug. They obviously start out and get some great experience at some great companies. But I like your story, the excitement in your voice about, you know, the passion and the growth of your baby.
Basically, you’re raising a baby as your own company. And I really love those stories. So, I appreciate that. And Aditya, with your background in enhancing customer experience, what specific gap did you see in the market that led you to the Hive? And how does First Hive address this gap?
Aditya Bhamidipaty: You know, that’s a great question.
To be honest, I think all of my experiences before First Hive were all contributing factors to First Hive because I was witness to the same opportunities and problems, but from different perspectives across my three different experiences prior to First Hive. And that was how companies were operating at their intersection of data, technology, and marketing.
for listening. Especially through my agency days, which is the e-Mart company that I’d co-founded. We had seen it very closely because we were running operations and we knew we saw how much opportunity was left on the table with all of the data that was getting generated, but how enterprises were struggling with being able to consume the data or being able to capitalize the intelligence that was getting generated to be able to use it and monetize it going forward.
Instead, most of the customer experience engagement programs were very, very isolated from most of other marketing initiatives. Data was isolated and scattered and therefore, the first time was really, it was very obvious to us as we had scaled in our business that there was tremendous opportunity that enterprises.
Left on the table around taking control of that data and not expensing out all of those initiatives. But really, can we create meaningful assets out of that and help them deliver consistent customer experience, you know, and having been in the loyalty space? One of the things that we very strongly believe was, you know, if a brand was to expect loyalty, it’s almost table stakes for the brand to be able to demonstrate loyalty to its consumers first before you demand loyalty.
And how do you demonstrate loyalty? I mean, and the first step in being able to demonstrate loyalty is at the very least recognize the consumer before you know, expect or demand loyalty from the consumer. And that was really where the whole idea of how most marketing still tended to be broadcast with very little to do with the consumer and really more to do with channel optimization was something that we, we had our own Eureka moment.
And as we were doing that. And that’s how we really started to build our prototype. We actually called our prototype, the God system, a little pun on that, but God, as we called it was G A W D a good analyzer of white data as a true engineering grad. And what we basically said was that there’s so much data that was getting generated, this was no longer the error of data paucity, but really an error of a data opulence, but insight paucity.
And if you had the right antenna, you can translate that data into insights and really put it to meaningful use. But if you did not have the right antenna to process it, if you did not have the right methods to extract, consolidate and make it useful, it was all just white noise. And that is where we said FirstHive was going to start making the difference.
And that’s how FirstHive was born. What we did at FirstHive was ask ourselves, how does an enterprise truly start taking control of its customer experience to drive ROI? And we broke it down really into, into three steps. We said, first, we need to be able to unlock and acquire all the consent data that consumers were giving to enterprises.
The enterprise therefore had legitimate access to, but it was not able to either bring it in or it could bring it in partially, or it was scat even after bringing in, it could not connect with the other data sets that it was, it was already having. So how do you bring the data in? And then how do you connect this data?
Because the way we saw it, Brian, is that at every interaction between a consumer and a brand, there’s an explosion of intelligence that happens. Right. And if only you could bring that in, you can now start having meaningful conversations. You know, my favorite example of that is if you walk into a supermarket and, you know, you see a Gillette razor on the aisle, the razor says, Hey, Aditya, I’m a great product.
But it says that to everybody who passes the aisle, it says, I’m a great product by me. But what if it could say, Hey, Aditya, welcome back! This is the 15th anniversary of your first shave. Suddenly a broadcast becomes a conversation and how can we help enterprises do that? So those were some of the drivers for us as we started building FirstHive and.
Given that we were focused on consumer, it was natural for us to conclude that in the consumer world where there’s millions of consumer interactions that are happening in real time, if you could not process the data in real time, and if you cannot use probabilistic methods to connect the dots, and if you cannot have the machine do this, But you still depended on the human to do this manually.
You will still be prone to the same game of chance probabilities. And, you know, marketing still continues to be one of the low conversion industries. When you look at the amount of communication that is thrown away and what percentage of it actually leads translates into a transaction. And that is where first I really came in and said, how do we help enterprises take control of the data, build those unified identities, and then allow enterprises to be consistent and cohesive to drive conversions. And that’s what I really brought in first.
Brian Thomas: Thank you for breaking that down. And I liked how you started out the conversation with, you know, you need to demonstrate to the customer before you demand their loyalty.
And then, you know, if you can get the right and direct message, personalize that message, you mentioned the Gillette razor as an example. And I think that’s awesome. And I’m glad you saw a gap there and jumped in. So, I appreciate that. And Aditya, if you could briefly share. FirstHive was among the first to integrate machine learning into customer data profiling.
Can you discuss how this technology has evolved within your platform and the impact it has had on your clients?
Aditya Bhamidipaty: Sure. See legacy systems, or at least most marketing tools took a very, you know, what I would say is a CRM approach to customer 360, which is you would essentially use primary keys of email or mobile to connect to different data sets.
So, what most tools would do really was glorified VLOOKUP’s on the basis of primary keys to connect and merge two data sets to arrive at a single data record and call it a customer 360. But there are some fundamental challenges with that kind of approach that tools took. Most legacy tools found this convenient and were constrained by their technology, you know, that was being deployed.
But if you go down to first principles, what a single record customer 360 implies is that each one of us as consumers will always operate at the averages of our aggregate behavior. And that’s simply not true. Each one of us as a consumer is always driven by the context with which we come to a brand.
And it is in the, through the prism of that context, that our actions and behaviors have to be understood. We are not always the same. We are not always operating at the averages. This is crucial when you are designing a system that needs to play in real time at scale And also one of the components of our vision was that eventually all of customer experience will have to be autonomously driven, at least at a baseline by the machine, almost like your copilot and then allow the human to kind of come in and orchestrate on top of it.
Now, if the machine has to be able to do that, you need your data in a manner in which it is machine friendly and it is as granular as you can get. So the insights that you have on the customer or the consumer are contextually relevant to what the consumer is doing now. For that, a single merged record was meaningless.
And therefore, when we started building first type, with identity as the core of the platform, what we did was take a completely different approach, but an approach that was true to human behavior, which is to look at every interaction as a unique interaction driven by context, and then to deliver context enriched identities and really create a trail of interactions.
Because we are all a collection of discrete interactions with a brand and each interaction driven by a context and then derive what the consumer preferences are and make sure. And when you do this model, you cannot just use deterministic VLOOKUP methods, but you have to look at probabilistic method because All interactions that are happening.
You don’t have the same identity. You don’t have all the identity vectors. You have partial information coming in. You have some anonymous interactions and so on and so forth. So how do you still make sense of all of that? And then really bubble it up to feed the machine, to be able to allow for autonomous delivery of customer experience.
And that’s how first I really changed the game in the way it brought identity to the core of understanding consumers. And with that, Brian, the biggest difference is that now enterprises do not have to think about list optimization or channel optimization. But I can really start thinking about which, who are the consumers that are right for this communication and who I should reach out to and how do I really personalize it to the node, which is at a segment of one and you are making sure that you’re able to do it at scale.
So, it’s almost like everywhere, every time, but with everyone, you’re able to be unique and personalized and in real time. And that’s the big difference that we brought in. And because of this, the impact is humongous. Thanks. I would say the impact, you know, at a high level would, there are three big areas of impact.
First is unlocking data. Humongous amounts of data get unlocked because when we come in, we’re not just resolving identities, but actually unlocking identities, which are all scattered in different systems. Two, we are delivering the richest, most granular identity trails that are, that become available because we deliver these prime series trails, and we maintain that as the persistent identity layer.
And third, We’re allowing for portability of all of this data and insights from a customer experience standpoint, which means not only are you able to do all of the segmentation, campaign management, journey orchestration, or being able to push this data to any other destination system, but you’re also able to make AI models read of this, you’re able to train your LLMs, you’re able to bring in other applications to read of this data.
So, you really start embedding the brain in the body or what was before a multi headed Hydra, and you’re now making it a cohesive organism. That’s able to recognize and deliver cohesive communication to its consumers. So huge impact all the way from knowing, unlocking data, understanding your user to delivering ROI and customer experience.
Brian Thomas: Thank you. I appreciate you breaking that down. Aditya, you’re right. In order to get it right with the customer and to leverage all that data, you need all the data. And of course, using a large language model is so important, but you have to move to that probability to understand the customer and where this might go.
And you can’t do that as a human, as you know, with all the different data points we collect these days. So, I appreciate the share. And Aditya, last question of the day, if you could briefly share, you’ve spoken about the power of hyper personalization in marketing. Could you explain how FirstHive enables this at scale and perhaps share an example of its success?
Aditya Bhamidipaty: Sure. You know, hyper personalization is the easiest way to think about hyper personalization is how do we make sure that we are really having one on one conversations between the brand and the consumer, because each consumer could be at different points in their own engagement cycle with a brand.
There are different points in that purchase cycle or advocacy cycle. And how do you make sure that you’re not sending one message to everybody, right? So, if you think about where marketing was 30 years back, it was one billboard on 101. That would probably put the same message to everyone. Versus as you started going through the media, you had surrogation.
And then you went in through digital media where you had more surrogation and segmentation. And now. As you’ve taken control of first party data, you really know your consumer. Then can we talk about meaningful, contextual messaging? Can we, can we ensure that there’s meaningful, contextual messaging to the consumer and not a generic conversation with everybody?
What that means is that instead of sending an email campaign to, let’s say a half a million people, Can the machine recommend to you, which are those 320, 000 people that need to get a message and then for each one of them, can all the components of the message be personalized? So, don’t start by saying, I want to send an email campaign.
You start by saying, who are the consumers who are most likely to respond to that? For this objective of mine and for each one of those consumers, what is the form factor? What is the channel? What is the content? What is the creative at what time? And can the machine optimize all of those different dimensions and ensure that it’s all delivering autonomously in real time?
That is really hyper personalization at scale. So, you’re able to do this omni channel, you’re able to do this with personalized content, and you’re able to do it at the right time for each consumer, all under a single campaign, but it’s all omni channel and omni content. We’ve seen some significant benefits to enterprises on this.
The fact that you’re going in with more context automatically ensures that there are higher conversions. We’ve seen enterprises see between 10-12 percent to about 60 percent improvements in their conversions six times in certain cases versus what they, what kind of conversion throughputs they were having, even though they are, you’re sending the communication out to a smaller number of people, you basically start getting a significantly higher conversion percentage.
And, you know, business objectors, the attribution ability, because you’re breaking this down to which interaction led to conversion, the ability to attribute success and therefore to scale the most relevant interaction becomes much better and therefore comes back into the circle of control of the enterprise.
Rather than spray and pray and then hope that your marketing is working. Those are some of the things that we’ve seen.
Brian Thomas: Thank you so much Aditya. I appreciate that. I really do. You broke down quite a bit and you all know today we’re NAMDA, the spray and pray, you know, we get these emails that are just so generic to us, and it doesn’t make any sense, and it really leaves a bad taste in our mouth with receiving marketing letters.
So, I appreciate it. I really do. And I love how you went in through the hyper personalization in marketing and how you’re really making this a game changer. I appreciate that. And DJ, it was such a pleasure having you today. And I look forward to speaking with you real soon.
Aditya Bhamidipaty: Absolutely. Brian, it was a pleasure to connect with you as well and have a good rest of the day.
Brian Thomas: Bye for now.
Aditya Bhamidipaty Podcast Transcript. Listen to the audio on the guest’s podcast page.
